{"id":93186,"date":"2021-05-20T13:53:21","date_gmt":"2021-05-20T18:53:21","guid":{"rendered":"https:\/\/uwm.edu\/news\/?p=93186"},"modified":"2023-05-10T16:02:22","modified_gmt":"2023-05-10T21:02:22","slug":"uwm-scientists-find-an-artificial-intelligence-solution-for-stubborn-wounds","status":"publish","type":"post","link":"https:\/\/uwm.edu\/news\/uwm-scientists-find-an-artificial-intelligence-solution-for-stubborn-wounds\/","title":{"rendered":"UWM scientists find an artificial intelligence solution for stubborn wounds"},"content":{"rendered":"

Health professionals treating nonhealing wounds rely heavily on comparing photos taken at each patient visit to track the healing trajectory. But 2D pictures offer limited information about the wound, and care is interrupted when patients skip or cannot make regular appointments.<\/p>\n

\u00a0<\/em>\u201cWhen I ask colleagues who work with these patients, \u2018What are the tools that you use to characterize the wound?\u2019 they say they use manual methods to collect wound data using a ruler and Q-tip,\u201d said Sandeep Gopalakrishnan, an assistant professor in the UWM College of Nursing.<\/p>\n

Gopalakrishnan and Zeyun Yu in the College of Engineering & Applied Science teamed up to develop a smartphone-based digital platform that could help clinicians improve treatment and accelerate healing.<\/p>\n

First, they created an app that patients and caregivers can use to capture wound photos themselves at home. Algorithms using artificial intelligence (AI) could then process the images and provide clinicians with accurate information on the healing characteristics.<\/p>\n

I-Corps program helps<\/h3>\n

\u00a0<\/em>To determine whether to commercialize their app, Gopalakrishnan and Yu participated in the UWM-administered I-Corps Program, which teaches academic researchers how to turn discoveries in the lab into products and startups. Through I-Corps, which is supported by the National Science Foundation, the two interviewed scores of potential users of the technology to zero in on clinicians\u2019 top needs.<\/p>\n

In the process, they met Milwaukee physician Jeffrey Niezgoda, a recognized wound care expert at AZH Wound and Vascular Centers. Niezgoda suggested ways to expand their initial business idea.<\/p>\n

Photos contain a rich source of data. If you have enough of them, machine-learning algorithms running in the cloud could help health care providers precisely monitor a wound\u2019s status.<\/p>\n

\u201cNot all clinicians identify the types of wounds correctly to begin with, and the kind of interventions that are needed are all very different,\u201d said Yu, a professor of computer science and biomedical engineering. \u201cSo, our platform ideally will be able to process images to classify and segment wounds, while at the same time, provide a tool that gives clinicians predictability of the healing potential.\u201d<\/p>\n

Since completing the I-Corps training last year, the three formed a startup company called MegaPerceptron<\/a> to take the system to market.<\/p>\n

Training the program<\/h3>\n

Yu used a sizable set of different wound images from Niezgoda\u2019s practice to \u201ctrain\u201d the AI program, which supports the prediction and analysis functions. Machine-learning algorithms are those that can detect patterns and improve their predictive performance with increasing amounts of data.<\/p>\n

\u201cFor the wound-classification problem, we have used thousands of wound images,\u201d Yu said. \u201cWith this amount of data to train the AI algorithm, we are able to classify the wound types with more than 90% accuracy.\u201d<\/p>\n

Contributing even simple information makes a difference in accuracy, Gopalakrishnan said. \u201cThere are kinds of wounds that are found only on certain parts of the body,\u201d he said. \u201cOnce you\u2019ve diagnosed the type, then that drives the algorithms. That makes our platform robust.\u201d<\/p>\n

Integrating AI and wound care is still a fledgling field, the researchers said, but since most people today have smartphones that take high-quality photos, the timing for this product is right.<\/p>\n

Potential for other uses<\/h3>\n

Their framework also could be used in other medical areas where treatment is monitored with images, such as dermatology, radiology and ophthalmology.<\/p>\n

\u201cIt also has the potential to lower the cost of treatment because patient progress could be made with fewer doctor visits,\u201d Niezgoda said. \u201cThere are many people who are underserved in the wound care area, not only here in the United States, but globally.\u201d<\/p>\n

Other digital tools for wound healing are being developed, the researchers said, but no other platform is as comprehensive as theirs. The next step for the three-month-old startup is to further improve the software for the phone-based app with funding they received last year from the UWM Research Foundation.<\/p>\n

\u00a0<\/em><\/strong>They credit the joint research with their success so far.<\/p>\n

\u201cCollaboration between the academic world and the clinical world is really what this project is all about. This is called \u2018translational medicine,\u2019 and it\u2019s the foundation of our company,\u201d Niezgoda said.<\/p>\n","protected":false},"excerpt":{"rendered":"

Health professionals treating hard-to-heal wounds have long been saddled with inadequate tools. 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