目的:评估商业人工智能(AI)辅助超声检查(US)对甲状腺结节的诊断性能,并验证其在现实医学实践中的价值。
方法:从2021年3月至2021年7月,前瞻性地纳入了236例具有312个可疑甲状腺结节的连续患者。一位经验丰富的放射科医生使用实时AI系统(S-Detect)进行了美国检查。记录结节的美国图像和AI报告。9名居民和3名资深放射科医生被邀请根据记录的美国图像做出“良性”或“恶性”诊断,而不知道AI报告。在提到AI报告后,再次诊断。AI的诊断性能,居民,分析了有和没有AI报告的高级放射科医生。
结果:灵敏度,准确度,AI系统的AUC分别为0.95、0.84和0.753,与经验丰富的放射科医生没有统计学差异,但优于居民(均p<0.01)。AI辅助驻留策略显著提高了结节≤1.5cm的准确度和灵敏度(均p<0.01),而对于>1.5cm的结节,不必要的活检率降低了27.7%(p=0.034)。
结论:AI系统实现了性能,用于癌症诊断,与普通的高级甲状腺放射科医生相当。AI辅助策略可以显着提高经验不足的放射科医生的整体诊断性能。同时增加甲状腺癌≤1.5cm的发现,并减少在现实世界的医疗实践中对>1.5cm的结节进行不必要的活检。
结论:•AI系统在评估甲状腺癌方面达到了类似于放射科医师的高级水平,并且可以显着提高居民的整体诊断能力。•AI辅助策略显着改善≤1.5cm甲状腺癌筛查AUC,准确度,和居民的敏感性,导致甲状腺癌的检出增加,同时保持与放射科医生相当的特异性。•AI辅助策略显着降低了居民对甲状腺结节>1.5cm的不必要活检率,同时保持与放射科医生相当的灵敏度。
OBJECTIVE: To evaluate the diagnostic performance of a commercial artificial intelligence (AI)-assisted ultrasonography (US) for thyroid nodules and to validate its value in real-world medical practice.
METHODS: From March 2021 to July 2021, 236 consecutive patients with 312 suspicious thyroid nodules were prospectively enrolled in this study. One experienced
radiologist performed US examinations with a real-time AI system (S-Detect). US images and AI reports of the nodules were recorded. Nine residents and three senior radiologists were invited to make a \"benign\" or \"malignant\" diagnosis based on recorded US images without knowing the AI reports. After referring to AI reports, the diagnosis was made again. The diagnostic performance of AI, residents, and senior radiologists with and without AI reports were analyzed.
RESULTS: The sensitivity, accuracy, and AUC of the AI system were 0.95, 0.84, and 0.753, respectively, and were not statistically different from those of the experienced radiologists, but were superior to those of the residents (all p < 0.01). The AI-assisted resident strategy significantly improved the accuracy and sensitivity for nodules ≤ 1.5 cm (all p < 0.01), while reducing the unnecessary biopsy rate by up to 27.7% for nodules > 1.5 cm (p = 0.034).
CONCLUSIONS: The AI system achieved performance, for cancer diagnosis, comparable to that of an average senior thyroid
radiologist. The AI-assisted strategy can significantly improve the overall diagnostic performance for less-experienced radiologists, while increasing the discovery of thyroid cancer ≤ 1.5 cm and reducing unnecessary biopsies for nodules > 1.5 cm in real-world medical practice.
CONCLUSIONS: • The AI system reached a senior
radiologist-like level in the evaluation of thyroid cancer and could significantly improve the overall diagnostic performance of residents. • The AI-assisted strategy significantly improved ≤ 1.5 cm thyroid cancer screening AUC, accuracy, and sensitivity of the residents, leading to an increased detection of thyroid cancer while maintaining a comparable specificity to that of radiologists alone. • The AI-assisted strategy significantly reduced the unnecessary biopsy rate for thyroid nodules > 1.5 cm by the residents, while maintaining a comparable sensitivity to that of radiologists alone.