Clinical value of artificial intelligence in thyroid ultrasound: a prospective study from the real world

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. Materials and 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.

Automated summary

This study evaluated the performance of a commercial AI-assisted ultrasonography system for diagnosing thyroid nodules and demonstrated its value in real-world medical practice. The AI system performed comparably to experienced radiologists and outperformed residents. It improved the diagnostic performance for nodules <= 1.5 cm while reducing unnecessary biopsies for nodules > 1.5 cm.