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Learnability of Thyroid Nodule Assessment on Ultrasonography: Using a Big Data Set

Authors
 Jiyoung Yoon  ;  Eunjung Lee  ;  Hye Sun Lee  ;  Sangwoo Cho  ;  JinWoo Son  ;  Hyuk Kwon  ;  Jung Hyun Yoon  ;  Vivian Youngjean Park  ;  Minah Lee  ;  Miribi Rho  ;  Daham Kim  ;  Jin Young Kwak 
Citation
 ULTRASOUND IN MEDICINE AND BIOLOGY, Vol.49(12) : 2581-2589, 2023-12 
Journal Title
ULTRASOUND IN MEDICINE AND BIOLOGY
ISSN
 0301-5629 
Issue Date
2023-12
MeSH
Artificial Intelligence ; Big Data ; Humans ; Retrospective Studies ; Sensitivity and Specificity ; Thyroid Nodule* / pathology ; Ultrasonography / methods
Keywords
Artificial intelligence ; Big data ; Education ; Thyroid nodule ; Ultrasonography
Abstract
Objective: The aims of the work described here were to evaluate the learnability of thyroid nodule assessment on ultrasonography (US) using a big data set of US images and to evaluate the diagnostic utilities of artificial intelligence computer-aided diagnosis (AI-CAD) used by readers with varying experience to differentiate benign and malignant thyroid nodules.

Methods: Six college freshmen independently studied the "learning set" composed of images of 13,560 thyroid nodules, and their diagnostic performance was evaluated after their daily learning sessions using the "test set" composed of images of 282 thyroid nodules. The diagnostic performance of two residents and an experienced radiologist was evaluated using the same "test set." After an initial diagnosis, all readers once again evaluated the "test set" with the assistance of AI-CAD.

Results: Diagnostic performance of almost all students increased after the learning program. Although the mean areas under the receiver operating characteristic curves (AUROCs) of residents and the experienced radiologist were significantly higher than those of students, the AUROCs of five of the six students did not differ significantly compared with that of the one resident. With the assistance of AI-CAD, sensitivity significantly increased in three students, specificity in one student, accuracy in four students and AUROC in four students. Diagnostic performance of the two residents and the experienced radiologist was better with the assistance of AI-CAD.

Conclusion: A self-learning method using a big data set of US images has potential as an ancillary tool alongside traditional training methods. With the assistance of AI-CAD, the diagnostic performance of readers with varying experience in thyroid imaging could be further improved.
Full Text
https://www.sciencedirect.com/science/article/pii/S0301562923002880
DOI
10.1016/j.ultrasmedbio.2023.08.026
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Kwak, Jin Young(곽진영) ORCID logo https://orcid.org/0000-0002-6212-1495
Kim, Daham(김다함) ORCID logo https://orcid.org/0000-0003-1871-686X
Rho, Miribi(노미리비) ORCID logo https://orcid.org/0000-0002-1703-7657
Park, Vivian Youngjean(박영진) ORCID logo https://orcid.org/0000-0002-5135-4058
Yoon, Jung Hyun(윤정현) ORCID logo https://orcid.org/0000-0002-2100-3513
Yoon, Jiyoung(윤지영) ORCID logo https://orcid.org/0000-0003-2266-0803
Lee, Minah(이민아)
Lee, Hye Sun(이혜선) ORCID logo https://orcid.org/0000-0001-6328-6948
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/196615
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