| dc.creator | Apostolopoulos I.D., Papandrianos N.I., Papageorgiou E.I., Apostolopoulos D.J. | en |
| dc.date.accessioned | 2023-01-31T07:32:36Z | |
| dc.date.available | 2023-01-31T07:32:36Z | |
| dc.date.issued | 2022 | |
| dc.identifier | 10.3390/make4040040 | |
| dc.identifier.issn | 25044990 | |
| dc.identifier.uri | http://hdl.handle.net/11615/70734 | |
| dc.description.abstract | Background: Recent advances in Artificial Intelligence (AI) algorithms, and specifically Deep Learning (DL) methods, demonstrate substantial performance in detecting and classifying medical images. Recent clinical studies have reported novel optical technologies which enhance the localization or assess the viability of Parathyroid Glands (PG) during surgery, or preoperatively. These technologies could become complementary to the surgeon’s eyes and may improve surgical outcomes in thyroidectomy and parathyroidectomy. Methods: The study explores and reports the use of AI methods for identifying and localizing PGs, Primary Hyperparathyroidism (PHPT), Parathyroid Adenoma (PTA), and Multiglandular Disease (MGD). Results: The review identified 13 publications that employ Machine Learning and DL methods for preoperative and operative implementations. Conclusions: AI can aid in PG, PHPT, PTA, and MGD detection, as well as PG abnormality discrimination, both during surgery and non-invasively. © 2022 by the authors. | en |
| dc.language.iso | en | en |
| dc.source | Machine Learning and Knowledge Extraction | en |
| dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144597221&doi=10.3390%2fmake4040040&partnerID=40&md5=4847fb5e4499d86464a89e8e29e3b889 | |
| dc.subject | MDPI | en |
| dc.title | Artificial Intelligence Methods for Identifying and Localizing Abnormal Parathyroid Glands: A Review Study | en |
| dc.type | other | en |
