Searchable abstracts of presentations at key conferences in endocrinology
Endocrine Abstracts (2023) 92 PS1-05-07 | DOI: 10.1530/endoabs.92.PS1-05-07

ETA2023 Poster Presentations Thyroid hormone diagnostics 1 (9 abstracts)

Real-Time assessment of the beneficial role of artifical intelligence-based computer assisted diagnosis (AI-CAD) of thyroid nodules on ultrasound

Youngsook Kim 1 , Miribi Rho 2 , Sungjae Shin 3 , Eunjung Lee 4 , Daham Kim 1 & Jin Young Kwak 2


1Severance Hospital, Institute of Endocrine Research, Yonsei University College of Medicine, Department of Internal Medicine, Seoul, Korea, Rep. of South; 2Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Department of Radiology, Seoul, Korea, Rep. of South; 3National Health Insurance Service Ilsan Hospital, Department of Internal Medicine, Goyang, Korea, Rep. of South; 4Yonsei University, School of Mathematics and Computing (Computational Science and Engineering), Seoul, Korea, Rep. of South


Objective: The purpose of this study is to evaluate and compare the effectiveness of artificial intelligence-based computer-assisted diagnosis (AI-CAD) in diagnosing thyroid malignancy by inexperienced physicians and experienced radiologist.

Methods: A total of 201 thyroid nodules from 192 patients was simultaneously evaluated by physician and radiologist using real-time ultrasound. After implementing AI-CAD, they reassessed the thyroid nodules. If necessary, the diagnosis was changed by referring to the AI-CAD results. Diagnostic performances of them with or without AI-CAD were calculated and compared.

Results: The sensitivity, specificity, diagnostic accuracy, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristics (AUROC) was analyzed with/without AI-CAD assistance. Without AI-CAD, the area under the receiver operating characteristics curve (AUROC) of the radiologist (0.799) was higher than that of the inexperienced physician (0.704). With the assistance of AI-CAD, the AUROC increased to 0.814 for the radiologist and 0.729 for the inexperienced physician. Both of radiologist and inexperienced physician showed increased sensitivity (70.37% vs 73.33%, 75.56% vs 80.74%), diagnostic accuracy (76.62% to 78.61% vs 72.64% to 75.62%), PPV (93.14% to 93.40% vs 81.60% to 82.58%), NPV (59.60% to 62.11% vs 56.58% to 62.32%) with aid of AI-CAD, while specificity remained unchanged (89.39% vs 65.15%).

Conclusion: The diagnostic performance in differentiating thyroid nodules can be further improved with the assistance of AI-CAD, regardless of the level of experience, particularly for inexperienced physicians.

Volume 92

45th Annual Meeting of the European Thyroid Association (ETA) 2023

European Thyroid Association 

Browse other volumes

Article tools

My recent searches

No recent searches.