American journal of surgery · 2025
The role of artificial intelligence in surgeon-performed ultrasonographic evaluation of cytologically indeterminate thyroid nodules.
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Fung Man Him Matrix, Ng Wai In, Lee Henry Ethan, Chan Tin Ho, Leung Steven Tsz King, Luk Yan, Lang Brian Hung Hin
Аннотация
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.
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.