Machine Learning to Diagnose Complications of Diabetes
Agatha F. Scheideman, M. Shao, Henry Zelada, Jorge Cuadros, Joshua Foreman, Pinaki Sarder, Cindy Ho, Niels Ejskjær, Jesper Fleischer, Simon Lebech Cichosz, David G. Armstrong, Nestoras Mathioudakis, Tao Wang, Yih Chung Tham, David C. Klonoff
Abstract
Machine learning (ML) uses computer systems to develop statistical algorithms and statistical models that can draw inferences from demographic data, structured behavioral data, continuous glucose monitor (CGM) tracings, laboratory data, cardiovascular and neurological physiology measurements, and images from a variety of sources. ML is becoming increasingly used to diagnose complications of diabetes based on these types of datasets. In this article, we review the current status, barriers to progress, and future prospects for using ML to diagnose seven complications of diabetes, including five traditional complications, one set of other systemic complications, and one prediction that can result in favorable or unfavorable outcomes. The complications include (1) diabetic retinopathy, (2) diabetic nephropathy, (3) peripheral neuropathy, (4) autonomic neuropathy, (5) diabetic foot ulcers, and (6) other systemic complications. The prediction is for outcomes in hospitalized patients with diabetes. ML for these purposes is in its infancy, as evidenced by only a limited number of products having received regulatory clearance at this time. However, as multicenter reference datasets become available, it will become possible to train algorithms on increasingly larger and more complex datasets and patterns so that diagnoses and predictions will become increasingly accurate. The use of novel choices of images and imaging technologies will contribute to progress in this field. ML is poised to become a widely used tool for the diagnosis of complications and predictions of outcomes and glycemia in people with diabetes.