Artificial intelligence for the evaluation of peripheral artery disease using arterial Doppler waveforms to predict abnormal ankle-brachial index. (August 2022)

• Record Type: Journal Article
• Authors: McBane, Robert D; Murphree, Dennis H; Liedl, David; Lopez-Jimenez, Francisco; Attia, Itzhak Zachi; Arruda-Olson, Adelaide; Scott, Christopher G; Prodduturi, Naresh; Nowakowski, Steve E; Rooke, Thom W; Casanegra, Ana I; Wysokinski, Waldemar E; Swanson, Keith E; Houghton, Damon E; Bjarnason, Haraldur; Wennberg, Paul W
• Is Part Of: Vascular medicine. Volume 27:Number 4(2022)
• Page Start: 333
• Page End: 342
• Publication Date: 2022-08
• Subjects: ankle-brachial index (ABI) -- artificial intelligence -- Doppler waveforms -- exercise testing -- peripheral artery disease (PAD); Blood-vessels -- Diseases -- Periodicals; Peripheral vascular diseases -- Periodicals; Vascular Diseases -- Periodicals; Vaisseaux sanguins -- Maladies -- Périodiques; Maladies vasculaires périphériques -- Périodiques; 616.13
• DOI: 10.1177/1358863X221094082
• Languages: English
• ISSNs: 1358-863X

Abstract

Background: Patients with peripheral artery disease (PAD) are at increased risk for major adverse limb and cardiac events including mortality. Developing screening tools capable of accurate PAD identification is a necessary first step for strategies of adverse outcome prevention. This study aimed to determine whether machine analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with PAD. Methods: Consecutive patients (4/8/2015 – 12/31/2020) undergoing rest and postexercise ankle–brachial index (ABI) testing were included. Patients were randomly allocated to training, validation, and testing subsets (70%/15%/15%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict normal (> 0.9) or PAD (⩽ 0.9) using rest and postexercise ABI. A separate dataset of 151 patients who underwent testing during a period after the model had been created and validated (1/1/2021 – 3/31/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance. Results: Among 11, 748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 ± 12 years) and 1491 without PAD (64 ± 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92–0.96), sensitivity 0.83, specificity 0.88, accuracy 0.85, and positive predictive value (PPV) 0.90. Results were similar for the validation dataset: AUC 0.94 (CI = 0.91–0.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison). Conclusion: An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.