{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T16:07:59Z","timestamp":1788883679276,"version":"build-2803163510"},"reference-count":42,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:00:00Z","timestamp":1652140800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Vasc Med"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Consecutive patients (4\/8\/2015 \u2013 12\/31\/2020) undergoing rest and postexercise ankle\u2013brachial 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 (&gt; 0.9) or PAD (\u2a7d 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 \u2013 3\/31\/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Among 11,748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 \u00b1 12 years) and 1491 without PAD (64 \u00b1 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92\u20130.96), 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\u20130.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/1358863x221094082","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T08:39:10Z","timestamp":1652171950000},"page":"333-342","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":16,"title":["Artificial intelligence for the evaluation of peripheral artery disease using arterial Doppler waveforms to predict abnormal ankle-brachial index"],"prefix":"10.1177","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8727-8029","authenticated-orcid":false,"suffix":"II","given":"Robert D","family":"McBane","sequence":"first","affiliation":[{"name":"Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA"},{"name":"Cardiovascular Department, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dennis H","family":"Murphree","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Liedl","sequence":"additional","affiliation":[{"name":"Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"Lopez-Jimenez","sequence":"additional","affiliation":[{"name":"Cardiovascular Department, Mayo Clinic, Rochester, MN, USA"},{"name":"Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itzhak Zachi","family":"Attia","sequence":"additional","affiliation":[{"name":"Cardiovascular Department, Mayo Clinic, Rochester, MN, USA"},{"name":"Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adelaide","family":"Arruda-Olson","sequence":"additional","affiliation":[{"name":"Cardiovascular Department, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher G","family":"Scott","sequence":"additional","affiliation":[{"name":"Clinical Trials and Biostatics, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naresh","family":"Prodduturi","sequence":"additional","affiliation":[{"name":"Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve E","family":"Nowakowski","sequence":"additional","affiliation":[{"name":"Division of Engineering, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thom W","family":"Rooke","sequence":"additional","affiliation":[{"name":"Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA"},{"name":"Cardiovascular Department, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6114-4284","authenticated-orcid":false,"given":"Ana I","family":"Casanegra","sequence":"additional","affiliation":[{"name":"Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA"},{"name":"Cardiovascular Department, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waldemar E","family":"Wysokinski","sequence":"additional","affiliation":[{"name":"Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA"},{"name":"Cardiovascular Department, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keith E","family":"Swanson","sequence":"additional","affiliation":[{"name":"Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA"},{"name":"Cardiovascular Department, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6065-9523","authenticated-orcid":false,"given":"Damon E","family":"Houghton","sequence":"additional","affiliation":[{"name":"Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA"},{"name":"Cardiovascular Department, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haraldur","family":"Bjarnason","sequence":"additional","affiliation":[{"name":"Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA"},{"name":"Vascular and Interventional Radiology, Mayo Clinic, Rochester, MN, 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