Feeds:
Posts
Comments

Archive for the ‘AI’ Category


Tweets and Retweets @ COVID-19 and AI: A Virtual Conference – Human-Centered Artificial Intelligence Institute, Stanford University, 4/1/2020, 9AM PST – 3:30PM PST @StanfordHAI  BY @pharma_BI and @AVIVA1950

COVID-19 and AI: A Virtual Conference – Human-Centered Artificial Intelligence Institute, Stanford University, 4/1/2020, 9AM PST – 3:30PM PST @StanfordHAI @pharma_BI @AVIVA1950

Real Time coverage: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/04/01/covid-19-and-ai-a-virtual-conference-human-centered-artificial-intelligence-stanford-university-4-1-2020-9am-pst-330pm-pst/

Aviva Lev-Ari
@AVIVA1950

Quote Tweet
Aviva Lev-Ari
@AVIVA1950
·
@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Fei-Fei Li AGE Fatality rate and infection rate of the aged Interaction between Acute Infection and Chronic Disease Safety of home – AI sensors at home Sensors data on secure systems clinically data recognized detection

Aviva Lev-Ari
@AVIVA1950

Quote Tweet
Aviva Lev-Ari
@AVIVA1950
·
@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Identifying COVID-19 Vaccine Candidates with ML Binbin Chen, MD and Ph.D. Student, Department of Genetics, Stanford University Immunogenic component of vaccine for COVID-19 spike protein bind epitome

Aviva Lev-Ari
@AVIVA1950

Quote Tweet
Aviva Lev-Ari
@AVIVA1950
·
@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Repurposing Existing Drugs to Fight COVID-19 Stefano Rensi #NLP Mine the literature for Proteins: Genomes genes proteins Biophysics #docking simulations for energy of 18 molecules as inhibitors  Selection of candidate

Aviva Lev-Ari
@AVIVA1950

Quote Tweet
Aviva Lev-Ari
@AVIVA1950
·
@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po #ML can be helpful in critical care navigate complexity by automating processes vaccine mutations in the spike protein binding ACE2

Aviva Lev-Ari
@AVIVA1950

Quote Tweet
Aviva Lev-Ari
@AVIVA1950
·
@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Mining article on sample size domain ares expert add to the challenges vs CS expertise alone

Aviva Lev-Ari
@AVIVA1950

Quote Tweet
Aviva Lev-Ari
@AVIVA1950
·
@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po #Virtual #informed #consent of #patient to accelerate ##clinical #trials

Aviva Lev-Ari
@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Xavier Amatriain Lack accessibility to health care systems HC Accessibility and Scalability AI based HC IT System PDA – Personalized Diagnostics Assessment – for self reporting AI Automations + Physicians home testing

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

Aviva Lev-Ari
@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Tina White, Ph.D. Candidate, Department of Mechanical Engineering, Stanford University China death toll >1000 China launched App to monitor quarantine early 1/2020 GPS based new App for contact tracing regulation on data

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

Aviva Lev-Ari
@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po John Brownstein Late December 2019 collecting dat a HealthMap – public domain Baidu – has movement information connected with cases Temperature Data published Buoy data base customized to collect MA data on Temperature

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

Aviva Lev-Ari
@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Jason Wang commend center in December 2019 All flight entering the country – Level 3 alert country: China Huhan, Hubei Quarantine all arriving from Level 3 alert country National STOKE PILES Activated x5 mask production

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

Aviva Lev-Ari
@AVIVA1950

#AI

pharmaceuticalintelligence.com/coronavirus-po Jason Wang Since 2003 Taiwan is preparing for a Pangemic JAMA paper on the topic is beebn reported  Location of patient Taiwan National Epidemic Center 100 persons 24×7 in the Command Center Taiwan activated

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

Aviva Lev-Ari
@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Michele Barry,TRACE together – Bluetooth tool on distance among people CHINA – contact racing surveillence scanning temp strict social distancing Hong Kong – tracing bracelets for quarantine Street locations of infected

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

Aviva Lev-Ari
@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Michele Barry 5Million people travel out of Huhan Singapore – Free testing 1st country Temp testing stay at home, text phone from Authorities, show picture they are in quarantine for 5 days if negative  TRACE together

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

Aviva Lev-Ari
@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Seema Yasmin March 7, 2020 Italy news quarantine of 16 million lockdown large movement of people moving out of lockeddown areas, this movement based on information lead to spread of the viral spread

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

Aviva Lev-Ari
@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Nigam Shah,  Operational Planning – Utilization – Resource planning Clinical – who to test Research Questions – ACE2 receptors

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

Aviva Lev-Ari
@AVIVA1950

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

Aviva Lev-Ari
@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com
3

Aviva Lev-Ari
@AVIVA1950

covering in real time Stanford HAI – COVID-19 and AI: A Virtual Conference youtu.be/z4105Exe23Q via

Stanford HAI – COVID-19 and AI: A Virtual Conference
COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…
youtube.com

Aviva Lev-Ari
@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com
3

Aviva Lev-Ari
@AVIVA1950

covering in real time Stanford HAI – COVID-19 and AI: A Virtual Conference youtu.be/z4105Exe23Q via

Stanford HAI – COVID-19 and AI: A Virtual Conference
COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…
youtube.com

Aviva Lev-Ari
@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com
3

Aviva Lev-Ari
@AVIVA1950

covering in real time Stanford HAI – COVID-19 and AI: A Virtual Conference youtu.be/z4105Exe23Q via

Stanford HAI – COVID-19 and AI: A Virtual Conference
COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…
youtube.com
1

Aviva Lev-Ari
@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Stanford Institute for Human-Centered Artificial Intelligence (HAI) Conference on COVID-19 and AI: A Virtual Conference on April 1, 2020 beginning at 9:00am (PDT). event covered in real time

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

 

Stanford HAI
@StanfordHAI

Vaccines are one of the most powerful tools to curb a pandemic and prevent its recurrence,

says. He discusses how AI tools built upon immunology knowledge and data can increase the chances of finding an effective vaccine. stanford.io/3aBidgh

Image

1
8
21

 

Aviva Lev-Ari
@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com
2

Aviva Lev-Ari
@AVIVA1950

covering in real time Stanford HAI – COVID-19 and AI: A Virtual Conference youtu.be/z4105Exe23Q via

Stanford HAI – COVID-19 and AI: A Virtual Conference
COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…
youtube.com
1

Aviva Lev-Ari
@AVIVA1950

Aviva Lev-Ari
@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com
2

Aviva Lev-Ari
@AVIVA1950

covering in real time Stanford HAI – COVID-19 and AI: A Virtual Conference youtu.be/z4105Exe23Q via

Stanford HAI – COVID-19 and AI: A Virtual Conference
COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…
youtube.com
1

Aviva Lev-Ari
@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Stanford Institute for Human-Centered Artificial Intelligence (HAI) Conference on COVID-19 and AI: A Virtual Conference on April 1, 2020 beginning at 9:00am (PDT). event covered in real time

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com

Read Full Post »


The Future of Synthetic Biology

Reporter: Irina Robu, PhD

With an estimated global evaluation of around $14 billion US, synthetic biology is a rapidly accelerating market. Nonetheless while the growth of the market has been remarkable, the ttrue impact has not yet been seen. The era of AI will quickly increase the pace of discovery, and produce materials not seen in nature, through extrapolation and generative design. The extraordinary is now possible: producing spider silk without spiders, egg proteins without chickens and fragrances without flowers.

Synthetic biology companies are associating with fashion designers as well as forming ‘organism foundries. Rapidly, AI will utilize its learning of the natural world to make guided inferences which produce entirely new materials. From a technology perspective, we’re experiencing an explosion of capability that will be invasive in the next 3-5 years. Language models have come a long way, to the point where full models are being kept private so as not to endanger the public.

Already today, the average person has the ability to start their own commercial space venture for less than the cost of a juice franchise. PwC Australia’s Charmaine Green believes secret trends can hide among obvious ones. She outlines three trends leading to her hypothesis that Australia is well placed to become the global creative hub for video game development.

Economies like Australia are situated to capitalize on this trend, and video game development can become a permanent and substantial part of the economy. In Australia, Green argues, we have all the basic elements needed: high ingenuity, creative risk taking, and the freedom and flexibility that comes with the country’s small-to-mid studios.

SOURCE

Tech trends that will change the world by 2025

Read Full Post »


Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine

Reporter: Aviva Lev-Ari, PhD, RN

 

This report is based on an article in Nature Medicine | VOL 25 | December 2019 | 1800–1809 | http://www.nature.com/naturemedicine

Looking forward 25 years: the future of medicine.

Nat Med 25, 1804–1807 (2019) doi:10.1038/s41591-019-0693-y

 

Aviv Regev, PhD

Core member and chair of the faculty, Broad Institute of MIT and Harvard; director, Klarman Cell Observatory, Broad Institute of MIT and Harvard; professor of biology, MIT; investigator, Howard Hughes Medical Institute; founding co-chair, Human Cell Atlas.

  • millions of genome variants, tens of thousands of disease-associated genes, thousands of cell types and an almost unimaginable number of ways they can combine, we had to approximate a best starting point—choose one target, guess the cell, simplify the experiment.
  • In 2020, advances in polygenic risk scores, in understanding the cell and modules of action of genes through genome-wide association studies (GWAS), and in predicting the impact of combinations of interventions.
  • we need algorithms to make better computational predictions of experiments we have never performed in the lab or in clinical trials.
  • Human Cell Atlas and the International Common Disease Alliance—and in new experimental platforms: data platforms and algorithms. But we also need a broader ecosystem of partnerships in medicine that engages interaction between clinical experts and mathematicians, computer scientists and engineers

Feng Zhang, PhD

investigator, Howard Hughes Medical Institute; core member, Broad Institute of MIT and Harvard; James and Patricia Poitras Professor of Neuroscience, McGovern Institute for Brain Research, MIT.

  • fundamental shift in medicine away from treating symptoms of disease and toward treating disease at its genetic roots.
  • Gene therapy with clinical feasibility, improved delivery methods and the development of robust molecular technologies for gene editing in human cells, affordable genome sequencing has accelerated our ability to identify the genetic causes of disease.
  • 1,000 clinical trials testing gene therapies are ongoing, and the pace of clinical development is likely to accelerate.
  • refine molecular technologies for gene editing, to push our understanding of gene function in health and disease forward, and to engage with all members of society

Elizabeth Jaffee, PhD

Dana and Albert “Cubby” Broccoli Professor of Oncology, Johns Hopkins School of Medicine; deputy director, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins.

  • a single blood test could inform individuals of the diseases they are at risk of (diabetes, cancer, heart disease, etc.) and that safe interventions will be available.
  • developing cancer vaccines. Vaccines targeting the causative agents of cervical and hepatocellular cancers have already proven to be effective. With these technologies and the wealth of data that will become available as precision medicine becomes more routine, new discoveries identifying the earliest genetic and inflammatory changes occurring within a cell as it transitions into a pre-cancer can be expected. With these discoveries, the opportunities to develop vaccine approaches preventing cancers development will grow.

Jeremy Farrar, OBE FRCP FRS FMedSci

Director, Wellcome Trust.

  • shape how the culture of research will develop over the next 25 years, a culture that cares more about what is achieved than how it is achieved.
  • building a creative, inclusive and open research culture will unleash greater discoveries with greater impact.

John Nkengasong, PhD

Director, Africa Centres for Disease Control and Prevention.

  • To meet its health challenges by 2050, the continent will have to be innovative in order to leapfrog toward solutions in public health.
  • Precision medicine will need to take center stage in a new public health order— whereby a more precise and targeted approach to screening, diagnosis, treatment and, potentially, cure is based on each patient’s unique genetic and biologic make-up.

Eric Topol, MD

Executive vice-president, Scripps Research Institute; founder and director, Scripps Research Translational Institute.

  • In 2045, a planetary health infrastructure based on deep, longitudinal, multimodal human data, ideally collected from and accessible to as many as possible of the 9+ billion people projected to then inhabit the Earth.
  • enhanced capabilities to perform functions that are not feasible now.
  • AI machines’ ability to ingest and process biomedical text at scale—such as the corpus of the up-to-date medical literature—will be used routinely by physicians and patients.
  • the concept of a learning health system will be redefined by AI.

Linda Partridge, PhD

Professor, Max Planck Institute for Biology of Ageing.

  • Geroprotective drugs, which target the underlying molecular mechanisms of ageing, are coming over the scientific and clinical horizons, and may help to prevent the most intractable age-related disease, dementia.

Trevor Mundel, MD

President of Global Health, Bill & Melinda Gates Foundation.

  • finding new ways to share clinical data that are as open as possible and as closed as necessary.
  • moving beyond drug donations toward a new era of corporate social responsibility that encourages biotechnology and pharmaceutical companies to offer their best minds and their most promising platforms.
  • working with governments and multilateral organizations much earlier in the product life cycle to finance the introduction of new interventions and to ensure the sustainable development of the health systems that will deliver them.
  • deliver on the promise of global health equity.

Josep Tabernero, MD, PhD

Vall d’Hebron Institute of Oncology (VHIO); president, European Society for Medical Oncology (2018–2019).

  • genomic-driven analysis will continue to broaden the impact of personalized medicine in healthcare globally.
  • Precision medicine will continue to deliver its new paradigm in cancer care and reach more patients.
  • Immunotherapy will deliver on its promise to dismantle cancer’s armory across tumor types.
  • AI will help guide the development of individually matched
  • genetic patient screenings
  • the promise of liquid biopsy policing of disease?

Pardis Sabeti, PhD

Professor, Harvard University & Harvard T.H. Chan School of Public Health and Broad Institute of MIT and Harvard; investigator, Howard Hughes Medical Institute.

  • the development and integration of tools into an early-warning system embedded into healthcare systems around the world could revolutionize infectious disease detection and response.
  • But this will only happen with a commitment from the global community.

Els Toreele, PhD

Executive director, Médecins Sans Frontières Access Campaign

  • we need a paradigm shift such that medicines are no longer lucrative market commodities but are global public health goods—available to all those who need them.
  • This will require members of the scientific community to go beyond their role as researchers and actively engage in R&D policy reform mandating health research in the public interest and ensuring that the results of their work benefit many more people.
  • The global research community can lead the way toward public-interest driven health innovation, by undertaking collaborative open science and piloting not-for-profit R&D strategies that positively impact people’s lives globally.

Read Full Post »


Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

One of the most contagious diseases known to humankind, measles killed an average of 2.6 million people each year before a vaccine was developed, according to the World Health Organization. Widespread vaccination has slashed the death toll. However, lack of access to vaccination and refusal to get vaccinated means measles still infects more than 7 million people and kills more than 100,000 each year worldwide as reported by WHO. The cases are on the rise, tripling in early 2019 and some experience well-known long-term consequences, including brain damage and vision and hearing loss. Previous epidemiological research into immune amnesia suggests that death rates attributed to measles could be even higher, accounting for as much as 50 percent of all childhood mortality.

 

Over the last decade, evidence has mounted that the measles vaccine protects in two ways. It prevents the well-known acute illness with spots and fever and also appears to protect from other infections over the long term by giving general boost to the immune system. The measles virus can impair the body’s immune memory, causing so-called immune amnesia. By protecting against measles infection, the vaccine prevents the body from losing or “forgetting” its immune memory and preserves its resistance to other infections. Researchers showed that the measles virus wipes out 11% to 73% of the different antibodies that protect against viral and bacterial strains a person was previously immune to like from influenza to herpes virus to bacteria that cause pneumonia and skin infections.

 

This study at Harvard Medical School and their collaborators is the first to measure the immune damage caused by the virus and underscores the value of preventing measles infection through vaccination. The discovery that measles depletes people’s antibody repertoires, partially obliterating immune memory to most previously encountered pathogens, supports the immune amnesia hypothesis. It was found that those who survive measles gradually regain their previous immunity to other viruses and bacteria as they get re-exposed to them. But because this process may take months to years, people remain vulnerable in the meantime to serious complications of those infections and thus booster shots of routine vaccines may be required.

 

VirScan detects antiviral and antibacterial antibodies in the blood that result from current or past encounters with viruses and bacteria, giving an overall snapshot of the immune system. Researchers gathered blood samples from unvaccinated children during a 2013 measles outbreak in the Netherlands and used VirScan to measure antibodies before and two months after infection in 77 children who’d contracted the disease. The researchers also compared the measurements to those of 115 uninfected children and adults. Researchers found a striking drop in antibodies from other pathogens in the measles-infected children that clearly suggested a direct effect on the immune system resembling measles-induced immune amnesia.

 

Further tests revealed that severe measles infection reduced people’s overall immunity more than mild infection. This could be particularly problematic for certain categories of children and adults, the researchers said. The present study observed the effects in previously healthy children only. But, measles is known to hit malnourished children much harder, the degree of immune amnesia and its effects could be even more severe in less healthy populations. Inoculation with the MMR (measles, mumps, rubella) vaccine did not impair children’s overall immunity. The results align with decades of research. Ensuring widespread vaccination against measles would not only help prevent the expected 120,000 deaths that will be directly attributed to measles this year alone, but could also avert potentially hundreds of thousands of additional deaths attributable to the lasting damage to the immune system.

 

References:

 

https://hms.harvard.edu/news/inside-immune-amnesia?utm_source=Silverpop

 

https://science.sciencemag.org/content/366/6465/599

 

www.who.int/immunization/newsroom/measles-data-2019/en/

 

https://www.ncbi.nlm.nih.gov/pubmed/20636817

 

https://www.ncbi.nlm.nih.gov/pubmed/27157064

 

https://www.ncbi.nlm.nih.gov/pubmed/30797735

 

Read Full Post »


Cardiac MRI Imaging Breakthrough: The First AI-assisted Cardiac MRI Scan Solution, HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package

Reporter: Aviva Lev-Ari, PhD, RN

 

HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package, the First AI-assisted Cardiac MRI Scan Solution

The future of imaging is here—and FDA cleared.

LOS ALTOS, Calif.–(BUSINESS WIRE)–HeartVista, a pioneer in AI-assisted MRI solutions, today announced that it received 510(k) clearance from the U.S. Food and Drug Administration to deliver its AI-assisted One Click™ MRI acquisition software for cardiac exams. Despite the many advantages of cardiac MRI, or cardiac magnetic resonance (CMR), its use has been largely limited due to a lack of trained technologists, high costs, longer scan time, and complexity of use. With HeartVista’s solution, cardiac MRI is now simple, time-efficient, affordable, and highly consistent.

“HeartVista’s Cardiac Package is a vital tool to enhance the consistency and productivity of cardiac magnetic resonance studies, across all levels of CMR expertise,” said Dr. Raymond Kwong, MPH, Director of Cardiac Magnetic Resonance Imaging at Brigham and Women’s Hospital and Associate Professor of Medicine at Harvard Medical School.

A recent multi-center, outcome-based study (MR-INFORM), published in the New England Journal of Medicine, demonstrated that non-invasive myocardial perfusion cardiovascular MRI was as good as invasive FFR, the previous gold standard method, to guide treatment for patients with stable chest pain, while leading to 20% fewer catheterizations.

“This recent NEJM study further reinforces the clinical literature that cardiac MRI is the gold standard for cardiac diagnosis, even when compared against invasive alternatives,” said Itamar Kandel, CEO of HeartVista. “Our One Click™ solution makes these kinds of cardiac MRI exams practical for widespread adoption. Patients across the country now have access to the only AI-guided cardiac MRI exam, which will deliver continuous imaging via an automated process, minimize errors, and simplify scan operation. Our AI solution generates definitive, accurate and actionable real-time data for cardiologists. We believe it will elevate the standard of care for cardiac imaging, enhance patient experience and access, and improve patient outcomes.”

HeartVista’s FDA-cleared Cardiac Package uses AI-assisted software to prescribe the standard cardiac views with just one click, and in as few as 10 seconds, while the patient breathes freely. A unique artifact detection neural network is incorporated in HeartVista’s protocol to identify when the image quality is below the acceptable threshold, prompting the operator to reacquire the questioned images if desired. Inversion time is optimized with further AI assistance prior to the myocardial delayed-enhancement acquisition. A 4D flow measurement application uses a non-Cartesian, volumetric parallel imaging acquisition to generate high quality images in a fraction of the time. The Cardiac Package also provides preliminary measures of left ventricular function, including ejection fraction, left ventricular volumes, and mass.

HeartVista is presenting its new One Click™ Cardiac Package features at the Radiological Society of North America (RSNA) annual meeting in Chicago, on Dec. 4, 2019, at 2 p.m., in the AI Showcase Theater. HeartVista will also be at Booth #11137 for the duration of the conference, from Dec. 1 through Dec. 5.

About HeartVista

HeartVista believes in leveraging artificial intelligence with the goal of improving access to MRI and improved patient care. The company’s One Click™ software platform enables real-time MRI for a variety of clinical and research applications. Its AI-driven, one-click cardiac localization method received first place honors at the International Society for Magnetic Resonance in Medicine’s Machine Learning Workshop in 2018. The company’s innovative technology originated at the Stanford Magnetic Resonance Systems Research Laboratory. HeartVista is funded by Khosla Ventures, and the National Institute of Health’s Small Business Innovation Research program.

For more information, visit www.heartvista.ai

SOURCE

Reply-To: Kimberly Ha <kimberly.ha@kkhadvisors.com>

Date: Tuesday, October 29, 2019 at 11:01 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: HeartVista Receives FDA Clearance for First AI-assisted Cardiac MRI Solution

Read Full Post »


Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

Curator: Stephen J. Williams, PhD

 

scTrio-seq identifies colon cancer lineages

Single-cell multiomics sequencing and analyses of human colorectal cancer. Shuhui Bian et al. Science  30 Nov 2018:Vol. 362, Issue 6418, pp. 1060-1063

To better design treatments for cancer, it is important to understand the heterogeneity in tumors and how this contributes to metastasis. To examine this process, Bian et al. used a single-cell triple omics sequencing (scTrio-seq) technique to examine the mutations, transcriptome, and methylome within colorectal cancer tumors and metastases from 10 individual patients. The analysis provided insights into tumor evolution, linked DNA methylation to genetic lineages, and showed that DNA methylation levels are consistent within lineages but can differ substantially among clones.

Science, this issue p. 1060

Abstract

Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multiomics sequencing together with multiregional sampling of the primary tumor and lymphatic and distant metastases, we developed insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 patients whose DNA we sequenced. The cancer cells’ DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue and those of repetitive element long interspersed nuclear element 1. Our work demonstrates the feasibility of reconstructing genetic lineages and tracing their epigenomic and transcriptomic dynamics with single-cell multiomics sequencing.

Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples from the TCGA Project and patient CRC01’s cancer cells are shown.

” data-icon-position=”” data-hide-link-title=”0″>

 

Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples

 

 

Read Full Post »


Showcase: How Deep Learning could help radiologists spend their time more efficiently

Reporter and Curator: Dror Nir, PhD

 

The debate on the function AI could or should realize in modern radiology is buoyant presenting wide spectrum of positive expectations and also fears.

The article: A Deep Learning Model to Triage Screening Mammograms: A Simulation Study that was published this month shows the best, and very much feasible, utility for AI in radiology at the present time. It would be of great benefit for radiologists and patients if such applications will be incorporated (with all safety precautions taken) into routine practice as soon as possible.

In a simulation study, a deep learning model to triage mammograms as cancer free improves workflow efficiency and significantly improves specificity while maintaining a noninferior sensitivity.

Background

Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specificity or efficiency.

Purpose

To develop a DL model to triage a portion of mammograms as cancer free, improving performance and workflow efficiency.

Materials and Methods

In this retrospective study, 223 109 consecutive screening mammograms performed in 66 661 women from January 2009 to December 2016 were collected with cancer outcomes obtained through linkage to a regional tumor registry. This cohort was split by patient into 212 272, 25 999, and 26 540 mammograms from 56 831, 7021, and 7176 patients for training, validation, and testing, respectively. A DL model was developed to triage mammograms as cancer free and evaluated on the test set. A DL-triage workflow was simulated in which radiologists skipped mammograms triaged as cancer free (interpreting them as negative for cancer) and read mammograms not triaged as cancer free by using the original interpreting radiologists’ assessments. Sensitivities, specificities, and percentage of mammograms read were calculated, with and without the DL-triage–simulated workflow. Statistics were computed across 5000 bootstrap samples to assess confidence intervals (CIs). Specificities were compared by using a two-tailed t test (P < .05) and sensitivities were compared by using a one-sided t test with a noninferiority margin of 5% (P < .05).

Results

The test set included 7176 women (mean age, 57.8 years ± 10.9 [standard deviation]). When reading all mammograms, radiologists obtained a sensitivity and specificity of 90.6% (173 of 191; 95% CI: 86.6%, 94.7%) and 93.5% (24 625 of 26 349; 95% CI: 93.3%, 93.9%). In the DL-simulated workflow, the radiologists obtained a sensitivity and specificity of 90.1% (172 of 191; 95% CI: 86.0%, 94.3%) and 94.2% (24 814 of 26 349; 95% CI: 94.0%, 94.6%) while reading 80.7% (21 420 of 26 540) of the mammograms. The simulated workflow improved specificity (P = .002) and obtained a noninferior sensitivity with a margin of 5% (P < .001).

Conclusion

This deep learning model has the potential to reduce radiologist workload and significantly improve specificity without harming sensitivity.

Read Full Post »


Forbes Opinion: 13 Industries Soon To Be Revolutionized By Artificial Intelligence

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 11/18/2019

Amazon Saw 15-Fold Jump In Forecast Accuracy With Deep Learning And Other AI Stats

https://www.forbes.com/sites/gilpress/2019/11/14/amazon-saw-15-fold-jump-in-forecast-accuracy-with-deep-learning-and-other-ai-stats/#2a03ca8a748f

 

China Leads in Highly Cited AI Papers

by GilPress

China AI Development Report 2018 (p. 20f.): Using the Web of Science database, this report puts China in the lead ahead of the US and any European country, both for the decade between 2007 and 2017 and in terms of annual output in 2017. Aggregating all European countries out of the top 10 from 2007 to 2017 (the UK, Germany, France, Italy, Spain), they almost catch up to the US and China, reaching 2,096 highly cited papers, compared to 2,241 and 2,349, respectively. The same applies to “hot papers.” Unfortunately, I do not know how they operationalized either “highly cited” or “hot.” Eyeballing combined European output in 2017 alone; China is still in the lead with an increasingly large margin, ahead of the US and Europe, which seem to be roughly tied.

Source: Stefan Torges

2020 Predictions for the Internet of Things (IoT)

by GilPress

IDC 2020 predictions for the Internet of Things (IoT):

In 2020, 90% of organizations will have determined key performance indicators to measure the success of their IoT projects.

By 2021, 75% of organizations embarking on an IoT project will work with a systems integrator to strategize, plan, deploy, and/or manage the initiative.

By 2022, 70% of new enterprise IoT applications built on IoT platforms will leverage container deployment.

By 2023, 70% of enterprises will run varying levels of data processing at the IoT edge. In tandem, organizations will spend over $16 billion on IoT edge infrastructure in that time.

By 2023, 20% of cybersecurity incidents will stem from Smart City IoT device deployments, forcing double-digit increases in cybersecurity software and staff training budgets.

To lessen critical equipment failures, by 2024, 40% of manufacturers will use field asset IoT data to intelligently diagnose issues and resolve autonomously, improving unplanned downtime by 25%.

By 2023, 70% of IoT deployment will include AI solutions for autonomous or edge decision making, supporting organizations’ operational and strategic agendas.

By 2025, there will be 79ZB of data created by billions of IoT devices, causing organizations to reevaluate their data governance, retention, and usage policies.

By 2025, 60% of manufacturers will use IoT platforms with digital innovation platforms to operate networks of asset, product, and process digital twins for a 25% reduction in cost of quality.

By 2023, enterprises will struggle to manage all the different access types used to connect their IoT endpoints, with 75% adopting more than one connectivity type.

GilPress | November 12, 2019 at 9:15 am | Tags: 2020 predictions, IDC | Categories: Data growth, Internet of Things | URL: https://wp.me/p1AMkl-2J2

13 Industries Soon To Be Revolutionized By Artificial Intelligence

7:00 am

30,112 views|Jan 16, 2019,

Post written by

Expert Panel, Forbes Technology Council

Successful CIOs, CTOs & executives from Forbes Technology Council offer firsthand insights on tech & business.

Artificial intelligence (AI) and machine learning (ML) have a rapidly growing presence in today’s world, with applications ranging from heavy industry to education. From streamlining operations to informing better decision making, it has become clear that this technology has the potential to truly revolutionize how the everyday world works.

While AI and ML can be applied to nearly every sector, once the technology advances enough, there are many fields that are either reaping the benefits of AI right now or that soon will be. According to a panel of Forbes Technology Council members, here are 13 industries that will soon be revolutionized by AI.

1. Cybersecurity

The enterprise attack surface is massive. There are countless permutations and combinations in which the adversary can get in. It is exceptionally hard for organizations to analyze and improve their security posture. With its power to bring complex reasoning and self-learning in an automated fashion at massive scale, AI will be a game-changer in how we improve our cyber-resilience. – Gaurav Banga, Balbix

2. DevOps And Cloud Hosting

AI is starting to make its mark in DevOps. Currently, Amazon has rolled out machine learning for their Elastic Compute Cloud (EC2) instances, which applies to predictive instance autoscaling. Other cloud vendors are following suit with similar technology. Within the next 10 years, I see the same being applied to bigger things like code deployments and infrastructure provisioning. – Rick Conlee, Meticulosity

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

3. Manufacturing

Artificial intelligence in the world of manufacturing has limitless potential. From preventative maintenance to the automation of human tasks, AI will enable more efficient work that’s less prone to error and has higher quality. Initiatives from tech giants like Microsoft (AI for Accessibility) and smaller leading companies like AtBot will revolutionize AI for all information workers. – Dan Sonneborn, Aerie Consulting

4. Healthcare

Healthcare is only starting on its AI journey. Computer vision against X-rays shows promises to help pinpoint diseases; natural language processing (NLP) shows promises in drug safety; ML shows promises to find patterns within a population. Once we reach a point of true information interoperability, supporting the secure exchange of health data, all these promises will join forces to become breakthroughs for the patients. – Florian Quarré, Ciox Health

5. Construction

The construction industry has long been underserved by the technology and software sector. Many new startups like ours are using AI in a big way to slingshot the construction industry into tomorrow. Bringing AI and machine learning into this industry will make the construction process faster, safer and more cost effective by reducing human error and better utilizing big data.  – Karuna Ammireddy, Pype

6. Senior Care

With the aging Baby Boomer generation, we need solutions that provide continued efficiency for seniors to make them feel more confident about living alone or receiving support from their caregivers. While AI may not be able to understand the cultural, physical and emotional needs of people, it can provide updates to many outdated resources. – Abdullah Snobar, DMZ at Ryerson University

7. Retail

The retail industry will be one that is most impacted by AI. Its global spending is expected to grow to $7.3 billion per year by 2022. Retailers will use augmented and virtual reality functionality in advertising. Immersive product catalog visualization will grow dramatically, and shoppers will experience products before buying. It’s predicted that by 2020, chatbots will power 85% of all customer service interactions. – Nacho De Marco, BairesDev

8. Business Intelligence

Enterprises are overwhelmed by the volume of data generated by their customers, tools and processes. They are finding traditional business intelligence tools are failing. Spreadsheets and dashboards will be replaced by AI-powered tools that explore data, find insights and make recommendations automatically. These tools will change the way companies use data and make decisions. – Sean Byrnes, Outlier AI

9. City Planning

Infrastructure planning and development will get a big boost from AI. So much data can be processed and organized to help understand urban areas and how they are changing. AI data can also provide a different way of looking at growth and development, utility use, safety, and more. – Chalmers Brown, Due

10. Mental Health Diagnosis And Treatment

We are starting to see an increase in mental health issues among young people. Whether it is device addiction or withdrawal from the physical world, some are starting to isolate themselves online. This can ultimately lead to a breakdown of social cohesion. I see potential in using AI to identify people at risk and recommend therapy before they fall into a hole of depression and hopelessness. – Chris Kirby, Retired

11. Education

The basic concepts of education have not changed much across generations, and it is quite obvious that change is needed. The most pressing question is what that change should be and how to achieve it. Harnessing AI to create a personalized, dynamic and effective learning path for any subject can prove to be an amazing enabler for such a revolution. – Ofer Garnett, YouAPPi Inc.

12. Fashion

Using AI to learn about buying patterns of users across the world and predict fashion trends would be a great implementation. Having a great recommendation engine backed by AI would help users tremendously. – Amit Ojha, Diamond Foundry

13. Supply Chain Management

AI can account for more factors and complicated nonlinear and correlated dependencies of data much better than a human can do. AI can predict the future without human bias, but with a proper risk assessment, and find optimal decisions even under asymmetric cost profile. This leads to improvements in every decision. – Michael Feindt, Blue Yonder

Forbes Technology Council is an invitation-only, fee-based organization comprised of leading CIOs, CTOs and technology executives. Find out if you qualify at forbestechcoRead More

SOURCE

https://www.forbes.com/sites/forbestechcouncil/2019/01/16/13-industries-soon-to-be-revolutionized-by-artificial-intelligence/amp/?__twitter_impression=true

Read Full Post »


Artificial Intelligence and Cardiovascular Disease

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Cardiology is a vast field that focuses on a large number of diseases specifically dealing with the heart, the circulatory system, and its functions. As such, similar symptomatologies and diagnostic features may be present in an individual, making it difficult for a doctor to easily isolate the actual heart-related problem. Consequently, the use of artificial intelligence aims to relieve doctors from this hurdle and extend better quality to patients. Results of screening tests such as echocardiograms, MRIs, or CT scans have long been proposed to be analyzed using more advanced techniques in the field of technology. As such, while artificial intelligence is not yet widely-used in clinical practice, it is seen as the future of healthcare.

 

The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health care. One of the ongoing trends in cardiology at present is the proposed utilization of artificial intelligence (AI) in augmenting and extending the effectiveness of the cardiologist. This is because AI or machine-learning would allow for an accurate measure of patient functioning and diagnosis from the beginning up to the end of the therapeutic process. In particular, the use of artificial intelligence in cardiology aims to focus on research and development, clinical practice, and population health. Created to be an all-in-one mechanism in cardiac healthcare, AI technologies incorporate complex algorithms in determining relevant steps needed for a successful diagnosis and treatment. The role of artificial intelligence specifically extends to the identification of novel drug therapies, disease stratification or statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, and extension of physician effectivity and efficiency.

 

Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans. Computers can be ‘trained’ to make these predictions. This is done by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. This information is heart scans, genetic and other test results, and how long each patient survived. These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.

 

In current medical practice, doctors will use risk scores to make treatment decisions for their cardiac patients. These are based on a series of variables like weight, age and lifestyle. However, they do not always have the desired levels of accuracy. A particular example of the use of artificial examination in cardiology is the experimental study on heart disease patients, published in 2017. The researchers utilized cardiac MRI-based algorithms coupled with a 3D systolic cardiac motion pattern to accurately predict the health outcomes of patients with pulmonary hypertension. The experiment proved to be successful, with the technology being able to pick-up 30,000 points within the heart activity of 250 patients. With the success of the aforementioned study, as well as the promise of other researches on artificial intelligence, cardiology is seemingly moving towards a more technological practice.

 

One study was conducted in Finland where researchers enrolled 950 patients complaining of chest pain, who underwent the centre’s usual scanning protocol to check for coronary artery disease. Their outcomes were tracked for six years following their initial scans, over the course of which 24 of the patients had heart attacks and 49 died from all causes. The patients first underwent a coronary computed tomography angiography (CCTA) scan, which yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing and calcification. Patients whose scans were suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were also obtained from medical records including sex, age, smoking status and diabetes. These 85 variables were then entered into an artificial intelligence (AI) programme called LogitBoost. The AI repeatedly analysed the imaging variables, and was able to learn how the imaging data interacted and identify the patterns which preceded death and heart attack with over 90% accuracy. The predictive performance using the ten clinical variables alone was modest, with an accuracy of 90%. When PET scan data was added, accuracy increased to 92.5%. The predictive performance increased significantly when CCTA scan data was added to clinical and PET data, with accuracy of 95.4%.

 

Another study findings showed that applying artificial intelligence (AI) to the electrocardiogram (ECG) enables early detection of left ventricular dysfunction and can identify individuals at increased risk for its development in the future. Asymptomatic left ventricular dysfunction (ALVD) is characterised by the presence of a weak heart pump with a risk of overt heart failure. It is present in three to six percent of the general population and is associated with reduced quality of life and longevity. However, it is treatable when found. Currently, there is no inexpensive, noninvasive, painless screening tool for ALVD available for diagnostic use. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3 percent, 85.7 percent, and 85.7 percent, respectively. Furthermore, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction compared with those with a negative screen.

 

In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient’s recovery, improve medical quality in the near future.

 

The primary issue about using artificial intelligence in cardiology, or in any field of medicine for that matter, is the ethical issues that it brings about. Physicians and healthcare professionals prior to their practice swear to the Hippocratic Oath—a promise to do their best for the welfare and betterment of their patients. Many physicians have argued that the use of artificial intelligence in medicine breaks the Hippocratic Oath since patients are technically left under the care of machines than of doctors. Furthermore, as machines may also malfunction, the safety of patients is also on the line at all times. As such, while medical practitioners see the promise of artificial technology, they are also heavily constricted about its use, safety, and appropriateness in medical practice.

 

Issues and challenges faced by technological innovations in cardiology are overpowered by current researches aiming to make artificial intelligence easily accessible and available for all. With that in mind, various projects are currently under study. For example, the use of wearable AI technology aims to develop a mechanism by which patients and doctors could easily access and monitor cardiac activity remotely. An ideal instrument for monitoring, wearable AI technology ensures real-time updates, monitoring, and evaluation. Another direction of cardiology in AI technology is the use of technology to record and validate empirical data to further analyze symptomatology, biomarkers, and treatment effectiveness. With AI technology, researchers in cardiology are aiming to simplify and expand the scope of knowledge on the field for better patient care and treatment outcomes.

 

References:

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.bhf.org.uk/informationsupport/heart-matters-magazine/research/artificial-intelligence

 

https://www.medicaldevice-network.com/news/heart-attack-artificial-intelligence/

 

https://www.nature.com/articles/s41569-019-0158-5

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711980/

 

www.j-pcs.org/article.asp

http://www.onlinejacc.org/content/71/23/2668

http://www.scielo.br/pdf/ijcs/v30n3/2359-4802-ijcs-30-03-0187.pdf

 

https://www.escardio.org/The-ESC/Press-Office/Press-releases/How-artificial-intelligence-is-tackling-heart-disease-Find-out-at-ICNC-2019

 

https://clinicaltrials.gov/ct2/show/NCT03877614

 

https://www.europeanpharmaceuticalreview.com/news/82870/artificial-intelligence-ai-heart-disease/

 

https://www.frontiersin.org/research-topics/10067/current-and-future-role-of-artificial-intelligence-in-cardiac-imaging

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.sciencedaily.com/releases/2019/05/190513104505.htm

 

Read Full Post »


Retrospect on HistoScanning; an AI routinely used in diagnostic imaging for over a decade

Author and Curator: Dror Nir, PhD

This blog-post is a retrospect on over a decade of doing with HistoScanning; an AI medical-device for imaging-based tissue characterization.

Imaging-based tissue characterization by AI is offering a change in imaging paradigm; enhancing the visual information received when using diagnostic-imaging beyond that which the eye alone can see and at the same time simplifying and increasing the cost-effectiveness of patients clinical pathway.

In the case of HistoScanning, imaging is a combination of 3D-scanning by ultrasound with a real-time application of AI. The HistoScanning AI application comprises fast “patterns recognition” algorithms trained on ultrasound-scans and matched histopathology of cancer patients. It classifies millimetric tissue-volumes by identifying differences in the scattered ultrasound characterizing different mechanical and morphological properties of the different pathologies. A user-friendly interface displays the analysis results on the live ultrasound video image.

Users of AI in diagnostic-imaging of cancer patients expect it to improve their ability to:

  • Detect clinically significant cancer lesions with high sensitivity and specificity
  • Accurately position lesions within an organ
  • Accurately estimate the lesion volume
  • AND; help determine the pre-clinical level of lesion aggressiveness

The last being achieved through real-time guidance of needle biopsy towards the most suspicious locations.

Unlike most technologies that get obsolete as time passes, AI gets better. Availability of more processing power, better storage technologies, and faster memories translate to an ever-growing capacity of machines to learn. Moreover, the human-perception of AI is transforming fast from disbelief at the time HistoScanning was first launched, into total embracement.

During the last decade, 192 systems were put to use at the hands of urologists, radiologists, and gynecologists. Over 200 peer-reviewed, scientific-posters and white-papers were written by HistoScanning users sharing experiences and thoughts. Most of these papers are about HistoScanning for Prostate (PHS) which was launched as a medical-device in 2007. The real-time guided prostate-biopsy application was added to it in late 2013. I have mentioned several  of these papers in blog-posts published in this open-access website, e.g. :

Today’s fundamental challenge in Prostate cancer screening (September 2, 2012)

The unfortunate ending of the Tower of Babel construction project and its effect on modern imaging-based cancer patients’ management (October 22, 2012)

On the road to improve prostate biopsy (February 15, 2013)

Ultrasound-based Screening for Ovarian Cancer (April 28, 2013)

Imaging-Biomarkers; from discovery to validation (September 28, 2014)

For people who are developing AI applications for health-care, retrospect on HistoScanning represents an excellent opportunity to better plan the life cycle of such products and what it would take to bring it to a level of wide adoption by global health systems.

It would require many pages to cover the lessons HistoScanning could teach each and all of us in detail. I will therefore briefly discuss the highlights:

  • Regulations: Clearance for HistoScanning by FDA required a PMA and was not achieved until today. The regulatory process in Europe was similar to that of ultrasound but getting harder in recent years.
  • Safety: During more than a decade and many thousands of procedures, no safety issue was brought up.
  • Learning curve: Many of the reports on HistoScanning conclude that in order to maximize its potential the sonographer must be experienced and well trained with using the system. Amongst else, it became clear that there is a strong correlation between the clinical added value of using HistoScanning and the quality of the ultrasound scan, which is dependant on the sonographer but also, in many cases, on the patient (e.g. his BMI)
  • Patient’s attitude: PMS reviews on HistoScanning shows that patients are generally excited about the opportunity of an AI application being involved in their diagnostic process. It seems to increase their confidence in the validity of the results and there was never a case of refusal to be exposed to the analysis. Also, some of the early adopters of PHS (HistoScanning for prostate) charged their patients privately for the service and patients were happy to accept that although there was no reimbursement of such cost by their health insurance.
  • Adoption by practitioners: To date, PHS did not achieve wide market adoption and users’ feedback on it are mixed, ranging from strong positive recommendation to very negative and dismissive. Close examination of the reasons for such a variety of experiences reveals that most of the reports are relying on small and largely varying samples. The reason for it being the relatively high complexity and cost of clinical trials aiming at measuring its performance. Moreover, without any available standards of assessing AI performance, what is good enough for one user can be totally insufficient for another. Realizing this led to recent efforts by some leading urologists to organize large patients’ registries related to routine-use of PHS.

The most recent peer-reviewed paper on PHS; Evaluation of Prostate HistoScanning as a Method for Targeted Biopsy in Routine Practice. Petr V. Glybochko, Yuriy G. Alyaev, Alexandr V. Amosov, German E. Krupinov, Dror Nir, Mathias Winkler, Timur M. Ganzha, European Urology Focus.

Studies PHS on statistically reasonable number (611) of patients and concluded that “Our study results support supplementing the standard schematic transrectal ultrasound-guided biopsy with a few guided cores harvested using the ultrasound-based prostate HistoScanning true targeting approach in cases for which multiparametric magnetic resonance imaging is not available.”

Read Full Post »

Older Posts »