Objective: To explore the diagnostic efficiency of K-TIRADS, ACR-TIRADS and ATA risk stratification in computer-aided detection and diagnosis(CAD) software and the application value of CAD-assisted ultrasound physicians in diagnosing thyroid nodules. Methods: One hundred and ninety-two thyroid nodules with postoperative pathological results were retrospectively analyzed. All of them were graded by K-TIRADS, ACR-TIRADS and ATA with CAD software, and the best guide was recognized by calculating the area under the ROC curve, sensitivity and specificity. Then, based on the best guidelines for the classification criteria, the double-blind method was used to compare the ability of the same ultrasonologist to diagnose thyroid nodules before and after CAD. Results: The AUC value of K-TIRADS, ACR-TIRADS, ATA was 0.88, 0.77, 0.62 respectively in the CAD software. The difference between the two groups was statistically significant (P<0.05). There was no significant difference in the specificity between K-TIRADS and ATA(P=0.176), which were both higher than ACR-TIRADS with statistically significant differences (P<0.05). The AUC value of the diagnosis among CAD itself, ultrasound physicians and physicians combined CAD was 0.88, 0.80, 0.93, respectively. The difference between the two groups was statistically significant (P<0.05). There was no significant difference in the sensitivity between CAD itself and physicians combined CAD(P=0.163), which were both higher than ultrasound physicians with statistical significant differences(P<0.05). Among ultrasound physicians, CAD itself and physicians combined CAD, the difference in specificity between the two groups was statistically significant(P<0.05). Conclusions All the three risk stratification systems of thyroid ultrasound in CAD software have good diagnostic values, among which K-TIRADS has the largest AUC. The CAD software can assist ultrasound physicians to improve the thyroid nodule diagnostic performance, and has a good clinical application prospect.

Objective To explore the diagnostic efficiency of K‐T IRADS ,ACR‐T IRADS and AT A risk stratification in computer‐aided detection and diagnosis ( CAD ) software and the application value of CAD‐assisted ultrasound physicians in diagnosing thyroid nodules . Methods One hundred and ninety‐two thyroid nodules with postoperative pathological results were retrospectively analyzed . All of them were graded by K‐T IRADS ,ACR‐T IRADS and A T A with CAD software ,and the best guide was recognized by calculating the area under the ROC curve ,sensitivity and specificity . T hen ,based on the best guidelines for the classification criteria , the double‐blind method was used to compare the ability of the same ultrasonologist to diagnose thyroid nodules before and after CAD . Results T he AUC value of K‐T IRADS , ACR‐T IRADS ,A T A was 0 .88 ,0 .77 ,0 .62 respectively in the CAD software . T he difference between the two groups was statistically significant ( P ＜0 .05 ) . T here was no significant difference in the specificity between K‐T IRADS and A T A ( P ＝0 .176 ) ,w hich were both higher than ACR‐T IRADS with statistically significant differences ( P ＜ 0 .05 ) . T he AUC value of the diagnosis among CAD itself , ultrasound physicians and physicians combined CAD was 0 .88 ,0 .80 ,0 .93 ,respectively . T he difference between the two groups was statistically significant ( P ＜0 .05) . T here was no significant difference in the sensitivity between CAD itself and physicians combined CAD ( P ＝0 .163 ) ,w hich were both higher than ultrasound physicians with statistical significant differences( P ＜0 .05) . Among ultrasound physicians ,CAD itself and physicians combined CAD ,the difference in specificity between the two groups was statistically significant ( P ＜0 .05) . Conclusions All the three risk stratification systems of thyroid ultrasound in CAD software have good diagnostic values ,among w hich K‐T IRADS has the largest AUC . T he CAD software can assist ultrasound physicians to improve the thyroid nodule diagnostic performance , and has a good clinical application prospect .