Artificial Intelligence Computer-Assisted Diagnosis for Thyroid Nodules: Comparison of Diagnostic Performance Using Original and Mobile Ultrasonography Images
10.11106/ijt.2023.16.1.111
- Author:
Sangwoo CHO
1
;
Eunjung LEE
;
Hyunju LEE
;
Hye Sun LEE
;
Jung Hyun YOON
;
Vivian Youngjean PARK
;
Miribi RHO
;
Jiyoung YOON
;
Jin Young KWAK
Author Information
1. Yonsei University College of Medicine, Seoul, Korea
- Publication Type:ORIGINAL ARTICLE
- From:International Journal of Thyroidology
2023;16(1):111-119
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background and Objectives:This study investigated whether an artificial intelligence computer-assisted diagnosis (AI-CAD) software recently developed in our institution named the Severance Artificial intelligence program (SERA) could show similar diagnostic performance for thyroid cancers using ultrasonographic (US) images from a mobile phone (SERA_M) compared to using images directly downloaded from the pictures archive and communication system (PACS) (SERA_P).
Materials and Methods:From October 2019 to December 2019, 259 thyroid nodules from 259 patients were included. SERA was run on original and mobile images to evaluate SERA_P and SERA_M. Nodules were categorized according to the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS). To compare diagnostic performance, a logistic regression analysis was conducted using the Generalized Estimating Equation. The area under the curve (AUC) was calculated using the receiver operating characteristic (ROC) curve, and compared using the Delong Method.
Results:There were 40 cancers (15.4%) and 219 benign lesions (84.6%). The AUC and sensitivity of SERA_M (0.82 and 85%, respectively) were not statistically different from SERA_P (0.8 and 75%, respectively) (p=0.526 and p=0.091, respectively). The AUC of radiologists (0.856) was not significantly different compared to SERA_P and SERA_M (p=0.163 and p=0.414, respectively). The sensitivity of radiologists (77.5%) was not statistically different compared to SERA_P and SERA_M (p=0.739 and p=0.361, respectively).
Conclusion:AI-CAD software using pictures taken by a mobile phone showed comparable diagnostic performance with the same software using images directly from PACS.
