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Archive for the ‘Medical Imaging Technology’ Category


Applying AI to Improve Interpretation of Medical Imaging

Author and Curator: Dror Nir, PhD

 

 

images

The idea that we can use machines’ intelligence to help us perform daily tasks is not an alien any more. As consequence, applying AI to improve the assessment of patients’ clinical condition is booming. What used to be the field of daring start-ups became now a playground for the tech-giants; Google, Amazon, Microsoft and IBM.

Interpretation of medical-Imaging involves standardised workflows and requires analysis of many data-items. Also, it is well established that human-subjectivity is a barrier to reproducibility and transferability of medical imaging results (evident by the reports on high intraoperative variability in  imaging-interpretation).Accepting the fact that computers are better suited that humans to perform routine, repeated tasks involving “big-data” analysis makes AI a very good candidate to improve on this situation.Google’s vision in that respect: “Machine learning has dozens of possible application areas, but healthcare stands out as a remarkable opportunity to benefit people — and working closely with clinicians and medical providers, we’re developing tools that we hope will dramatically improve the availability and accuracy of medical services.”

Google’s commitment to their vision is evident by their TensorFlow initiative. “TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.” Two recent papers describe in length the use of TensorFlow in retrospective studies (supported by Google AI) in which medical-images (from publicly accessed databases) where used:

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning, Nature Biomedical Engineering, Authors: Ryan Poplin, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, Greg S. Corrado, Lily Peng, and Dale R. Webster

As a demonstrator to the expected benefits the use of AI in interpretation of medical-imaging entails this is a very interesting paper. The authors show how they could extract information that is relevant for the assessment of the risk for having an adverse cardiac event from retinal fundus images collected while managing a totally different medical condition.  “Using deep-learning models trained on data from 284,335 patients and validated on two independent datasets of 12,026 and 999 patients, we predicted cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as age (mean absolute error within 3.26 years), gender (area under the receiver operating characteristic curve (AUC) = 0.97), smoking status (AUC = 0.71), systolic

blood pressure (mean absolute error within 11.23 mmHg) and major adverse cardiac events (AUC = 0.70).”

 

Screenshot 2019-05-28 at 10.07.21Screenshot 2019-05-28 at 10.09.40

Clearly, if such algorithm would be implemented as a generalised and transferrable medical-device that can be used in routine practice, it will contribute to the cost-effectiveness of screening programs.

 

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography, Nature Medicine, Authors: Diego Ardila, Atilla P. Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua J. Reicher, Lily Peng, Daniel Tse , Mozziyar Etemadi, Wenxing Ye, Greg Corrado, David P. Naidich and Shravya Shetty.

This paper is in line of many previously published works demonstrating how AI can increase the accuracy of cancer diagnosis in comparison to current state of the art: “Existing challenges include inter-grader variability and high false-positive and false-negative rates. We propose a deep learning algorithm that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases.”

Screenshot 2019-05-28 at 10.22.06Screenshot 2019-05-28 at 10.23.48

The benefit of using an AI based application for lung cancer screening (If and when such algorithm is implemented as a generalised and transferable medical device) is well summarised by the authors: “The strong performance of the model at the case level has important potential clinical relevance. The observed increase in specificity could translate to fewer unnecessary follow up procedures. Increased sensitivity in cases without priors could translate to fewer missed cancers in clinical practice, especially as more patients begin screening. For patients with prior imaging exams, the performance of the deep learning model could enable gains in workflow efficiency and consistency as assessment of prior imaging is already a key component of a specialist’s workflow. Given that LDCT screening is in the relatively early phases of adoption, the potential for considerable improvement in patient care in the coming years is substantial. The model’s localization directs follow-up for specific lesion(s) of greatest concern. These predictions are critical for patients proceeding for further work-up and treatment, including diagnostic CT, positron emission tomography (PET)/CT or biopsy. Malignancy risk prediction allows for the possibility of augmenting existing, manually created interpretation guidelines such as Lung-RADS, which are limited to subjective clustering and assessment to approximate cancer risk.

BTW: The methods section in these two papers is detailed enough to allow any interested party to reproduce the study.

For the sake of balance-of-information, I would like to note that:

  • Amazon is encouraging access to its AI platform Amazon SageMaker “Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly. Amazon SageMaker is a fully-managed service that covers the entire machine learning workflow to label and prepare your data, choose an algorithm, train the model, tune and optimize it for deployment, make predictions, and take action. Your models get to production faster with much less effort and lower cost.” Amazon is offering training courses to help programmers get proficiency in Machine-Learning using its AWS platform: “We offer 30+ digital ML courses totaling 45+ hours, plus hands-on labs and documentation, originally developed for Amazon’s internal use. Developers, data scientists, data platform engineers, and business decision makers can use this training to learn how to apply ML, artificial intelligence (AI), and deep learning (DL) to their businesses unlocking new insights and value. Validate your learning and your years of experience in machine learning on AWS with a new certification.”
  • IBM is offering a general-purpose AI platform named Watson. Watson is also promoted as a platform to develop AI applications in the “health” sector with the following positioning: “IBM Watson Health applies data-driven analytics, advisory services and advanced technologies such as AI, to deliver actionable insights that can help you free up time to care, identify efficiencies, and improve population health.”
  • Microsoft is offering its AI platform as a tool to accelerate development of AI solutions. They are also offering an AI school : “Dive in and learn how to start building intelligence into your solutions with the Microsoft AI platform, including pre-trained AI services like Cognitive Services and Bot Framework, as well as deep learning tools like Azure Machine Learning, Visual Studio Code Tools for AI and Cognitive Toolkit. Our platform enables any developer to code in any language and infuse AI into your apps. Whether your solutions are existing or new, this is the intelligence platform to build on.”

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The role of PET/CT in diagnosing giant cell arteritis (GCA) and assessing the risk of ischemic events

 

Reporter: Aviva Lev-Ari, PhD, RN

 

 

May 20, 2019 — PET/CT images are offering evidence of a link between vascular patterns at the time of diagnosis for giant cell arteritis (GCA) and a patient’s risk of an ischemic event, Spanish researchers explained in a study published online on 12 May in the European Journal of Nuclear Medicine and Molecular Imaging.

The group found that patients with inflammation in vertebral arteries, which causes blood vessels to narrow, were five times more likely to develop ischemic symptoms. The information may be particularly helpful because GCA is difficult to diagnose in its early stages.

“Bearing in mind these results and our findings, we consider that the vertebral arteries should be carefully studied in patients with suspected GCA, not only to support the diagnosis but also to assess the risk of development of ischemic events,” wrote lead author Dr. Jaume Mestre-Torres and colleagues from Hospital Vall d’Hebron in Barcelona.

GCA’s challenges

Giant cell arteritis is an inflammatory disease that causes the large blood vessels to narrow and restrict blood flow. The affliction is typically seen in the temporal arteries and the aorta in adults older than 50. Currently, there is little information on how the disease develops, although there are indications that it may be linked to genetics.

The challenge for clinicians is that there are “no specific clinical symptoms that lead to the diagnosis of GCA, but headache and ischemic symptoms such as jaw claudication and transient visual loss or permanent visual loss may raise suspicion [of the disease],” the authors noted.

Results

In assessing visual loss, the team found no significant differences between patients with vertebral artery involvement and permanent visual loss (61.5%) and patients with vertebral artery issues and no permanent visual loss (58.8%) (p = 0.88). Interestingly, the presence of intrathoracic large-vessel vasculitis tended to protect against a patient’s likelihood of permanent visual loss.

In addition, “all patients with vertebral involvement but no aortic involvement showed ischemic manifestations at disease onset,” the researchers noted. “In contrast, none of the patients with aortic involvement but no vertebral hypermetabolism showed ischemic symptoms.”

SOURCE

https://www.auntminnieeurope.com/index.aspx?sec=sup&sub=mol&pag=dis&ItemID=617395

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That is the question…

Anyone who follows healthcare news, as I do , cannot help being impressed with the number of scientific and non-scientific items that mention the applicability of Magnetic Resonance Imaging (‘MRI’) to medical procedures.

A very important aspect that is worthwhile noting is that the promise MRI bears to improve patients’ screening – pre-clinical diagnosis, better treatment choice, treatment guidance and outcome follow-up – is based on new techniques that enables MRI-based tissue characterisation.

Magnetic resonance imaging (MRI) is an imaging device that relies on the well-known physical phenomena named “Nuclear Magnetic Resonance”. It so happens that, due to its short relaxation time, the 1H isotope (spin ½ nucleus) has a very distinctive response to changes in the surrounding magnetic field. This serves MRI imaging of the human body well as, basically, we are 90% water. The MRI device makes use of strong magnetic fields changing at radio frequency to produce cross-sectional images of organs and internal structures in the body. Because the signal detected by an MRI machine varies depending on the water content and local magnetic properties of a particular area of the body, different tissues or substances can be distinguished from one another in the scan’s resulting image.

The main advantages of MRI in comparison to X-ray-based devices such as CT scanners and mammography systems are that the energy it uses is non-ionizing and it can differentiate soft tissues very well based on differences in their water content.

In the last decade, the basic imaging capabilities of MRI have been augmented for the purpose of cancer patient management, by using magnetically active materials (called contrast agents) and adding functional measurements such as tissue temperature to show internal structures or abnormalities more clearly.

 

In order to increase the specificity and sensitivity of MRI imaging in cancer detection, various imaging strategies have been developed. The most discussed in MRI related literature are:

  • T2 weighted imaging: The measured response of the 1H isotope in a resolution cell of a T2-weighted image is related to the extent of random tumbling and the rotational motion of the water molecules within that resolution cell. The faster the rotation of the water molecule, the higher the measured value of the T2 weighted response in that resolution cell. For example, prostate cancer is characterized by a low T2 response relative to the values typical to normal prostatic tissue [5].

T2 MRI pelvis with Endo Rectal Coil ( DATA of Dr. Lance Mynders, MAYO Clinic)

  • Dynamic Contrast Enhanced (DCE) MRI involves a series of rapid MRI scans in the presence of a contrast agent. In the case of scanning the prostate, the most commonly used material is gadolinium [4].

Axial MRI  Lava DCE with Endo Rectal ( DATA of Dr. Lance Mynders, MAYO Clinic)

  • Diffusion weighted (DW) imaging: Provides an image intensity that is related to the microscopic motion of water molecules [5].

DW image of the left parietal glioblastoma multiforme (WHO grade IV) in a 59-year-old woman, Al-Okaili R N et al. Radiographics 2006;26:S173-S189

  • Multifunctional MRI: MRI image overlaid with combined information from T2-weighted scans, dynamic contrast-enhancement (DCE), and diffusion weighting (DW) [5].

Source AJR: http://www.ajronline.org/content/196/6/W715/F3

  • Blood oxygen level-dependent (BOLD) MRI: Assessing tissue oxygenation. Tumors are characterized by a higher density of micro blood vessels. The images that are acquired follow changes in the concentration of paramagnetic deoxyhaemoglobin [5].

In the last couple of years, medical opinion leaders are offering to use MRI to solve almost every weakness of the cancer patients’ pathway. Such proposals are not always supported by any evidence of feasibility. For example, a couple of weeks ago, the British Medical Journal published a study [1] concluding that women carrying a mutation in the BRCA1 or BRCA2 genes who have undergone a mammogram or chest x-ray before the age of 30 are more likely to develop breast cancer than those who carry the gene mutation but who have not been exposed to mammography. What is published over the internet and media to patients and lay medical practitioners is: “The results of this study support the use of non-ionising radiation imaging techniques (such as magnetic resonance imaging) as the main tool for surveillance in young women with BRCA1/2 mutations.”.

Why is ultrasound not mentioned as a potential “non-ionising radiation imaging technique”?

Another illustration is the following advert:

An MRI scan takes between 30 to 45 minutes to perform (not including the time of waiting for the interpretation by the radiologist). It requires the support of around 4 well-trained team members. It costs between $400 and $3500 (depending on the scan).

The important question, therefore, is: Are there, in the USA, enough MRI  systems to meet the demand of 40 million scans a year addressing women with radiographically dense  breasts? Toda there are approximately 10,000 MRI systems in the USA. Only a small percentage (~2%) of the examinations are related to breast cancer. A

A rough calculation reveals that around 10000 additional MRI centers would need to be financed and operated to meet that demand alone.

References

  1. Exposure to diagnostic radiation and risk of breast cancer among carriers of BRCA1/2 mutations: retrospective cohort study (GENE-RAD-RISK), BMJ 2012; 345 doi: 10.1136/bmj.e5660 (Published 6 September 2012), Cite this as: BMJ 2012;345:e5660 – http://www.bmj.com/content/345/bmj.e5660
  1. http://www.auntminnieeurope.com/index.aspx?sec=sup&sub=wom&pag=dis&itemId=607075
  1. Ahmed HU, Kirkham A, Arya M, Illing R, Freeman A, Allen C, Emberton M. Is it time to consider a role for MRI before prostate biopsy? Nat Rev Clin Oncol. 2009;6(4):197-206.
  1. Puech P, Potiron E, Lemaitre L, Leroy X, Haber GP, Crouzet S, Kamoi K, Villers A. Dynamic contrast-enhanced-magnetic resonance imaging evaluation of intraprostatic prostate cancer: correlation with radical prostatectomy specimens. Urology. 2009;74(5):1094-9.
  1. Advanced MR Imaging Techniques in the Diagnosis of Intraaxial Brain Tumors in Adults, Al-Okaili R N et al. Radiographics 2006;26:S173-S189 ,

http://radiographics.rsna.org/content/26/suppl_1/S173.full

  1. Ahmed HU. The Index Lesion and the Origin of Prostate Cancer. N Engl J Med. 2009 Oct; 361(17): 1704-6

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Comparison of four methods in diagnosing acute myocarditis: The diagnostic performance of native T1, T2, ECV to LLC

 

Reporter: Aviva Lev-Ari, PhD, RN

 

Abstract

Background:

The Lake Louise Criteria (LLC) were established in 2009 and are the recommended cardiac magnetic resonance imaging criterion for diagnosing patients with suspected myocarditis. Subsequently, newer parametric imaging techniques which can quantify T1, T2, and the extracellular volume (ECV) have been developed and may provide additional utility in the diagnosis of myocarditis. However, whether their diagnostic accuracy is superior to LLC remains unclear. In this meta-analysis, we compared the diagnostic performance of native T1, T2, ECV to LLC in diagnosing acute myocarditis.

Methods and Results:

We searched PubMed for published studies of LLC, native T1, ECV, and T2 diagnostic criteria used to diagnose acute myocarditis. Seventeen studies were included, with a total of 867 myocarditis patients and 441 control subjects. Pooled sensitivity, specificity, and diagnostic odds ratio of all diagnostic tests were assessed by bivariate analysis. LLC had a pooled sensitivity of 74%, specificity of 86%, and diagnostic odds ratio of 17.7. Native T1 had a significantly higher sensitivity than LLC (85% versus 74%, P=0.025). Otherwise, there was no significant difference in sensitivity, specificity, and diagnostic odds ratio when comparing LLC to native T1, T2, or ECV.

Conclusions:

Native T1, T2, and ECV mapping provide comparable diagnostic performance to LLC. Although only native T1 had significantly better sensitivity than LLC, each technique offers distinct advantages for evaluating and characterizing myocarditis when compared with the LLC.

SOURCE

https://www.ahajournals.org/doi/10.1161/CIRCIMAGING.118.007598

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Live Conference Coverage @Medcitynews Converge 2018 Philadelphia: The Davids vs. the Cancer Goliath Part 2

8:40 – 9:25 AM The Davids vs. the Cancer Goliath Part 2

Startups from diagnostics, biopharma, medtech, digital health and emerging tech will have 8 minutes to articulate their visions on how they aim to tame the beast.

Start Time End Time Company
8:40 8:48 3Derm
8:49 8:57 CNS Pharmaceuticals
8:58 9:06 Cubismi
9:07 9:15 CytoSavvy
9:16 9:24 PotentiaMetrics

Speakers:
Liz Asai, CEO & Co-Founder, 3Derm Systems, Inc. @liz_asai
John M. Climaco, CEO, CNS Pharmaceuticals @cns_pharma 

John Freyhof, CEO, CytoSavvy
Robert Palmer, President & CEO, PotentiaMetrics @robertdpalmer 
Moira Schieke M.D., Founder, Cubismi, Adjunct Assistant Prof UW Madison @cubismi_inc

 

3Derm Systems

3Derm Systems is an image analysis firm for dermatologic malignancies.  They use a tele-medicine platform to accurately triage out benign malignancies observed from the primary care physician, expediate those pathology cases if urgent to the dermatologist and rapidly consults with you over home or portable device (HIPAA compliant).  Their suite also includes a digital dermatology teaching resource including digital training for students and documentation services.

 

CNS Pharmaceuticals

developing drugs against CNS malignancies, spun out of research at MD Anderson.  They are focusing on glioblastoma and Berubicin, an anthracycline antiobiotic (TOPOII inhibitor) that can cross the blood brain barrier.  Berubicin has good activity in a number of animal models.  Phase I results were very positive and Phase II is scheduled for later in the year.  They hope that the cardiotoxicity profile is less severe than other anthracyclines.  The market opportunity will be in temazolamide resistant glioblastoma.

Cubismi

They are using machine learning and biomarker based imaging to visualize tumor heterogeneity. “Data is the new oil” (Intel CEO). We need prediction machines so they developed a “my body one file” system, a cloud based data rich file of a 3D map of human body.

CUBISMI IS ON A MISSION TO HELP DELIVER THE FUTURE PROMISE OF PRECISION MEDICINE TO CURE DISEASE AND ASSURE YOUR OPTIMAL HEALTH.  WE ARE BUILDING A PATIENT-DOCTOR HEALTH DATA EXCHANGE PLATFORM THAT WILL LEVERAGE REVOLUTIONARY MEDICAL IMAGING TECHNOLOGY AND PUT THE POWER OF HEALTH DATA INTO THE HANDS OF YOU AND YOUR DOCTORS.

 

CytoSavvy

CytoSavvy is a digital pathology company.  They feel AI has a fatal flaw in that no way to tell how a decision was made. Use a Shape Based Model Segmentation algorithm which uses automated image analysis to provide objective personalized pathology data.  They are partnering with three academic centers (OSU, UM, UPMC) and pool data and automate the rule base for image analysis.

CytoSavvy’s patented diagnostic dashboards are intuitive, easy–to-use and HIPAA compliant. Our patented Shape-Based Modeling Segmentation (SBMS) algorithms combine shape and color analysis capabilities to increase reliability, save time, and improve decisions. Specifications and capabilities for our web-based delivery system follow.

link to their white paper: https://www.cytosavvy.com/resources/healthcare-ai-value-proposition.pdf

PotentialMetrics

They were developing a diagnostic software for cardiology epidemiology measuring outcomes however when a family member got a cancer diagnosis felt there was a need for outcomes based models for cancer treatment/care.  They deliver real world outcomes for persoanlized patient care to help patients make decisions on there care by using a socioeconomic modeling integrated with real time clinical data.

Featured in the Wall Street Journal, using the informed treatment decisions they have generated achieve a 20% cost savings on average.  There research was spun out of Washington University St. Louis.

They have concentrated on urban markets however the CEO had mentioned his desire to move into more rural areas of the country as there models work well for patients in the rural setting as well.

Please follow on Twitter using the following #hash tags and @pharma_BI 

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

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3D-printed body parts could replace cadavers for medical training

Reporter: Irina Robu, PhD

Even though, the 3-D printing based tissue modeling is still in early phases it is considered a promising approach for anatomy training. Models that are produced on a computer screen can be reproduced as tangible objects that students can examine and even dissect. According to a recent report in Medical Science Educator, the latest advancement in 3D printing can revolutionize how anatomy students learn.

For now, human cadavers have been the norm for studying human anatomy but they come with financial and logistical concerns both on storage and disposal. However, with the advancement of custom designed 3D organs, made possible by using 3D printing the need to keep large collection of physical models are reduced. With just a 3D printer, a digital model of the organ needed to study can be reproduced either with resin, thermoplastics, photopolymers and other material. Different materials can be used to allow construction of complex models with hard, soft, opaque and transparent conditions. The printed body parts will look exactly the same as the real thing because they are falsely colored to help students distinguish between the different parts of the anatomy including ligaments, muscles and blood vessels. Medical schools and hospitals around the world would be able to buy just an arm or a foot or the entire body depending on their training need.

Furthermore, to customizing anatomy lessons, 3D printed models can be used for teaching pathology/radiology by comparing CT images of the organs to their 3D-printed counterparts which students can examine and understand. Yet, the methods of 3D printing vary by materials used, resolution accuracy, long term stability, cost, speed and more. The printer cost is still a concern at this point partly because 3D bioprinters cost thousands of dollars nonetheless the cost is dropping due to the introduction of innovative printing materials.

Therefore, in order for 3-D printing to become more widely used, costs must be reduced while resolution must continue to improve. Instructors can potentially print one model per student in a material of their choosing that can be dissected. And no matter how much medical science moves with the times, there would always be the requisite skeleton model in the corner of most anatomy rooms.

SOURCE

http://www.abc.net.au/news/2014-07-22/an-3d-body-parts-could-replace-cadavers-for-medical-training/5615210

 

Additional Resources

Medical Science Educator, June 2015, Volume 25, Issue 2, pp 183–194| Cite as

Anatomical Models: a Digital Revolution

https://link.springer.com/article/10.1007/s40670-015-0115-9/fulltext.html

 

Goodbye to Cadavers?

https://consultqd.clevelandclinic.org/2015/09/goodbye-to-cadavers/

 

3-D Printing: Innovation Allows Customized Airway Stents

https://consultqd.clevelandclinic.org/2014/12/3-d-printing-innovation-allows-customized-airway-stents/

 

Exploring 3-D Printing’s Potential in Renal Surgery

https://consultqd.clevelandclinic.org/2015/06/exploring-3-d-printings-potential-in-renal-surgery/

 

How 3-D Printing Is Revolutionizing Medicine at Cleveland Clinic

https://consultqd.clevelandclinic.org/2015/11/how-3-d-printing-is-revolutionizing-medicine-at-cleveland-clinic/

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Sperm Analysis by Smart Phone

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

 

Low sperm count and motility are markers for male infertility, a condition that is actually a neglected health issue worldwide, according to the World Health Organization. Researchers at Harvard Medical School have developed a very low cost device that can attach to a cell phone and provides a quick and easy semen analysis. The device is still under development, but a study of the machine’s capabilities concludes that it is just as accurate as the elaborate high cost computer-assisted semen analysis machines costing tens of thousands of dollars in measuring sperm concentration, sperm motility, total sperm count and total motile cells.

 

The Harvard team isn’t the first to develop an at-home fertility test for men, but they are the first to be able to determine sperm concentration as well as motility. The scientists compared the smart phone sperm tracker to current lab equipment by analyzing the same semen samples side by side. They analyzed over 350 semen samples of both infertile and fertile men. The smart phone system was able to identify abnormal sperm samples with 98 percent accuracy. The results of the study were published in the journal named Science Translational Medicine.

 

The device uses an optical attachment for magnification and a disposable microchip for handling the semen sample. With two lenses that require no manual focusing and an inexpensive battery, it slides onto the smart phone’s camera. Total cost for manufacturing the equipment: $4.45, including $3.59 for the optical attachment and 86 cents for the disposable micro-fluidic chip that contains the semen sample.

 

The software of the app is designed with a simple interface that guides the user through the test with onscreen prompts. After the sample is inserted, the app can photograph it, create a video and report the results in less than five seconds. The test results are stored on the phone so that semen quality can be monitored over time. The device is under consideration for approval from the Food and Drug Administration within the next two years.

 

With this device at home, a man can avoid the embarrassment and stress of providing a sample in a doctor’s clinic. The device could also be useful for men who get vasectomies, who are supposed to return to the urologist for semen analysis twice in the six months after the procedure. Compliance is typically poor, but with this device, a man could perform his own semen analysis at home and email the result to the urologist. This will make sperm analysis available in the privacy of our home and as easy as a home pregnancy test or blood sugar test.

 

The device costs about $5 to make in the lab and can be made available in the market at lower than $50 initially. This low cost could help provide much-needed infertility care in developing or underdeveloped nations, which often lack the resources for currently available diagnostics.

 

References:

 

https://www.nytimes.com/2017/03/22/well/live/sperm-counts-via-your-cellphone.html?em_pos=small&emc=edit_hh_20170324&nl=well&nl_art=7&nlid=65713389&ref=headline&te=1&_r=1

 

http://www.npr.org/sections/health-shots/2017/03/22/520837557/a-smartphone-can-accurately-test-sperm-count

 

https://www.ncbi.nlm.nih.gov/pubmed/28330865

 

http://www.sciencealert.com/new-smartphone-microscope-lets-men-check-the-health-of-their-own-sperm

 

https://www.newscientist.com/article/2097618-are-your-sperm-up-to-scratch-phone-microscope-lets-you-check/

 

https://www.dezeen.com/2017/01/19/yo-fertility-kit-men-test-sperm-count-smartphone-design-technology-apps/

 

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