Efficacy of preoperative ultrasound evaluation of thyroid nodules by artificial intelligence automatic detection system version 2.0: A preliminary study
10.16781/j.0258-879x.2020.10.1077
- VernacularTitle: 2.0 版人工智能自动检测系统对甲状腺结节术前超声评估效能的初步探讨
- Author:
Fang-Qi GUO
1
Author Information
1. Department of Ultrasound, Changzheng Hospital, Naval Medical University (Second Military Medical University)
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Data system;
High-frequency ultrasound;
Thyroid imaging reporting;
Thyroid nodules
- From:
Academic Journal of Second Military Medical University
2020;41(10):1077-1083
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the efficacy of AI-SONICTM Thyroid system, a version 2.0 artificial intelligence (AI) automatic detection system, in the preoperative ultrasound diagnosis of thyroid nodules, and to evaluate the application value of AI automatic detection system version 2.0 in the differential diagnosis of benign and malignant thyroid nodules by comparing with the subjective diagnosis conclusions of sonographers with different seniorities. Methods A total of 247 patients (325 thyroid nodules) admitted to the Department of General Surgery in our hospital from Aug. 2019 to Jan. 2020 were selected for this study. All patients underwent routine ultrasound examinations by a senior sonographer with 13 years of experience in thyroid ultrasound diagnosis and a junior sonographer with 4 years of work experience. At the same time, the patients were also examined by another sonographer with 20 years of work experience using AI automatic detection system version 2.0, without knowing the diagnosis conclusions of the above two sonographers. Kappa test was used to evaluate the consistency of the results of routine ultrasound examination of sonographers with different seniorities and AI automatic detection system version 2.0 and the postoperative pathological results. Results The postoperative pathology confirmed 229 malignant nodules and 96 benign nodules. The sensitivity, specificity and accuracy in the diagnosis of benign and malignant thyroid nodules were 85.15% (195/229), 66.67% (64/96) and 79.69% (259/325), 93.45% (214/229), 79.17% (76/96) and 89.23% (290/325), and 92.58% (212/229), 71.88% (69/96) and 86.46% (281/325) for junior sonographer, senior sonographer and AI automatic detection system version 2.0, respectively. The Kappa consistency test results showed that the diagnosis result of senior sonographer was highly consistent with the pathological diagnosis result (Kappa value 0.78, P＜0.01), while the diagnosis results of junior sonographer and AI automatic detection system version 2.0 were generally consistent with the pathological diagnosis result (Kappa values 0.55 and 0.74, both P＜0.01). Conclusion The sensitivity, accuracy and specificity of the AI automatic detection system version 2.0 AI-SONICTM Thyroid in diagnosing benign and malignant thyroid nodules are similar to those of routine ultrasound examination by senior sonographers, and the system might be a reliable auxiliary means for preoperative evaluation of benign and malignant thyroid nodules.
