Artificial intelligence of arterial Doppler waveforms to predict major adverse outcomes among patients with diabetes mellitus.

Patients with diabetes mellitus (DM) are at increased risk for peripheral artery disease (PAD) and its complications. Arterial calcification and non-compressibility may limit test interpretation in this population. Developing tools capable of identifying PAD and predicting major adverse cardiac (MACE) and limb (MALE) outcomes among diabetic patients would be clinically useful. Deep neural network analysis of resting Doppler arterial waveforms was used to detect PAD among diabetic patients and to identify those at greatest risk for major adverse outcome events.

Source: Journal of Vascular Surgery - February 26, 2024 Category: Surgery Authors: Robert D. McBane, Dennis H. Murphree, David Liedl, Francisco Lopez-Jimenez, Adelaide Arruda-Olson, Christopher G. Scott, Naresh Prodduturi, Steve E. Nowakowski, Thom W. Rooke, Ana I. Casanegra, Waldemar E. Wysokinski, Damon E. Houghton, Kalpana Muthusamy, Source Type: research