Computer-aided diagnosis system of thyroid nodules ultrasonography

Abstract

To evaluate the diagnostic efficiency of computer-aided diagnosis (CAD) system and 111 radiologists with different experience in identifying benign and malignant thyroid nodules, and to summarize the ultrasound features that may affect the diagnostic of CAD and radiologists. Fifty thyroid nodules and 111 radiologists were enrolled in this study. All the 50 nodules were diagnosed by the 111 radiologists and the CAD system simultaneously. The diagnostic performance of the CAD system, senior and junior radiologists with the maximum accuracy were calculated and compared. Interobserver agreement for different ultrasound characteristics between the CAD and senior radiologist were analyzed. CAD system showed a higher specificity than junior radiologist (87.5% vs 70.4%, P = .03), and a lower sensitivity than the senior radiologist and junior radiologist but the statistics were not significant (76.9% vs 86.9%, P > .5; 76.9% vs 82.6%, P > .5). The CAD system and senior radiologist got larger AUC than junior radiologist but the differences were not statistically significant (0.82 vs 0.76, respectively; P = .5). The interobserver agreement for the US characteristics between the CAD system and senior radiologist were: substantial agreement for hypoechoic and taller than wide (kappa value = 0.66, 0.78), and moderate agreement for irregular margin and micro-calcifications (kappa value = 0.52, 0.42). The CAD system achieved equal diagnostic accuracy to the senior radiologists and higher accuracy than the junior radiologists. The interobserver agreements in the US features between the CAD system and senior radiologist were substantial agreement for hypoechoic and taller than wide; moderate agreement for irregular margin and micro-calcifications. The location of a thyroid nodule and the feature of macrocalcification with wide acoustic shadow may influence the analysis of the CAD system.

Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance

“…After the self-learning process, the final test performance with AI-CAD assistance showed additional increases in sensitivity, AUC, and accuracy. Previous research has well-documented the increased advantage that AI-CAD offers to beginners in US ( 12 , 20 – 24 ). AI-CAD appears to supplement self-learning by offering direct assistance on specific cases, rather than just amplifying the learning effect.…”

Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network

“…The deep convolutional neural network (CNN) is trained with an automated process using raw image pixels rather than engineered features extracted by experts of the traditional machine learning algorithm 7 . For thyroid cancer diagnosis, many machine learning and deep learning techniques have been implemented 8 – 12 . When machine learning techniques using support vector machines were compared with an experienced radiologist, they showed lower accuracy 13 , while deep learning techniques showed similar accuracies to experienced radiologists and higher accuracies than inexperienced radiologists 12 , 14 .…”

“…For thyroid cancer diagnosis, many machine learning and deep learning techniques have been implemented 8 – 12 . When machine learning techniques using support vector machines were compared with an experienced radiologist, they showed lower accuracy 13 , while deep learning techniques showed similar accuracies to experienced radiologists and higher accuracies than inexperienced radiologists 12 , 14 . Recently, we developed a computer-aided program that uses a deep convolutional neural network (CNN) to diagnose thyroid nodules according to US features 14 .…”

Effects of 1 year of training on the performance of ultrasonographic image interpretation: A preliminary evaluation using images of Sjögren syndrome patients

“…Previous studies of US have reported that the diagnostic accuracy of inexperienced radiologists is lower than that of experienced radiologists in the fields of breast, thyroid, and salivary gland imaging. 2 3 4 5 6 In a recent study, inexperienced radiologists were reported to show a lower diagnostic performance for classifying US images of Sjögren syndrome (SjS) than a deep learning artificial intelligence system. 7 …”