Technology could enable patients to monitor their own oxygen levels and transmit that data to healthcare providers, including clinical laboratories
Clinical laboratories may soon have a new data point to add to their laboratory information system (LIS) for doctors to review. Researchers have determined that smartphones can read blood-oxygen levels as accurately as purpose-built pulse oximeters.
Conducted by researchers at the University of Washington (UW) and University of California San Diego (UC San Diego), the proof-of-concept study found that an unmodified smartphone camera and flash along with an app is “capable of detecting blood oxygen saturation levels down to 70%. This is the lowest value that pulse oximeters should be able to measure, as recommended by the US Food and Drug Administration,” according to Digital Health News.
This could mean that patients at risk of hypoxemia, or who are suffering a respiratory illness such as COVID-19, could eventually add accurate blood-oxygen saturation (SpO2) readings to their lab test results at any time and from any location.
“In an ideal world, this information could be seamlessly transmitted to a doctor’s office. This would be really beneficial for telemedicine appointments or for triage nurses to be able to quickly determine whether patients need to go to the emergency department or if they can continue to rest at home and make an appointment with their primary care provider later,” Matthew Thompson, DPhil, Professor of Global Health and Family Medicine at University of Washington, told Digital Health News. Clinical laboratories may soon have a new data point for their laboratory information systems. (Photo copyright. University of Washington.)
UW/UC San Diego Study Details
The researchers studied three men and three women, ages 20-34. All were Caucasian except for one African American, Digital Health News reported. To conduct the study, a standard pulse oximeter was placed on a finger and, on the same hand, another of the participant’s fingers was placed over a smartphone camera.
“We performed the first clinical development validation on a smartphone camera-based SpO2 sensing system using a varied fraction of inspired oxygen (FiO2) protocol, creating a clinically relevant validation dataset for solely smartphone-based contact PPG [photoplethysmography] methods on a wider range of SpO2 values (70–100%) than prior studies (85–100%). We built a deep learning model using this data to demonstrate an overall MAE [Mean Absolute Error] = 5.00% SpO2 while identifying positive cases of low SpO2 < 90% with 81% sensitivity and 79% specificity,” the researchers wrote in NPJ Digital Medicine.
When the smartphone camera’s flash passes light through the finger, “a deep-learning algorithm deciphers the blood oxygen levels.” Participants were also breathing in “a controlled mixture of oxygen and nitrogen to slowly reduce oxygen levels,” Digital Health News reported.
“The camera is recording a video: Every time your heart beats, fresh blood flows through the part illuminated by the flash,” Edward Wang, PhD, Assistant Professor of Electrical and Computer Engineering at UC San Diego and senior author of the project, told Digital Health News. Wang started this project as a UW doctoral student studying electrical and computer engineering and now directs the UC San Diego DigiHealth Lab.
“The camera records how much that blood absorbs the light from the flash in each of the three color channels it measures: red, green, and blue. Then we can feed those intensity measurements into our deep-learning model,” he added.
The deep learning algorithm “pulled out the blood oxygen levels. The remainder of the data was used to validate the method and then test it to see how well it performed on new subjects,” Digital Health News reported.
“Smartphone light can get scattered by all these other components in your finger, which means there’s a lot of noise in the data that we’re looking at,” Varun Viswanath, co-lead author in the study, told Digital Health News. Viswanath is a UW alumnus who is now a doctoral student being advised by Wang at UC San Diego.
“Deep learning is a really helpful technique here because it can see these really complex and nuanced features and helps you find patterns that you wouldn’t otherwise be able to see,” he added.
Each round of testing took approximately 15 minutes. In total the researchers gathered more than 10,000 blood oxygen readings. Levels ranged from 61% to 100%.
“The smartphone correctly predicted whether the subject had low blood oxygen levels 80% of the time,” Digital Health News reported.
Smartphones Accurately Collecting Data
The UW/UC San Diego study is the first to show such precise results using a smartphone.
“Other smartphone apps that do this were developed by asking people to hold their breath. But people get very uncomfortable and have to breathe after a minute or so, and that’s before their blood-oxygen levels have gone down far enough to represent the full range of clinically relevant data,” said Jason Hoffman, a PhD student researcher at UW’s UbiComp Lab and co-lead author of the study.
The ability to track a full 15 minutes of data is a prime example of improvement. “Our data shows that smartphones could work well right in the critical threshold range,” Hoffman added.
“Smartphone-based SpO2 monitors, especially those that rely only on built-in hardware with no modifications, present an opportunity to detect and monitor respiratory conditions in contexts where pulse oximeters are less available,” the researchers wrote.
“This way you could have multiple measurements with your own device at either no cost or low cost,” Matthew Thompson, DPhil, Professor of Global Health and Family Medicine at University of Washington, told Digital Health News. Thompson is a professor of both family medicine and global health and an adjunct professor of pediatrics at the UW School of Medicine.
What Comes Next
The UW/UC San Diego research team plans to continue its research and gather more diversity among subjects.
“It’s so important to do a study like this,” Wang said. “Traditional medical devices go through rigorous testing. But computer science research is still just starting to dig its teeth into using machine learning for biomedical device development and we’re all still learning. By forcing ourselves to be rigorous, we’re forcing ourselves to learn how to do things right.”
Though no current clinical laboratory application is pending, smartphone use to capture biometrics for testing is increasing. Soon, labs may need a way to input all that data into their laboratory information systems. It’s something to consider.
Though smartphone apps are technically not clinical laboratory tools, anatomic pathologists and medical laboratory scientists (MLSs) may be interested to learn how health information technology (HIT), machine learning, and smartphone apps are being used to assess different aspects of individuals’ health, independent of trained healthcare professionals.
The issue that the Cedars Sinai researchers were investigating is the accuracy of patient self-reporting. Because poop can be more complicated than meets the eye, when asked to describe their bowel movements patients often find it difficult to be specific. Thus, use of a smartphone app that enables patients to accurately assess their stools in cases where watching the function of their digestive tract is relevant to their diagnoses and treatment would be a boon to precision medicine treatments of gastroenterology diseases.
“This app takes out the guesswork by using AI—not patient input—to process the images (of bowel movements) taken by the smartphone,” said gastroenterologist Mark Pimentel, MD (above), Executive Director of Cedars-Sinai’s Medically Associated Science and Technology (MAST) program and principal investigator of the study, in a news release. “The mobile app produced more accurate and complete descriptions of constipation, diarrhea, and normal stools than a patient could, and was comparable to specimen evaluations by well-trained gastroenterologists in the study.” (Photo copyright: Cedars-Sinai.)
Pros and Cons of Bristol Stool Scale
In their paper, the scientists discussed the Bristol Stool Scale (BSS), a traditional diagnostic tool for identifying stool forms into seven categories. The seven types of stool are:
Type 1: Separate hard lumps, like nuts (difficult to pass).
Type 2: Sausage-shaped, but lumpy.
Type 3: Like a sausage, but with cracks on its surface.
Type 4: Like a sausage or snake, smooth and soft (average stool).
Type 5: Soft blobs with clear cut edges.
Type 6: Fluffy pieces with ragged edges, a mushy stool (diarrhea).
Type 7: Watery, no solid pieces, entirely liquid (diarrhea).
But even with the BSS, things can get murky for patients. Inaccurate self-reporting of stool forms by people with IBS and diarrhea can make proper diagnoses difficult.
“The problem is that whenever you have a patient reporting an outcome measure, it becomes subjective rather than objective. This can impact the placebo effect,” gastroenterologist Mark Pimentel, MD, Executive Director of Cedars-Sinai’s Medically Associated Science and Technology (MAST) program and principal investigator of the study, told Healio.
Thus, according to the researchers, AI algorithms can help with diagnosis by systematically doing the assessments for the patients, News Medical reported.
30,000 Stool Images Train New App
To conduct their study, the Cedars-Sinai researchers tested an AI smartphone app developed by Dieta Health. According to Health IT Analytics, employing AI trained on 30,000 annotated stool images, the app characterizes digital images of bowel movements using five parameters:
BSS,
Consistency,
Edge fuzziness,
Fragmentation, and
Volume.
“The app used AI to train the software to detect the consistency of the stool in the toilet based on the five parameters of stool form, We then compared that with doctors who know what they are looking at,” Pimentel told Healio.
AI Assessments Comparable to Doctors, Better than Patients
According to Health IT Analytics, the researchers found that:
AI assessed the stool comparable to gastroenterologists’ assessments on BSS, consistency, fragmentation, and edge fuzziness scores.
AI and gastroenterologists had moderate-to-good agreement on volume.
AI outperformed study participant self-reports based on the BSS with 95% accuracy, compared to patients’ 89% accuracy.
Additionally, the AI outperformed humans in specificity and sensitivity as well:
Specificity (ability to correctly report a negative result) was 27% higher.
Sensitivity (ability to correctly report a positive result) was 23% higher.
“A novel smartphone application can determine BSS and other visual stool characteristics with high accuracy compared with the two expert gastroenterologists. Moreover, trained AI was superior to subject self-reporting of BSS. AI assessments could provide more objective outcome measures for stool characterization in gastroenterology,” the Cedars-Sinai researchers wrote in their paper.
“In addition to improving a physician’s ability to assess their patients’ digestive health, this app could be advantageous for clinical trials by reducing the variability of stool outcome measures,” said gastroenterologist Ali Rezaie, MD, study co-author and Medical Director of Cedars-Sinai’s GI Motility Program in the news release.
The researchers plan to seek FDA review of the mobile app.
Opportunity for Clinical Laboratories
Anatomic pathologists and clinical laboratory leaders may want to reach out to referring gastroenterologists to find out how they can help to better serve gastro patients. As the Cedars-Sinai study suggests, AI smartphone apps can perform BSS assessments as good as or better than humans and may be useful tools in the pursuit of precision medicine treatments for patient suffering from painful gastrointestinal disorders.
UW scientists believe their at-home test could help more people on anticoagulants monitor their clotting levels and avoid blood clots
In a proof-of-concept study,researchers at the University of Washington (UW) are developing a new smartphone-based technology/application designed to enable people on anticoagulants such as warfarin to monitor their clotting levels from the comfort of their homes. Should this new test methodology prove successful, clinical laboratories may have yet one more source of competition from this at-home PT/INR test solution.
PT/INR (prothrombin time with an international normalized ratio) is one of the most frequently performed clinical laboratory blood tests. This well-proven assay helps physicians monitor clotting in patients taking certain anticoagulation medications.
However, the process can be onerous for those on anticoagulation drugs. Users of this type of medication must have their blood tested regularly—typically by a clinical laboratory—to ensure the medication is working effectively. When not, a doctor visit is required to adjust the amount of the medication in the bloodstream.
Alternatively, where a state’s scope of practice law permits, pharmacists can perform a point-of-care test for the patient, thus allowing the pharmacist to appropriately adjust the patient’s prescription.
Though in the early stages of its development, were the UW’s new smartphone-based blood clotting test to be cleared by the federal Food and Drug Administration (FDA), then users would only need to see a doctor when their readings went and stayed out of range, according to Clinical Lab Products (CLP).
Enabling Patients to Test Their Blood More Frequently
More than eight million Americans with mechanical heart valves or other cardiac conditions take anticoagulants, and 55% of people taking those medication say they fear experiencing life-threatening bleeding, according to the National Blood Clot Alliance.
They have reason to be worried. Even when taking an anticoagulation drug, its level may not stay within therapeutic range due to the effects of food and other medications, experts say.
“In the US, most people are only in what we call the ‘desirable range’ of PT/INR levels about 64% of the time. This number is even lower—only about 40% of the time—in countries such as India or Uganda, where there is less frequent testing. We need to make it easier for people to test more frequently,” said anesthesiologist and co-author of the study Kelly Michaelsen, MD, PhD, UW Assistant Professor of Anesthesiology and Pain Medicine, in a UW news release.
“Back in the day, doctors used to manually rock tubes of blood back and forth to monitor how long it took a clot to form. This, however, requires a lot of blood, making it infeasible to use in home settings,” said senior study author Shyam Gollakota, PhD (above), professor and head of the Networks and Mobile Systems Lab at UW’s Paul G. Allen School of Computer Science and Engineering, in the UW news release. “The creative leap we make here is that we’re showing that by using the vibration motor on a smartphone, our algorithms can do the same thing, except with a single drop of blood. And we get accuracy similar to the best commercially available techniques [used by clinical laboratories].” (Photo copyright: University of Washington.)
How UW’s Smartphone-based Blood Clotting Test Works
The UW researchers were motived by the success of home continuous glucose monitors, which enable diabetics to continually track their blood glucose levels.
According to the Nature Communications paper, here’s how UW’s “smartphone-based micro-mechanical clot detection system” works:
Samples of blood plasma and whole blood are placed into a thimble-size plastic cup.
The cup includes a small copper particle and thromboplastin activator.
When the smartphone is turned on and vibrating, the cup (which is mounted on an attachment) moves beneath the phone’s camera.
Video analytic algorithms running on the smartphone track the motion of the copper particle.
If blood clots, the “viscous mixture” slows and stops.
PT/INR values can be determined in less than a minute.
“Our system visually tracks the micro-mechanical movements of a small copper particle in a cup with either a single drop of whole blood or plasma and the addition of activators,” the researchers wrote in Nature Communications. “As the blood clots, it forms a network that tightens. And in that process, the particle goes from happily bouncing around to no longer moving,” Michaelsen explained.
The system produced these results:
140 de-identified plasma samples: PT/INR with inter-class correlation coefficients of 0.963 and 0.966.
79 de-identified whole blood samples: 0.974 for both PT/INR.
Another At-home Test That Could Impact Clinical Laboratories
The UW scientists intend to test the system with patients in their homes, and in areas and countries with limited testing resources, Medical Device Network reported.
Should UW’s smartphone-based blood-clotting test be cleared by the FDA, there could be a ready market for it. But it will need to be offered it at a price competitive with current clinical laboratory assays for blood clotting, as well as with the current point-of-care tests in use today.
Nevertheless, UW’s work is the latest example of a self-testing methodology that could become a new competitor for clinical laboratories. This may motivate medical laboratories to keep PT/INR testing costs low, while also reporting quick and accurate results to physicians and patients on anticoagulants.
Alternatively, innovative clinical laboratories could develop a patient management service to oversee a patient’s self-testing at home and coordinate delivery of the results with the patient’s physician and pharmacist. This approach would enable the lab to add value for which it could be reimbursed.
MIT’s deep learning artificial intelligence algorithm demonstrates how similar new technologies and smartphones can be combined to give dermatologists and dermatopathologists valuable new ways to diagnose skin cancer from digital images
According to an MIT press release, “The paper describes the development of an SPL [Suspicious Pigmented Lesion] analysis system using DCNNs [Deep Convolutional Neural Networks] to more quickly and efficiently identify skin lesions that require more investigation, screenings that can be done during routine primary care visits, or even by the patients themselves. The system utilized DCNNs to optimize the identification and classification of SPLs in wide-field images.”
The MIT scientists believe their AI analysis system could aid dermatologists, dermatopathologists, and clinical laboratories detect melanoma, a deadly form of skin cancer, in its early stages using smartphones at the point-of-care.
“Our research suggests that systems leveraging computer vision and deep neural networks, quantifying such common signs, can achieve comparable accuracy to expert dermatologists,” said Luis Soenksen, PhD (above), Venture Builder in Artificial Intelligence and Healthcare at MIT and first author of the study in an MIT press release. “We hope our research revitalizes the desire to deliver more efficient dermatological screenings in primary care settings to drive adequate referrals.” The MIT study demonstrates that dermatologists, dermatopathologists, and clinical laboratories can benefit from using common technologies like smartphones in the diagnosis of disease. (Photo copyright: Wyss Institute Harvard University.)
Improving Melanoma Treatment and Patient Outcomes
Melanoma develops when pigment-producing cells called melanocytes start to grow out of control. The cancer has traditionally been diagnosed through visual inspection of SPLs by physicians in medical settings. Early-stage identification of SPLs can drastically improve the prognosis for patients and significantly reduce treatment costs. It is common to biopsy many lesions to ensure that every case of melanoma can be diagnosed as early as possible, thus contributing to better patient outcomes.
“Early detection of SPLs can save lives. However, the current capacity of medical systems to provide comprehensive skin screenings at scale are still lacking,” said Luis Soenksen, PhD, Venture Builder in Artificial Intelligence and Healthcare at MIT and first author of the study in the MIT press release.
The researchers trained their AI system by using 20,388 wide-field images from 133 patients at the Gregorio Marañón General University Hospital in Madrid, as well as publicly available images. The collected photographs were taken with a variety of ordinary smartphone cameras that are easily obtainable by consumers.
They taught the deep learning algorithm to examine various features of skin lesions such as size, circularity, and intensity. Dermatologists working with the researchers also visually classified the lesions for comparison.
When the algorithm is “shown” a wide-field image like that above taken with a smartphone, it uses deep convolutional neural networks to analyze individual pigmented lesions and screen for early-stage melanoma. The algorithm then marks suspicious images as either yellow (meaning further inspection should be considered) or red (indicating that further inspection and/or referral to a dermatologist is required). Using this tool, dermatopathologists may be able to diagnose skin cancer and excise it in-office long before it becomes deadly. (Photo copyright: MIT.)
“Our system achieved more than 90.3% sensitivity (95% confidence interval, 90 to 90.6) and 89.9% specificity (89.6 to 90.2%) in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, avoiding the need for cumbersome individual lesion imaging,” the MIT researchers noted in their Science Translational Medicine paper.
In addition, the algorithm agreed with the consensus of experienced dermatologists 88% of the time and concurred with the opinions of individual dermatologists 86% of the time, Medgadget reported.
Modern Imaging Technologies Will Advance Diagnosis of Disease
According to the American Cancer Society, about 106,110 new cases of melanoma will be diagnosed in the United States in 2021. Approximately 7,180 people are expected to die of the disease this year. Melanoma is less common than other types of skin cancer but more dangerous as it’s more likely to spread to other parts of the body if not detected and treated early.
More research is needed to substantiate the effectiveness and accuracy of this new tool before it could be used in clinical settings. However, the early research looks promising and smartphone camera technology is constantly improving. Higher resolutions would further advance development of this type of diagnostic tool.
In addition, MIT’s algorithm enables in situ examination and possible diagnosis of cancer. Therefore, a smartphone so equipped could enable a dermatologist to diagnose and excise cancerous tissue in a single visit, without the need for biopsies to be sent to a dermatopathologist.
Currently, dermatologists refer a lot of skin biopsies to dermapathologists and anatomic pathology laboratories. An accurate diagnostic tool that uses modern smartphones to characterize suspicious skin lesions could become quite popular with dermatologists and affect the flow of referrals to medical laboratories.
Amazon’s app-based employee healthcare service could be first step toward retailer becoming a disruptive force in healthcare; federal VA develops its own mHealth apps
More consumers are using smartphone applications (apps) to manage different aspects of their healthcare. That fact should put clinical laboratories and anatomic pathology groups on the alert, because a passive “wait and see” strategy for making relevant services and lab test information available via mobile apps could cause patients to choose other labs that do offer such services.
Patient use of apps to manage healthcare is an important trend. In January, Dark Daily covered online retail giant Amazon’s move to position itself as a leader in smartphone app-based healthcare with its launch of Amazon Care, a virtual medical clinic and homecare services program. At that time, the program was being piloted for Seattle-based employees and their families only. Since then, it has been expanded to include eligible Amazon employees throughout Washington State.
Mobile health (mHealth) apps are giving healthcare providers rapid access to patient information. And healthcare consumers are increasingly turning to their mobile devices for 24/7 access to medical records, clinical laboratory test results, management of chronic conditions, and quick appointment scheduling and prescription refills.
Thus, hearing ‘There’s an app for that’ has become part of patients’ expectations for access to quality, affordable healthcare.
For clinical laboratory managers, this steady shift toward mHealth-based care means accommodating patients who want to use mobile apps to access lab test results and on-demand lab data to monitor their health or gain advice from providers about symptoms and health issues.
Amazon, VA, and EMS Develop Their Own mHealth Apps
The Amazon Care app can be freely downloaded from Apple’s App Store and Google Play. With it, eligible employees and family members can:
Communicate with an advice nurse;
Launch an in-app video visit with a doctor or nurse practitioner for advice, diagnoses, treatment, or referrals;
Request a mobile care nurse for in-home or in-office visits;
Receive prescriptions through courier delivery.
The combination telehealth, in-person care program, mobile medical service includes dispatching nurses to homes or workplaces who can provide “physical assessments, vaccines or common [clinical laboratory] tests.”
“Amazon is a company that is experimenting a lot with a variety of opportunities in healthcare,” Glen Tullman (above), Executive Chairman of Livongo, a healthcare company specializing in treating diabetes, and an Amazon partner company, told CNBC. “It’s one to watch.” (Photo copyright: CNBC.)
However, the US federal Department of Veterans Affairs (VA) also is becoming a major player in the mHealth space with the development of its own mobile app—VA Launchpad—which serves as a portal to a range of medical services.
Veterans can access five categories of apps that allow them to manage their health, communicate with their healthcare team, share health information, and use mental health and personal improvement tools.
“The VA was an early adopter of digital health tools and remains a leader within US healthcare in leveraging technology to enhance patient engagement,” Neil C. Evans, MD (above), Chief Officer in the VA Office of Connected Care, told Healthcare IT News. “These digital tools are allowing veterans to more actively understand their health data, to better communicate with VA clinical teams, and to engage more productively as they navigate their individual health journeys,” Evans added. (Photo copyright: Department of Veterans’ Affairs.)
mHealthIntelligence reported that mobile health tools also are enabling first responders to improve emergency patient care. At King’s Daughters Medical Center in Brookhaven, Miss., emergency medical technicians (EMTs) are using a group of mHealth apps from DrFirst called Backline to gain real-time access to patients’ HIPAA-compliant medication histories, share clinical data, and gain critical information about patients prior to arriving on the scene.
Using Backline, EMTs can scan the barcode on a patient’s driver’s license to access six months’ worth of medication history.
“In the past, we could only get information from [patients] who are awake or are willing to give us that information,” Lee Robbins, Director of Emergency Medical Services at King’s Daughters Medical Center in Brookhaven, Miss., told mHealthIntelligence. “Knowing this information gives us a much better chance at a good outcome.”
Smartphone App Detects Opioid Overdose
The opioid crisis remains one of the US’ greatest health challenges. The federal Centers for Disease Control and Prevention (CDC) reported 47,600 opioid-related deaths in 2017, and the problem has only gotten worse since then.
To curtail these tragic deaths, University of Washington (UW) researchers developed a smartphone app called Second Chance, that they believe can save lives by quickly diagnosing when an opioid overdose has occurred.
The app uses sonar to monitor an opioid user’s breathing rate and, according to a UW press release, can detect overdose-related symptoms about 90% of the time from up to three feet away. The app then contacts the user’s healthcare provider or emergency services.
The UW researchers are applying for US Food and Drug Administration (FDA) clearance. They published their findings in the journal Science Translational Medicine.
While Demand for mHealth Apps Grows, Concern over Privacy and Security also Increases
According to mobile data and analytics company App Annie, global downloads of medical apps grew to more than 400 million in 2018, up 15% from two years earlier.
“As with mobile banking, consumers are showing they trust mobile apps with their most sensitive information and are willing to leverage them to replace tasks traditionally fulfilled in-person, such as going into a bank branch or, in the case of medical apps, to a doctor’s office,” App Annie’s website states.
However, the proliferation of mHealth apps has raised privacy and safety concerns as well. While the FDA does regulate some mobile health software functions, it does not ensure an mHealth app’s accuracy or reliability.
Fierce Healthcarereported that federal lawmakers are worried veterans who use the VA’s 47 mHealth apps could find their sensitive healthcare information shared or sold by third-party companies. In fiscal year 2018, veterans participated in more than one million video telehealth visits, a VA press release reported.
US Rep. Susie Lee, D-Nevada, Chairperson of the House Veterans’ Affairs Subcommittee on Technology Modernization, told Fierce Healthcare, “As we assess the data landscape at the VA and the larger health IT space, we need to look at where protections exist or don’t exist and whether we need more guardrails.”
What does all this mean for clinical laboratories? Well, lab managers will want to keep an eye on the growing demand from consumers who want direct access to laboratory test data and appointment scheduling through mHealth apps. And, also be aware of HIPAA regulations concerning the sharing of that information.
