A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment

被引:160
作者
Choi, Young Jun [1 ,2 ]
Baek, Jung Hwan [1 ,2 ]
Park, Hye Sun [1 ,2 ]
Shim, Woo Hyun [1 ,2 ]
Kim, Tae Yong [3 ,4 ]
Shong, Young Kee [3 ,4 ]
Lee, Jeong Hyun [1 ,2 ]
机构
[1] Univ Ulsan, Coll Med, Dept Radiol, Seoul, South Korea
[2] Univ Ulsan, Res Inst Radiol, Coll Med, Seoul, South Korea
[3] Univ Ulsan, Coll Med, Dept Endocrinol & Metab, Seoul, South Korea
[4] Univ Ulsan, Coll Med, Asan Med Ctr, Seoul, South Korea
关键词
computer-aided system; thyroid nodule; ultrasound; CONSENSUS STATEMENT; KOREAN SOCIETY; ULTRASONOGRAPHY; MANAGEMENT; ACCURACY; US; RECOMMENDATIONS; VOLUMETRY; RADIOLOGY; BENIGN;
D O I
10.1089/thy.2016.0372
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: An initial clinical assessment is described of a new, commercially available, computer-aided diagnosis (CAD) system using artificial intelligence (AI) for thyroid ultrasound, and its performance is evaluated in the diagnosis of malignant thyroid nodules and categorization of nodule characteristics. Methods: Patients with thyroid nodules with decisive diagnosis, whether benign or malignant, were consecutively enrolled from November 2015 to February 2016. An experienced radiologist reviewed the ultrasound image characteristics of the thyroid nodules, while another radiologist assessed the same thyroid nodules using the CAD system, providing ultrasound characteristics and a diagnosis of whether nodules were benign or malignant. The diagnostic performance and agreement of US characteristics between the experienced radiologist and the CAD system were compared. Results: In total, 102 thyroid nodules from 89 patients were included; 59 (57.8%) were benign and 43 (42.2%) were malignant. The CAD system showed a similar sensitivity as the experienced radiologist (90.7% vs. 88.4%, p > 0.99), but a lower specificity and a lower area under the receiver operating characteristic (AUROC) curve (specificity: 74.6% vs. 94.9%, p = 0.002; AUROC: 0.83 vs. 0.92, p = 0.021). Classifications of the ultrasound characteristics (composition, orientation, echogenicity, and spongiform) between radiologist and CAD system were in substantial agreement (j = 0.659, 0.740, 0.733, and 0.658, respectively), while the margin showed a fair agreement (j = 0.239). Conclusion: The sensitivity of the CAD system using AI for malignant thyroid nodules was as good as that of the experienced radiologist, while specificity and accuracy were lower than those of the experienced radiologist. The CAD system showed an acceptable agreement with the experienced radiologist for characterization of thyroid nodules.
引用
收藏
页码:546 / 552
页数:7
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