{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T06:04:41Z","timestamp":1784873081169,"version":"3.55.0"},"reference-count":26,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Parathyroid scintigraphy with 99mTc-sestamibi (MIBI) is an established technique for localising abnormal parathyroid glands (PGs). However, the identification and localisation of PGs require much attention from medical experts and are time-consuming. Artificial intelligence methods can offer an assisting solution. This retrospective study enrolled 632 patients who underwent parathyroid scintigraphy with double-phase and thyroid subtraction techniques. The study proposes a three-path approach, employing the state-of-the-art convolutional neural network called VGG19. Images input to the model involved a set of three scintigraphic images in each case: MIBI early phase, MIBI late phase, and 99mTcO4 thyroid scan. A medical expert\u2019s diagnosis provided the ground truth for positive\/negative results. Moreover, the visualised suggested areas of interest produced by the Grad-CAM algorithm are examined to evaluate the PG-level agreement between the model and the experts. Medical experts identified 545 abnormal glands in 452 patients. On a patient basis, the deep learning (DL) model attained an accuracy of 94.8% (sensitivity 93.8%; specificity 97.2%) in distinguishing normal from abnormal scintigraphic images. On a PG basis and in achieving identical positioning of the findings with the experts, the model correctly identified and localised 453\/545 glands (83.1%) and yielded 101 false focal results (false positive rate 18.23%). Concerning surgical findings, the expert\u2019s sensitivity was 89.68% on patients and 77.6% on a PG basis, while that of the model reached 84.5% and 67.6%, respectively. Deep learning in parathyroid scintigraphy can potentially assist medical experts in identifying abnormal findings.<\/jats:p>","DOI":"10.3390\/a15120455","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T03:00:36Z","timestamp":1669950036000},"page":"455","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Detection and Localisation of Abnormal Parathyroid Glands: An Explainable Deep Learning Approach"],"prefix":"10.3390","volume":"15","author":[{"given":"Dimitris J.","family":"Apostolopoulos","sequence":"first","affiliation":[{"name":"Department of Nuclear Medicine, School of Medicine, University General Hospital of Patras, University of Patras, GR 265-00 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6439-9282","authenticated-orcid":false,"given":"Ioannis D.","family":"Apostolopoulos","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, School of Medicine, University of Patras, GR 265-00 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolaos D.","family":"Papathanasiou","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, School of Medicine, University General Hospital of Patras, University of Patras, GR 265-00 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trifon","family":"Spyridonidis","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, School of Medicine, University General Hospital of Patras, University of Patras, GR 265-00 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George S.","family":"Panayiotakis","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, School of Medicine, University of Patras, GR 265-00 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s12105-008-0088-8","article-title":"Parathyroid Adenoma","volume":"2","author":"Wieneke","year":"2008","journal-title":"Head Neck Pathol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1111\/joim.12523","article-title":"Genetics of Parathyroid Tumours","volume":"280","author":"Thakker","year":"2016","journal-title":"J. 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