{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T04:41:19Z","timestamp":1792384879061,"version":"4.4.0"},"reference-count":36,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100007771","name":"Medical University of Silesia in Katowice","doi-asserted-by":"publisher","award":["BNW\u20102\u2010046\/N\/4\/K"],"award-info":[{"award-number":["BNW\u20102\u2010046\/N\/4\/K"]}],"id":[{"id":"10.13039\/100007771","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To investigate whether artificial intelligence (AI) models trained on standard 12\u2010lead electrocardiograms (ECG) can identify symptom\u2010defined diabetic peripheral neuropathy (DPN) as assessed by the Michigan Neuropathy Screening Instrument Questionnaire (MNSI\u2010Q).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials<\/jats:title>\n                    <jats:p>This was an observational study of people with diabetes enrolled in the Silesia\u2010Diabetes Heart Project. DPN was assessed using the MNSI\u2010Q. We used an original questionnaire cut\u2010off score of \u2265\u20097 and revised cut\u2010off score of \u2265\u20094 to diagnose DPN and classified DPN as highly symptomatic and moderately symptomatic accordingly. Feature extraction from 10\u2010s raw ECG recordings utilised algorithms to identify recurring signal segments (motifs) and anomalies (discords). These features were used to train XGBoost, Support Vector Machine (SVM) and Ridge classifiers to differentiate between DPN\u2010positive and DPN\u2010negative people.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 640 participants (mean age 54\u2009\u00b1\u200917; 52% female) were included. Of these, 95 (15%) had highly symptomatic DPN (MNSI\u2010Q\u2009\u2265\u20097) and 281 (44%) had moderately symptomatic DPN (MNSI\u2010Q\u2009\u2265\u20094). For the highly symptomatic DPN, the XGBoost classifier utilizing a combination of motifs and discords demonstrated the highest performance, achieving an area under the receiver\u2010operating characteristic curve (AUC) of 0.89 (95% CI 0.88\u20130.91), an accuracy of 88.5% and a sensitivity of 93.4%. The model showed substantially lower predictive capability when tested against the broader screening threshold of MNSI \u2265\u20094 (AUC 0.64).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI analysis of the standard ECG demonstrated a strong association with the presence of symptom\u2010defined peripheral neuropathy but lacked sensitivity for milder presentation. With further validation, this method could serve as an accessible, supplementary screening aid to help identify high\u2010risk patients during routine cardiovascular assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.71240","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T02:34:31Z","timestamp":1787193271000},"page":"10330-10342","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Analysis of Standard 12\u2010Lead Electrocardiograms for Screening of Symptomatic Diabetic Peripheral Neuropathy"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2938-4541","authenticated-orcid":false,"given":"Pawe\u0142","family":"Ignacy","sequence":"first","affiliation":[{"name":"Doctoral School, Department of Internal Medicine, Diabetology and Nephrology Faculty of Medical Sciences in Zabrze, Medical University of Silesia  Katowice Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanadi","family":"Aldosari","sequence":"additional","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital  Liverpool UK"},{"name":"Department of Artificial Intelligence and Data Science College of Computer Science and Engineering, Taibah University  Madinah Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krzysztof","family":"Irlik","sequence":"additional","affiliation":[{"name":"Doctoral School, Department of Internal Medicine, Diabetology and Nephrology Faculty of Medical Sciences in Zabrze, Medical University of Silesia  Katowice Poland"},{"name":"Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital  Liverpool UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martyna","family":"Gut\u2010Misiaga","sequence":"additional","affiliation":[{"name":"Student's Scientific Association at the Department of Internal Medicine, Diabetology and Nephrology Faculty of Medical Sciences in Zabrze, Medical University of Silesia  Katowice Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleksandra","family":"Gil","sequence":"additional","affiliation":[{"name":"Student's Scientific Association at the Department of Internal Medicine, Diabetology and Nephrology Faculty of Medical Sciences in Zabrze, Medical University of Silesia  Katowice Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Kwiendacz","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze Medical University of Silesia  Katowice Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janusz","family":"Gumprecht","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze Medical University of Silesia  Katowice Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7566-1626","authenticated-orcid":false,"given":"Gregory Y. 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