THU676 Real-time Assessment Of The Beneficial Role Of Computer-Aided Diagnosis In The Diagnosis Of Thyroid Nodules On Ultrasound

Abstract

Abstract Disclosure: Y. Kim: None. S. Shin: None. E. Lee: None. D. Kim: None. J. Kwak: None. Objective: Computer-aided Diagnosis (CAD) has been applied in various medical fields. SERA (SEveRance Artificial intelligence program) is a deep convolutional neural network algorithm. We conducted this study to evaluate whether CAD-SERA helps diagnose the malignancy when the ultrasound was used by physicians with low levels of experience and to compare with the experienced radiologist. Methods: In real-time, an inexperienced physician and a radiologist simultaneously assessed the same thyroid nodules to determine malignancy and performed CAD. The diagnosis was changed if it was necessary to correct it by referring to the CAD results, and this was compared with the surgical pathology or biopsy pathology. Results: We evaluated 191 thyroid nodules in 179 patients. Area Under the Receiver Operating Characteristics (AUROC) was analyzed with the diagnoses with/without CAD assistance and pathology results. The AUROC of the inexperienced physician was 0.716, and when assisted by CAD that increased to 0.740 (Accuracy 73.82 -> 76.44%). In the case of the radiologist, AUROC without CAD assist was 0.868 and 0.880 with the assist of CAD (Accuracy 84.29 -> 85.86%). Conclusion: CAD improves diagnostic performance in the diagnosis of thyroid nodules in various experienced physicians in ultrasound. In particular, the extent of this is greater in physicians with less experience in ultrasound. Presentation: Thursday, June 15, 2023