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Digital Health

Artificial Intelligence and Machine Learning (AI/ML) in Medical Devices

Do you know that FDA already approved 521 Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices?  

PAPNET. The failure of the pioneering AI/ML-enabled test.

Five hundred apps may not surprise you in January 2023, given the noise around Open AI and its GPTChat. However, the first such device, PAPNET Testing System, was approved over 28 years ago. In 1995, the year of Johnny Mnemonic and Ghost in the Shell movies!

Fig.1 Neural net-based (PAPNET, Neuromedical Systems, Suffern, NY) display of squamous cells (Papanicolaou stain) from a balloon smear showing effects of radiotherapy. Marked cell enlargement and vacuolization of cytoplasm are easily recognized.
Source: Koss, Leopold & Morgenstern, Nora & Tahir-Kheli, Naveed & Suhrland, Mark & Schreiber, Katie & Greenebaum, Ellen. (1998). Evaluation of Esophageal Cytology Using a Neural Net–Based Interactive Scanning System (the PAPNET System): Its Possible Role in Screening for Esophageal and Gastric Carcinoma. American journal of clinical pathology. 109. 549-57. 10.1093/ajcp/109.5.549.

PAPNET was using a neural network to analyze and interpret cytology from Pap smears. While this early system generated a lot of interest and Google Scholar lists 217 peer-reviewed articles on PAPNET results, the business side of it was not that great. The cost-effectiveness of the system in comparison to manual screening by cytotechnician was not there. Neuromedical Systems Inc, the company behind PAPNET went bust in 1999, and now its intellectual property is a part of Becton, Dickinson and Company portfolio.

List of FDA-approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices

If you look at the list of approved AI/ML-enabled medical devices, you will notice that the vast majority (392 medical devices, 75% of the whole) are for Radiology. Cardiovascular (57,11%), Hematology (15, 3%), and Neurology (14, 3%) are the remaining three significant categories.

Fig. 2: Split of approved AI/ML-enabled medical devices by Specialty Panel.
Source: FDA.gov, graphics by disrupting.healthcare

There are only 15 companies that have more than five AI/ML-enabled medical devices approved. Five of those companies are actually subsidiaries of GE, which in total owns 42 AI/ML-enabled medical devices. Then there is Siemens with 27 devices, Canon with 15, Aidoc Medical with 13, and Zebra Medical Vision with 9 devices. Philips, which also submitted its devices via different subsidiaries has in total 10 approved AI-enabled devices

Table 1. Companies with over 5 approved AI/ML-enabled medical devices
CompanyAI/ML-enabled medical devices
GE Medical Systems42
Siemens Healthineers27
Canon17
Aidoc Medical13
Philips Healthcare10
Zebra Medical Vision9
Quantib BV6
Arterys Inc.5
Clarius Mobile Health Corp.5
HeartFlow, Inc.5
RaySearch Laboratories AB5
Viz.ai, Inc.5
Source: FDA.gov, disrupting.healthcare

The future of AI-enabled and data-driven MedTech

The market for Artificial Intelligence / Machine Learning – enabled Medical Devices seems to be poised for growth. At a recent HLTH 2022 conference in Las Vegas, Michelle Wu, CEO of NyquistData, a company offering an AI-supported intelligence platform dedicated to MedTech companies discusses the advantages of using AI to unlock the potential of unstructured data from medical devices.

Leveraging Artificial Intelligence, Data to Improve Medical Devices
Source: Xtelligent Healthcare Media

Cleerly. An example of an AI-enabled medical device for a heart-attack-free future.

A good example of upcoming Artificial Intelligence / Machine Learning – enabled Medical Device may be Cleerly. The startup has raised $279 million from investors including Fidelity, T. Rowe Price, Novartis and Peter Thiel.

Founded by cardiologist James Min, former professor at Weill Cornell Medical College and director of the Dalio Institute of Cardiovascular Imaging at New York-Presbyterian, Cleerly uses AI to improve diagnostics cutting down on the time it takes to flag patients at risk.

Its proprietary AI algorithms analyze CCTA images to generate a 3D model of patients’ coronary arteries, identify their lumen (the cavity or channel within a tube or tubular organ such as a blood vessel) and vessel walls, locate and quantify stenoses, as well as identify, quantify and categorize plaque.

Source: Cleerly
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Digital Health

Deprexis DiGA approved! 11th Digital Therapeutics (DTx) reimbursed in Germany

Deprexis – Digital therapeutics (DTx) for depression has received DiGA fast-track approval for DTx prescription and reimbursement in Germany.

The innovative DiGA process allows for fast-track approval of digital therapeutics and is the first such program in the world. It was created by the 2019 Digital Healthcare Act and allows apps to be prescribed by doctors while costs will be reimbursed through German statutory health insurance. 

The federal regulator, BfArM, manages DiGA. To get through DiGA, there are certain conditions:

  • Safety and Suitability for Use confirmed by CE certification as a medical product in the lowest-risk classes
  • Data Protection Conformity to data protection legislation (EU-wide GDPR and German Federal Data Protection Act (BDSG)) 
  • Information security Assessment is based on the recommendations of the German Federal Office for Information Security (BSI) and specific parts of  IT-Grundschutz (ITbasic data protection) catalogs designated for healthcare apps. 
  • Interoperability Related to German central IT standards directory available via online platform vesta, managed by gematik
  • Availability of preliminary data on the health benefits provided. Data must show that patient-relevant endpoints, in particular morbidity, mortality, or quality of life, are positively influenced.

Check out the full guide for DiGA here.

Results of DTx DiGA assessment as of June 2022. Source: BfArM.

At the time of writing this, there were 59 applications for DiGA listing, 40 for provisional listing, and 19 for the final listing. So far BfArM has approved 11 applications and rejected one. 25 applications have been withdrawn. In theory, the full approval process should take three months.

Deprexis, the 11th DiGA-approved application is interesting on its own. The manufacturer of the app is GAIA Group, an offspring of Airbus which builds its products on a proprietary AI platform called broca.

Deprexis, Digital Therapeutics (DTx) for depression. Source: GAIA Group AG


The focus here is clearly not on UX, but on medical benefits. Deprexis may not have the nicest UX, but is a proper DTx providing a three-month-long highly individualized Cognitive Behavioral Therapy support program for patients with depression. Application is able to perform a dialogue with the patient, learning from the input on the way. It contains 10 content modules and is available online via desktop and mobile app interface. 

Deprexis is backed by clinical data from at least nine studies, one of which had a sample of 3,800 patients, which does not sound much in pharmaceuticals, but it is a lot in DTx. While in Germany it just received reimbursement, in the US the price for treatment is $400 one-time payment, or $540 in three monthly installments of $135 each.

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Digital Health

Big Data, AI and Coronavirus COVID-19 (NCov-2019)

Coronavirus COVID-19 (NCov-2019) has tested some of the digital health capabilities such as AI-based predicitive models and real-time big data visualization. As a positive side effect, it has also allowed public to learn about epidemiology via video games.

 

AI-based predictive models caught COVID-19 faster than us

According to the news reports, two AI-based and one human volunteer-based warning systems were first to alert humanity about the threat coming from Wuhan.

HealthMap Project Website

The first to react was the automated HealthMap system at Boston Children’s Hospital, which scans online news and social media reports for signals of spreading disease. Its warning was very quick and accurate (pneumonia cases in Wuhan) – raised at 11:12PM local time on December 30, but it did not assign significance high enough to the message.

The second report came from a human. Marjorie Pollack from the Program for Monitoring Emerging Diseases (ProMed)  has based on similar social media reports received about 4 hours before the HealthMap warning. ProMed team’s analysis was more detailed than the first warning from AI but came about half an hour later.

BlueDot Explorer (screenshot from the website)

The third and most publicized report came from another AI-based model called BlueDot. BlueDot first became aware of the pneumonia cases in Wuhan on December 31st, and in addition to notifying their clients and government stakeholders directly, they publicly released their findings in the Journal of Travel Medicine on January 14th. While it was not truly the fastest, it is worth hearing how Kamran Khan, a Canadian MD, and founder of BlueDot explains the process behind.

Big Data Visualization to track Coronavirus COVID-19 (NCov-2019)

Dashboards showing the number of infected people, geographical spread and trends of the disease are useful to HCPs but also journalists and the public. This use case, although unfortunate, shows how important it is to be able to see and not only read data.

coronavirus covid19 dashboard
Screenshot of the COVID-19 dashboard by Johns Hopkins CSSE

The first and most known dashboard came from the team at Johns Hopkins University. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It has been accompanied by an article in The Lancet.

Currently, there are multiple dashboards showing diverse aspects of the epidemic of COVID-19. The list of dashboards with working links can be found at ESRI website.

Coronavirus virally spreads a game for people want to know

Due to the virus business slows down. Except for Ndemic Creations, a studio that in 2012 developed Plague Inc. A game that simulates a viral epidemic.

Plague Inc. is a game, but it is based on science and realistically shows the spread of viral infections amongst the human population. In the game, the player is supposed to infect all humans before the cure is available. It is so successful in teaching about epidemiology, that it has been endorsed by the CDC. During the COVID-19 outbreak, it has reached the top of charts on the Apple Store. According to its developers, similar peaks in popularity have accompanied the Ebola outbreak in Africa in 2014-2016.