Journal of clinical ultrasound : JCU · 2024
Integrating artificial intelligence (S-Detect software) and contrast-enhanced ultrasound for enhanced diagnosis of thyroid nodules: A comprehensive evaluation study.
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Zou Lu-Lu, Zhang Qi, Yao Zhi, He Yong, Zhou Jun
Аннотация
PURPOSE: This study aims to assess the diagnostic efficacy of Korean Thyroid imaging reporting and data system (K-TIRADS), S-Detect software and contrast-enhanced ultrasound (CEUS) when employed individually, as well as their combined application, for the evaluation of thyroid nodules, with the objective of identifying the optimal method for diagnosing thyroid nodules.
METHODS: Two hundred and sixty eight cases pathologically proven of thyroid nodules were retrospectively enrolled. Each nodule was classified according to K-TIRADS. S-Detect software was utilized for intelligent analysis. CEUS was employed to acquire contrast-enhanced features.
RESULTS: The area under curve (AUC) values for diagnosing benign and malignant thyroid nodules using K-TIRADS alone, S-Detect software alone, CEUS alone, the combined application of K-TIRADS and CEUS, the combined application of S-Detect software and CEUS were 0.668, 0.668, 0.719, 0.741, and 0.759, respectively (p < 0.001). The sensitivity rate of S-Detect software was 89.9% (p < 0.001). It was the highest of the five diagnostic methods above.
CONCLUSION: The utilization of S-Detect software can be served as a powerful tool for early screening. Notably, the combined utilization of S-Detect software with CEUS demonstrates superior diagnostic performance compared to employing K-TIRADS, S-Detect software, CEUS used individually, as well as the combined application of K-TIRADS with CEUS.
METHODS: Two hundred and sixty eight cases pathologically proven of thyroid nodules were retrospectively enrolled. Each nodule was classified according to K-TIRADS. S-Detect software was utilized for intelligent analysis. CEUS was employed to acquire contrast-enhanced features.
RESULTS: The area under curve (AUC) values for diagnosing benign and malignant thyroid nodules using K-TIRADS alone, S-Detect software alone, CEUS alone, the combined application of K-TIRADS and CEUS, the combined application of S-Detect software and CEUS were 0.668, 0.668, 0.719, 0.741, and 0.759, respectively (p < 0.001). The sensitivity rate of S-Detect software was 89.9% (p < 0.001). It was the highest of the five diagnostic methods above.
CONCLUSION: The utilization of S-Detect software can be served as a powerful tool for early screening. Notably, the combined utilization of S-Detect software with CEUS demonstrates superior diagnostic performance compared to employing K-TIRADS, S-Detect software, CEUS used individually, as well as the combined application of K-TIRADS with CEUS.