Seeking a Revolution in Clinical Care: One firm shares its experience in applying AI and machine learning to medical devices.

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Author: Aaron McCabe
Date: Jan-Feb 2021
From: Medical Product Outsourcing(Vol. 19, Issue 1)
Publisher: Rodman Publishing
Document Type: Article
Length: 2,470 words
Lexile Measure: 1450L

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New computational techniques stemming out of the field of data science, including machine learning, computer vision, and artificial intelligence (AI) techniques (collectively, here--ML), stand poised to revolutionize clinical care. These include the decision making leading up to and surrounding clinical interventions, the decisions to use medical devices, and the ongoing therapies delivered via medical devices. While these techniques are powerful, becoming (or are, depending on industry) mainstream, and generally exciting, they are not without risk and can incur quite considerable time and capital costs to develop and deploy. In fact, as others have noted, the vast majority of measurables (cost, time, code, etc.) developed and incurred while developing any ML system are not the ML-algorithm itself. (1)

There's been a profound uptick in medical device companies making or wanting to make use of these ML techniques to meet unmet clinical needs and provide better therapies as these techniques may provide better and more adaptive insights than traditional methods. Meeting the need requires a focus on generalizing and standardizing the approach to developing ML-enabled technologies in the medical device space. By focusing on process, generalization, and re-use, it's possible to reduce hidden time and capital costs in the development of image-based ML-algorithms.

Background

As an example, there have been many advancements in the neurointensive care pathway in recent decades, and both the neurosurgical and neurocritical care specialties are rapidly evolving. In our experience, new interventions that are developed are often surrounded by questions about their optimal usage: when? in whom? for how long? Therefore, coupling interventions with decision support leads to better outcomes for the patient (Figure 1). In this article, we examine a tool currently under development to support decisions surrounding the use of Minnetronix Medical's newly FDA-cleared expandable port for deep brain access, called the MindsEye Expandable Port (Figure 2). This port, and others like it, are used to access a hematoma in a patient that has experienced an intracerebral hemorrhage (ICH), allowing for evacuation of the bleed. Accordingly, Minnetronix sought to develop a decision support tool that provides additional insight into the critical choice of surgical intervention (with minimally invasive tools like the MindsEye port) versus medical management in the ICH population.

The treatment paradigm of a patient with ICH is a subject of numerous recent and ongoing clinical trials. (2-4) The current standard of care ranges from aggressive evacuation of the blood to "watch and wait," depending on size, location, severity, and other still-debated factors. Many of these factors may be clarified by careful, quantitative examination of the progression of the patient's status on CT imagery. Unfortunately, this is time-consuming and not currently the standard of care through radiology. While there are manual tools to help evaluate some of the metrics of the CT image, there are, to date, no automated tools that cover the breadth of measures required.

With this in mind, Minnetronix sought to create an automated CT processing algorithm (CT segmentation) that calculates relevant anatomic and volumetric factors over time to assist the neurosurgeon...

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Gale Document Number: GALE|A652003862