Automatic detection of thyroid nodules with a real-time artificial intelligence system in a real clinical scenario and the associated influencing factors

Article type: Research Article

Authors: Xu, Ya-Dan^{a; b; c; 1} | Tang, Yang^{a; b; 1} | Zhang, Qi^{a; b; 1} | Zhao, Zheng-Yong^a | Zhao, Chong-Ke^a | Fan, Pei-Li^{a; b} | Jin, Yun-Jie^{a; b} | Ji, Zheng-Biao^{a; b} | Han, Hong^{a; b; c} | Xu, Hui-Xiong^{a; b; c} | Shi, Yi-Lei^d | Xu, Ben-Hua^{a; b; *} | Li, Xiao-Long^{a; b; *}

Affiliations: [a] Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China | [b] Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China | [c] Shanghai Institute of Medical Imaging, Shanghai, China | [d] Med Imaging AI Co., Ltd, Wuxi, China

Correspondence: [*] Corresponding authors: Ben-Hua Xu, Xiao-Long Li; Department of Ultrasound, Zhongshan Hospital, Fudan University, 180th Fengling Rd, Xuhui District, Shanghai, 200032, China; Institute of Ultrasound in Medicine and Engineering, Fudan University,180th Fengling Rd, Xuhui District, Shanghai, 200032, China. E-mail: [email protected] (Ben-Hua Xu); E-mail: [email protected] (Xiao-Long Li).

Note: [1] Ya-Dan Xu, Yang Tang and Qi Zhang contributed equally to this work.

Abstract: BACKGROUND:At present, most articles mainly focused on the diagnosis of thyroid nodules by using artificial intelligence (AI), and there was little research on the detection performance of AI in thyroid nodules. OBJECTIVE:To explore the value of a real-time AI based on computer-aided diagnosis system in the detection of thyroid nodules and to analyze the factors influencing the detection accuracy. METHODS:From June 1, 2022 to December 31, 2023, 224 consecutive patients with 587 thyroid nodules were prospective collected. Based on the detection results determined by two experienced radiologists (both with more than 15 years experience in thyroid diagnosis), the detection ability of thyroid nodules of radiologists with different experience levels (junior radiologist with 1 year experience and senior radiologist with 5 years experience in thyroid diagnosis) and real-time AI were compared. According to the logistic regression analysis, the factors influencing the real-time AI detection of thyroid nodules were analyzed. RESULTS:The detection rate of thyroid nodules by real-time AI was significantly higher than that of junior radiologist (P = 0.013), but lower than that of senior radiologist (P = 0.001). Multivariate logistic regression analysis showed that nodules size, superior pole, outside (near carotid artery), close to vessel, echogenicity (isoechoic, hyperechoic, mixed-echoic), morphology (not very regular, irregular), margin (unclear), ACR TI-RADS category 4 and 5 were significant independent influencing factors (all P < 0.05). With the combination of real-time AI and radiologists, junior and senior radiologist increased the detection rate to 97.4% (P < 0.001) and 99.1% (P = 0.015) respectively. CONCLUSONS:The real-time AI has good performance in thyroid nodule detection and can be a good auxiliary tool in the clinical work of radiologists.

Keywords: Artificial intelligence, thyroid, nodule, ultrasonography

DOI: 10.3233/CH-242099

Journal: Clinical Hemorheology and Microcirculation, vol. 87, no. 4, pp. 437-450, 2024