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Updated: Jan 18, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
The role of artificial intelligence in surgeon-performed ultrasonographic evaluation of cytologically indeterminate
Man Him Matrix Fung1, Wai In Ng2, Henry Ethan Lee1
1Department of Surgery, Queen Mary Hospital, the University of Hong Kong, Hong Kong.
Introduction:
Evaluating indeterminate thyroid nodules(ITN) is challenging, especially without molecular tests. This study examines whether artificial intelligence (AI) assistance can improve ITN diagnostic accuracy and bridge expertise gaps in surgeon-performed ultrasound.
Methods:
134 ultrasound clips from 67 patients with ITN were reviewed by doctors of four levels: endocrine-surgery specialist, senior residents, junior residents, and medical student. After a 2-week wash-out, they re-evaluated the clips using AI-SONIC, an AI platform analyzing ultrasound real-time to predict cancer risk. Performance was validated against final histopathology.
Results:
Without AI, medical students, junior residents and senior residents performed significantly worse than specialists(AUROC 0.530-0.560 vs 0.771, p < 0.05). AI-SONIC improved residents' and medical students' diagnostic accuracy to levels comparable with specialists(AUROC 0.733-0.751 vs 0.771). The specialists' performance remained unchanged with AI assistance.
Conclusion:
AI enhances ultrasound evaluation of ITN by junior surgeons and medical students, elevating their accuracy to expert levels, supporting clinical assessment and medical education.
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