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- Title
Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance.
- Authors
Si Eun Lee; Hye Jung Kim; Hae Kyoung Jung; Jing Hyang Jung; Jae-Han Jeon; Jin Hee Lee; Hanpyo Hong; Eun Jung Lee; Daham Kim; Jin Young Kwak
- 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 intelligencebased 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 ormalignant independently. Theywere 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 selftraining 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 AICAD (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.
- Subjects
ARTIFICIAL intelligence; THYROID nodules; AUTODIDACTICISM; COMPUTER-aided diagnosis; RECEIVER operating characteristic curves
- Publication
Frontiers in Endocrinology, 2024, p1
- ISSN
1664-2392
- Publication type
Article
- DOI
10.3389/fendo.2024.1372397