Application of artificial intelligence automatic detection system in preoperative ultrasonic diagnosis of thyroid nodules
10.16781/j.0258-879x.2019.11.1183
- 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;
Thyroid neoplasms;
Thyroid nodule;
Ultrasonography
- From:
Academic Journal of Second Military Medical University
2019;40(11):1183-1189
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To explore the application value of artificial intelligence (AI) automatic detection system in preoperative ultrasonic diagnosis of thyroid nodules. Methods: Totally 98 patients with 137 thyroid nodules admitted to the General Surgery Department of Changzheng Hospital of Naval Medical University (Second Military Medical University) from April 2019 to July 2019 were enrolled in this study. Pathological data and ultrasonic diagnosis results were retrospectively analyzed. All patients underwent conventional ultrasonography and AI automatic detection before surgery. The diagnoses for benign and malignant thyroid nodules were compared between conventional ultrasonography and AI automatic detection system, which were based on the postoperative pathology. The sensitivity, specificity and accuracy of the two examination methods were calculated, and Kappa coefficient was performed to measure the consistency between the two methods and postoperative pathological diagnosis. Results: The sensitivity, specificity and accuracy of conventional ultrasonography in diagnosis of benign and malignant thyroid nodules were respectively 93.75% (90/96), 80.49% (33/41) and 89.78% (123/137), and those of AI automatic detection were 89.58% (86/96), 68.29% (28/41) and 83.21% (114/137). There was substantial coefficience between conventional ultrasonography and pathological diagnosis results (Kappa=0.75, P<0.001), and that was fair coefficience between AI automatic detection system and pathological diagnosis results (Kappa=0.59, P<0.001). Conclusion: The sensitivity and accuracy of AI automatic detection system are slightly lower than but close to those of conventional ultrasonography in differentiating benign from malignant thyroid nodules. AI automatic detection system can be used as an effective supplement to assist conventional ultrasonography for preoperative assessment of thyroid nodules.
