{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T06:04:45Z","timestamp":1784873085008,"version":"3.55.0"},"reference-count":20,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Surgical Oncology"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Previous work demonstrated that abnormal versus normal parathyroid glands (PGs) exhibit different patterns of autofluorescence, with former appearing darker and more heterogenous. Our objective was to develop a visual artificial intelligence model using intraoperative autofluorescence signals to predict whether a PG is abnormal (hypersecreting and\/or hypercellular) or normal before excision during surgical exploration for primary hyperparathyroidism.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 906 intraoperative parathyroid autofluorescence images of 303 patients undergoing parathyroidectomy\/thyroidectomy were used to develop model. Autofluorescence image of each PG was uploaded into the visual artificial intelligence platform as abnormal or normal. For deep learning, randomly chosen 80% of data was used for training, 10% for testing, 10% for validation. The area under the receiver operating characteristic (AUROC), area under the precision\u2010recall curve (AUPRC), recall (sensitivity), and precision (positive predictive value) of the model were calculated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>AUROC and AUPRC of the model to predict normal and abnormal PGs were 0.90 and 0.93, respectively. Recall and precision of the model were 89%\u00a0each.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Visual artificial intelligence platforms may be used to compare the autofluorescence signal of a given parathyroid gland against a large database. This may be a new adjunctive tool for intraoperative assessment of parathyroid glands during surgical exploration for primary hyperparathyroidism.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jso.26884","type":"journal-article","created":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T09:28:42Z","timestamp":1649842122000},"page":"263-267","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A visual deep learning model to predict abnormal versus normal parathyroid glands using intraoperative autofluorescence signals"],"prefix":"10.1002","volume":"126","author":[{"given":"Seyma N.","family":"Avci","sequence":"first","affiliation":[{"name":"Department of Endocrine Surgery Cleveland Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gizem","family":"Isiktas","sequence":"additional","affiliation":[{"name":"Department of Endocrine Surgery Cleveland Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Onuralp","family":"Ergun","sequence":"additional","affiliation":[{"name":"Department of Endocrine Surgery Cleveland Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1964-9286","authenticated-orcid":false,"given":"Eren","family":"Berber","sequence":"additional","affiliation":[{"name":"Department of Endocrine Surgery Cleveland Clinic  Cleveland Ohio USA"},{"name":"Department of General Surgery Cleveland Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2012-4022"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/20406223211015965"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/jso.26336"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.surg.2021.01.033"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.3583571"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.surg.2018.08.006"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000002693"},{"key":"e_1_2_7_9_1","unstructured":"An on\u2010device deep neural network for face detection. 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