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This paper is in the following e-collection/theme issue:

Artificial Intelligence (2509) Imaging Informatics (360) Machine Learning (2347) Computer-Aided Diagnosis (296) Medical imaging (148) AI Applications in Biomedical Engineering (22) Applications of AI (408)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73516, first published .
Doctor reviewing ultrasound image on laptop with stethoscope

Performance Evaluation of Deep Learning for the Detection and Segmentation of Thyroid Nodules: Systematic Review and Meta-Analysis

Performance Evaluation of Deep Learning for the Detection and Segmentation of Thyroid Nodules: Systematic Review and Meta-Analysis

Authors of this article:

Jiayu Ni1 Author Orcid Image ;   Yue You1 Author Orcid Image ;   Xiaohe Wu2 Author Orcid Image ;   Xueke Chen3 Author Orcid Image ;   Jiaying Wang4 Author Orcid Image ;   Yuan Li1, 5 Author Orcid Image
Article Authors Cited by (11) Tweetations (2) Metrics

Journals

  1. Wortsman X, Lozano M, Rodriguez F, Valderrama Y, Ortiz‐Orellana G, Zattar L, de Cabo F, Ducati E, Sigrist R, Fontan C, Rezende J, Gonzalez C, Schelke L, Zavariz J, Barrera P, Velthuis P. Artificial Intelligence Deep Learning Ultrasound Discrimination of Cosmetic Fillers. Journal of Ultrasound in Medicine 2026;45(2):429 View
  2. Chien I, Hsu Y, Cheng S. Deep Learning for Ultrasound Classification to Identify Noninvasive Follicular Thyroid Neoplasms with Papillary–Like Nuclear Features. Journal of Imaging Informatics in Medicine 2026 View
  3. Tatar O, Çubukçu A, Cantürk N. Better together: what can state-of-the-art ML models add to thyroid nodule diagnostics?. Updates in Surgery 2026;78(3):1289 View
  4. Zhang C, Wang F, Tang X, Li J, Ding N, Hong Y, Song P, Bai L, Su J. Virtual medicine: medical AI in human health and diseases. Military Medical Research 2026;13(1):100012 View
  5. Ruan J, Chen X, Yao H, Xu Y, Xu S, Zhao S, Wu Y, Dai Y, Chen Y, Ma S, Zhang Q, Zhou Y, Asghar Heidari A, Chen H, Shentu Y. AGPLO‐Driven Optimisation for Accurate Segmentation of Papillary Thyroid Carcinoma in Medical Imaging. CAAI Transactions on Intelligence Technology 2026 View
  6. Skaik K, Abdallah J, Koucheki R, Lex J, Ahn H, Toor J, Ravi B, Larouche J, Abbas A. Opportunistic screening for osteoporosis using chest X-rays and deep learning: A systematic review and meta-analysis. Bone 2026;210:117923 View
  7. Ślusarczyk J, Szreder B, Pietrzyk P, Stanek N, Krawczyk P, Łapaj M, Jagiełło A, Noweta Z, Lewicka M, Chadań T. ARTIFICIAL INTELLIGENCE FOR ULTRASOUND‑BASED DIAGNOSIS AND RISK STRATIFICATION OF THYROID NODULES: EVIDENCE, HUMAN FACTORS, AND HEALTH‑IT IMPLICATIONS. International Journal of Innovative Technologies in Social Science 2026;2(2(50)) View

Conference Proceedings

  1. Murugesakumar B, S A, Arunmanikandan A, Chithra M, Dhasaranjan M. 2025 Tenth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). U-Net Convolutional Networks for Real-Time Biomedical Image Segmentation and Anomaly Detection View
  2. Shaik H, Syed H. 2026 IEEE International Conference on Emerging Computing and Intelligent Technologies (ICoECIT). A Hybrid Transformer Framework for Text-Visual Thyroid Nodule Segmentation in Ultrasound Diagnostics View
  3. Das S, Ahmed A, Chaudhury P, Tripathy H, Gaber T, Qutqut M. 2026 6th Middle East and North Africa Communications Conference (MENACOMM). Autism Spectrum Disorder Detection: Privacy-Preserving AI Approaches View
  4. S A. 2026 6th International Conference on Pervasive Computing and Social Networking (ICPCSN). Optimized ConvNeXt-Tiny Architecture for Clinical Prediction of Hypothyroidism using Enhanced Deep Learning Analytics to Strengthen Early Diagnosis and Sustainable Healthcare Innovation View