{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T13:43:50Z","timestamp":1792417430111,"version":"4.4.0"},"reference-count":24,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Objectives:<\/jats:title>\n                    <jats:p>The facial nerve, or seventh cranial nerve, with its complex course and association with multiple pathologies, remains a critical area of study. Bell\u2019s palsy, an idiopathic condition leading to unilateral facial weakness, is frequently diagnosed through magnetic resonance imaging (MRI), which can reveal inflammation or pathology within the facial nerve. However, physiological enhancement can complicate diagnosis, particularly in healthy individuals. This study aimed to assess facial nerve enhancement in Bell\u2019s palsy patients using machine learning algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We retrospectively analyzed 306 MR images from subjects, including 156 Bell\u2019s palsy patients and 150 controls, and trained a DenseNet-based deep learning model to identify pathological enhancement with an 80% accuracy (area under the curve (AUC) 87%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Our model achieved an accuracy of 80% and an AUC of 87% as compared to neuroradiologist\u2019s decision, demonstrating its potential to aid clinicians in diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Future studies with external validation and larger datasets may further enhance the clinical applicability of AI-based assessment tools for facial nerve pathologies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/aian.aian_45_25","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T07:00:15Z","timestamp":1778655615000},"page":"399-405","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Detection of Facial Nerve Enhancement in Bell\u2019s Palsy"],"prefix":"10.4103","volume":"29","author":[{"given":"Habip Eser","family":"Akkaya","sequence":"first","affiliation":[{"name":"Department of Radiology, Ankara Training and Research Hospital, Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00d6nder","family":"Polat","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics 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