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- Title
A Deep Learning Methodology for the Detection of Abnormal Parathyroid Glands via Scintigraphy with 99m Tc-Sestamibi.
- Authors
Apostolopoulos, Ioannis D.; Papathanasiou, Nikolaos D.; Apostolopoulos, Dimitris J.
- Abstract
Background: Parathyroid proliferative disorder encompasses a wide spectrum of diseases, including parathyroid adenoma (PTA), parathyroid hyperplasia, and parathyroid carcinoma. Imaging modalities that deliver their results preoperatively help in the localisation of parathyroid glands (PGs) and assist in surgery. Artificial intelligence and, more specifically, image detection methods, can assist medical experts and reduce the workload in their everyday routine. Methods: The present study employs an innovative CNN topology called ParaNet, to analyse early MIBI, late MIBI, and TcO4 thyroid scan images simultaneously to perform first-level discrimination between patients with abnormal PGs (aPG) and patients with normal PGs (nPG). The study includes 632 parathyroid scans. Results: ParaNet exhibits a top performance, reaching an accuracy of 96.56% in distinguishing between aPG and nPG scans. Its sensitivity and specificity are 96.38% and 97.02%, respectively. PPV and NPV values are 98.76% and 91.57%, respectively. Conclusions: The proposed network is the first to introduce the automatic discrimination of PG and nPG scans acquired by scintigraphy with 99mTc-sestamibi (MIBI). This methodology could be applied to the everyday routine of medics for real-time evaluation or educational purposes.
- Subjects
PARATHYROID glands; RADIONUCLIDE imaging; ARTIFICIAL intelligence; EDUCATIONAL objectives; DEEP learning; SENSITIVITY &; specificity (Statistics)
- Publication
Diseases, 2022, Vol 10, Issue 3, pN.PAG
- ISSN
2079-9721
- Publication type
Article
- DOI
10.3390/diseases10030056