ArticleThe Journal of clinical endocrinology and metabolism2022
Convolutional Neural Network-Based Computer-Assisted Diagnosis of Hashimoto's Thyroiditis on Ultrasound.
Article in The Journal of clinical endocrinology and metabolism, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 33 citations in OpenAlex.
- Ultrasound Radiomics for Hashimoto's Thyroiditis: State of the Art and Future Perspectives.Journal of clinical ultrasound : JCU · 2026Review
- Nodule size significantly affects the diagnostic value of chinese thyroid imaging reporting and data system: a multi-center retrospective study.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Artificial intelligence-based ultrasound diagnosis of Hashimoto's thyroiditis: a systematic review and meta-analysis.Thyroid research · 2026Review
- Diagnostic Performance of the ACR TI-RADS, C-TIRADS, K-TIRADS, and EU TI-RADS Classification Systems for Thyroid Micronodules: A Multicenter Prospective Study.International journal of general medicine · 2026Article
- Revolutionizing thyroid nodule diagnosis in Hashimoto's thyroiditis: AI-driven radiomics and deep learning model.International journal of surgery (London, England) · 2025Article
- Prediction of Seronegative Hashimoto's thyroiditis using machine learning models based on ultrasound radiomics: a multicenter study.BMC immunology · 2025Article
- Transcriptomic profiling and machine learning reveal novel RNA signatures for enhanced molecular characterization of Hashimoto's thyroiditis.Scientific reports · 2025Article
- ChatGPT-4 Vision: a promising tool for diagnosing thyroid nodules.Frontiers in medicine · 2025Article
- Association of autoimmune thyroid disease with type 1 diabetes mellitus and its ultrasonic diagnosis and management.World journal of diabetes · 2024Review
- A narrative review of deep learning in thyroid imaging: current progress and future prospects.Quantitative imaging in medicine and surgery · 2024Review
- The diagnostic value of ultrasound on different-sized thyroid nodules based on ACR TI-RADS.Endocrine · 2023Article
- HTC-Net: Hashimoto's thyroiditis ultrasound image classification model based on residual network reinforced by channel attention mechanism.Health information science and systems · 2023Article
- Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals.Postgraduate medical journal · 2023Review
- Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin Tumors.Sensors (Basel, Switzerland) · 2023Article
- Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- Comparing Diagnostic Efficacy of C-TIRADS Positive Features on Different Sizes of Thyroid Nodules.International journal of general medicine · 2023Article
- Comparison of Diagnostic Values of ACR TI-RADS versus C-TIRADS Scoring and Classification Systems for the Elderly Thyroid Cancers.International journal of general medicine · 2023Article
- Diagnosis of thyroid disease using deep convolutional neural network models applied to thyroid scintigraphy images: a multicenter study.Frontiers in endocrinology · 2023Article
- AI-Assisted Ultrasound for the Early Diagnosis of Antibody-Negative Autoimmune Thyroiditis.Journal of multidisciplinary healthcare · 2023Review
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study investigates the efficiency of deep learning models in the automated diagnosis of Hashimoto's thyroiditis (HT) using real-world ultrasound data from ultrasound examinations by computer-assisted diagnosis (CAD) with artificial intelligence.
methodsWe retrospectively collected ultrasound images from patients with and without HT from 2 hospitals in China between September 2008 and February 2018. Images were divided into a training set (80%) and a validation set (20%). We ensembled 9 convolutional neural networks (CNNs) as the final model (CAD-HT) for HT classification. The model's diagnostic performance was validated and compared to 2 hospital validation sets. We also compared the accuracy of CAD-HT against seniors/junior radiologists. Subgroup analysis of CAD-HT performance for different thyroid hormone levels (hyperthyroidism, hypothyroidism, and euthyroidism) was also evaluated.
results39 280 ultrasound images from 21 118 patients were included in this study. The accuracy, sensitivity, and specificity of the HT-CAD model were 0.892, 0.890, and 0.895, respectively. HT-CAD performance between 2 hospitals was not significantly different. The HT-CAD model achieved a higher performance (P < 0.001) when compared to senior radiologists, with a nearly 9% accuracy improvement. HT-CAD had almost similar accuracy (range 0.87-0.894) for the 3 subgroups based on thyroid hormone level.
conclusionThe HT-CAD strategy based on CNN significantly improved the radiologists' diagnostic accuracy of HT. Our model demonstrates good performance and robustness in different hospitals and for different thyroid hormone levels.
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