European thyroid journal · 2026
Artificial intelligence-assisted risk stratification of thyroid nodules with atypia of undetermined significance.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kim Jinyoung, Lee Jeongmin, Ha Jeonghoon, Kwon Ohjoon, Baek Ki-Hyun, Song Chang Myeon, Lee Hyeon A, Kwon Hyemi, Youn Inyoung, Kwon Mi-Ri, Lim Dong-Jun
Аннотация
OBJECTIVE: Given the limitations of conventional approaches in managing indeterminate thyroid nodules, there remains an unmet need for non-invasive assistant tools to improve risk stratification. This study aimed to evaluate the clinical applicability of an artificial intelligence (AI) model for thyroid nodules with atypia of undetermined significance (AUS) cytology.
METHODS: We retrospectively reviewed patients who underwent fine-needle aspiration (FNA) for thyroid nodules between January 2019 and December 2020 across five medical institutions in Korea. Nodules initially diagnosed as AUS and later confirmed as benign or malignant were included. A previously developed deep learning-based AI model, AI-Thyroid, was employed to provide binary classifications (benign or malignant) and malignancy risk estimates.
RESULTS: A total of 165 thyroid nodules were analyzed. The median (interquartile range) longest diameter was 1.30 cm (0.80-2.10), and the malignancy rate of the cohort was 39%. In binary classification tasks, the model achieved a sensitivity of 0.91 and a negative predictive value of 0.87. The area under the curve (AUC) based on estimated malignancy risk was 0.75 (95% confidence interval (CI): 0.68-0.83), and the AUC derived from K-TIRADS categories 2-5 was 0.76 (95% CI: 0.69-0.83), indicating comparable diagnostic accuracy with the traditional scoring system. Subgroup analyses demonstrated that the model achieved a sensitivity of 98% in nodules smaller than 1.5 cm.
CONCLUSION: AI-assisted ultrasound analysis offers supplementary diagnostic information for thyroid nodules with AUS cytology. Its high sensitivity and negative predictive value may assist clinicians in decision-making processes, particularly for small, low-risk thyroid nodules.
METHODS: We retrospectively reviewed patients who underwent fine-needle aspiration (FNA) for thyroid nodules between January 2019 and December 2020 across five medical institutions in Korea. Nodules initially diagnosed as AUS and later confirmed as benign or malignant were included. A previously developed deep learning-based AI model, AI-Thyroid, was employed to provide binary classifications (benign or malignant) and malignancy risk estimates.
RESULTS: A total of 165 thyroid nodules were analyzed. The median (interquartile range) longest diameter was 1.30 cm (0.80-2.10), and the malignancy rate of the cohort was 39%. In binary classification tasks, the model achieved a sensitivity of 0.91 and a negative predictive value of 0.87. The area under the curve (AUC) based on estimated malignancy risk was 0.75 (95% confidence interval (CI): 0.68-0.83), and the AUC derived from K-TIRADS categories 2-5 was 0.76 (95% CI: 0.69-0.83), indicating comparable diagnostic accuracy with the traditional scoring system. Subgroup analyses demonstrated that the model achieved a sensitivity of 98% in nodules smaller than 1.5 cm.
CONCLUSION: AI-assisted ultrasound analysis offers supplementary diagnostic information for thyroid nodules with AUS cytology. Its high sensitivity and negative predictive value may assist clinicians in decision-making processes, particularly for small, low-risk thyroid nodules.