ArticleDiseases (Basel, Switzerland)2022
A Deep Learning Methodology for the Detection of Abnormal Parathyroid Glands via Scintigraphy with
Article in Diseases (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 13 citations in OpenAlex.
- Slice Level Classification of Parathyroid Adenoma Using Arterial Phase CT Images with Hybrid Light Attention Mechanism Based Residual Framework.Bioengineering (Basel, Switzerland) · 2026Article
- Imaging Recommendations for Diagnosis and Management of Primary Parathyroid Pathologies: A Comprehensive Review.Cancers · 2024Review
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
backgroundParathyroid 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.
methodsThe 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.
resultsParaNet 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.
conclusionsThe proposed network is the first to introduce the automatic discrimination of PG and nPG scans acquired by scintigraphy with
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