ArticleFrontiers in endocrinology2024
Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance.
Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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4 citing papers in PubMed.
- Scoping review of regulatory transparency in AI-based radiology software: analysis of PMDA-approved SaMD products.Japanese journal of radiology · 2026Article
- Development of a Deep Learning-Based Feedback Model to Assist Medical Students Learning Renal Ultrasound Acquisition: Mixed Methods Study.JMIR medical education · 2026Article
- Multi-view AI output variability as a teaching resource in a micro-course for thyroid TI-RADS interpretation among ultrasound residents: a randomized controlled trial.Frontiers in medicine · 2026Article
- Thyroid Nodule Characterization: Which Thyroid Imaging Reporting and Data System (TIRADS) Is More Accurate? A Comparison Between Radiologists with Different Experiences and Artificial Intelligence Software.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Data-driven digital learning could improve the diagnostic performance of novice students for thyroid nodules. Objective: To evaluate the efficacy of digital self-learning and artificial intelligence-based computer-assisted diagnosis (AI-CAD) for inexperienced readers to diagnose thyroid nodules. Methods: Between February and August 2023, a total of 26 readers (less than 1 year of experience in thyroid US from various departments) from 6 hospitals participated in this study. Readers completed an online learning session comprising 3,000 thyroid nodules annotated as benign or malignant independently. They were asked to assess a test set consisting of 120 thyroid nodules with known surgical pathology before and after a learning session. Then, they referred to AI-CAD and made their final decisions on the thyroid nodules. Diagnostic performances before and after self-training and with AI-CAD assistance were evaluated and compared between radiology residents and readers from different specialties. Results: AUC (area under the receiver operating characteristic curve) improved after the self-learning session, and it improved further after radiologists referred to AI-CAD (0.679 vs 0.713 vs 0.758, p<0.05). Although the 18 radiology residents showed improved AUC (0.7 to 0.743, p=0.016) and accuracy (69.9% to 74.2%, p=0.013) after self-learning, the readers from other departments did not. With AI-CAD assistance, sensitivity (radiology 70.3% to 74.9%, others 67.9% to 82.3%, all p<0.05) and accuracy (radiology 74.2% to 77.1%, others 64.4% to 72.8%, all p <0.05) improved in all readers. Conclusion: While AI-CAD assistance helps improve the diagnostic performance of all inexperienced readers for thyroid nodules, self-learning was only effective for radiology residents with more background knowledge of ultrasonography. Clinical Impact: Online self-learning, along with AI-CAD assistance, can effectively enhance the diagnostic performance of radiology residents in thyroid cancer.
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