Roundup: 12 healthcare algorithms cleared by the FDA

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As AI cements its role in healthcare, more and more intelligent software offerings are pursuing 510(k) and De Novo approvals.

Every day sees strides across the field of artificial intelligence, and healthcare is just one of the many industries looking to smart automation as a means to reduce burden and improve results. The last year in particular has brought a wealth of new healthcare focused software tools to the forefront, and as such has ignited debate

“FDA is lagging in the production of guidance to explain its approach for these newer products. This is a problem, because Commissioner Gottlieb himself in a blog post noted well over a year ago that individual decision-making by FDA is not enough for digital therapeutics to thrive,” Bradley Merrill Thompson, a lawyer at Epstein Becker & Green who also leads CDS Coalition, an industry group, wrote in an email on the subject.

“Industry has been asking since 2015 for better guidance on the use of software-based algorithms in connection with drug administration. The commissioner, starting in April 2018, has been promising new guidance focused on the use of software with drugs, and in fact reiterated that promise only a couple weeks ago. But the concern is that the new guidance may not be focused on the issues of greatest concern to industry. We shall have to wait to see.”

Thompson also noted that while the agency is relying on its 510(k) regulatory pathways in the meantime, the heterogeneity of these nontraditional tools has resulted in an ever-growing number of De Novo clearances and device classifications. Below, MobiHealthNews has collection of 12 algorithm clearances — five De Novo, seven 510(k) — spanning the breadth of digital health.’s Contact is a clinical decision support (CDS) tool that analyzes CT results and highlights cases that may have experienced a stroke. Approved through the agency’s De Novo premarket review pathway

IDx’s IDx-DR is an AI software system for the autonomous detection of diabetic retinopathy in adults who have diabetes. The algorithm analyzes images taken with the Topcon NW400 retinal camera and uploaded to a cloud server. Within minutes the software provides doctors with a binary result, either indicating that more than mild diabetic retinopathy is present and that the patient should be referred to an eye care professional, or that the screen is negative and should be repeated in 12 months. Approved in April, the software is notable in that it was the first AI-based diagnostic system to be authorized by the FDA for commercialization in the US that can provide a screening decision without the need for clinician interpretation.

MaxQ AI’s Accipio Ix in an AI workflow tool designed to help clinicians prioritize adults patients likely presenting with acute intracranial hemorrhage. Cleared just last week, the algorithm automatically retrieves and processes non-contrast CT images to provide a case-level indicator, which is used to triage cases most in need of expert review and diagnosis. MaxQ AI CEO and Chairman Gene Saragnese noted in an announcement that it is the first in a planned wider ecosystem of similar apps.

Imagen’s OsteoDetect uses an AI algorithm to scan X-ray images for a common type of wrist bone fracture, known as distal radius fracture. The software can be fed images of adult wrists in the posterior-anterior and medial-lateral position, and using these highlights regions with potential fracture. OsteoDetect — which received De Novo clearance in May — is intended for use by primary, emergency, urgent and specialty care practitioners alike, but should be accompanied by a standard clinical review.

The cloud-based DreaMed Advisor Pro is a diabetes treatment decision support product that analyzes data from continuous glucose monitors, insulin pumps and self-monitoring to determine an insulin delivery recommendation. Through an event-based learning process, the software incorporates a number of components into its recommendations, including basal rate, carbohydrate ratio and correction factor. Dosage recommendations are delivered directly to the monitoring clinician, who can push the adjustment to a patient’s diabetes management devices with the click of a button. FDA granted DreaMed’s algorithm a De Novo approval in June.

AliveCor received 510(k) clearance back in 2014 for the AF algorithm

Less than a year later, AliveCor also received clearance for two more related algorithms: the Normal Detector, which assures patients that their personal ECG is free of abnormalities; and the Interference Detector, a tool that automatically detects whether interference could be compromising their ECG test.

On the subject of atrial fibrillation, Apple made waves a few months back when it announced that its latest ECG-equipped smartwatch would also come with an algorithm to detect irregular heart rates. The software, which required the consumer tech company to seek a De Novo marketing approval, can monitor the user’s heart rate behind its various other functions, and alerts the wearer when it notices cause for concern.

Bay Labs’ EchoMD AutoEF software assists cardiologists by using an algorithm to automatically review relevant digital video clips collected from an echocardiography study, rates their quality and then selects the best to calculate ejection fraction, a key measure of cardiac function. Of note, the software can be integrated into a cardiologist’s routine diagnostic workflow to better assist decision making.

The Coronary Calcium Scoring algorithm from Zebra Medical Vision offers a coronary artery calcification score from a patient’s ECG-gated CT scan. Clinicians can use this score to flag patients at high risk of cardiovascular disease sooner, thereby allowing for quicker and more effective care. The July 510(k) clearance was the first for the Israel-based company, which also holds a number of algorithm clearances in the EU.

In February, medical imaging software company Arterys Inc. touted 510(k) clearance for its Artyrys Oncology AI suite, a web-based platform that helps clinicians analyze ARIs and CT scans for signs of potential liver and lung cancer. The tool uses deep learning algorithms to expedite interpretation of these images.