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Construction of a Standard Dataset for Liver Tumors for Testing the Performance and Safety of Artificial Intelligence-Based Clinical Decision Support Systems

Other Titles
 인공지능 기반 임상의학 결정 지원 시스템 의료기기의 성능 및 안전성 검증을 위한 간 종양 표준 데이터셋 구축 
 Seung-seob Kim  ;  Dong Ho Lee  ;  Min Woo Lee  ;  So Yeon Kim  ;  Jaeseung Shin  ;  Jin‑Young Choi  ;  Byoung Wook Choi 
 Journal of the Korean Society of Radiology (대한영상의학회지), Vol.82(5) : 1186-1196, 2021-09 
Journal Title
Journal of the Korean Society of Radiology(대한영상의학회지)
Issue Date
Liver Neoplasms ; Aritificial Intelligence ; Machine Learning ; Deep Learning ; Datasets as Topic
To construct a standard dataset of contrast-enhanced CT images of liver tumors to test the performance and safety of artificial intelligence (AI)-based algorithms for clinical decision support systems (CDSSs).

Materials and Methods
A consensus group of medical experts in gastrointestinal radiology from four national tertiary institutions discussed the conditions to be included in a standard dataset. Seventy-five cases of hepatocellular carcinoma, 75 cases of metastasis, and 30–50 cases of benign lesions were retrieved from each institution, and the final dataset consisted of 300 cases of hepatocellular carcinoma, 300 cases of metastasis, and 183 cases of benign lesions. Only pathologically confirmed cases of hepatocellular carcinomas and metastases were enrolled. The medical experts retrieved the medical records of the patients and manually labeled the CT images. The CT images were saved as Digital Imaging and Communications in Medicine (DICOM) files.

The medical experts in gastrointestinal radiology constructed the standard dataset of contrast-enhanced CT images for 783 cases of liver tumors. The performance and safety of the AI algorithm can be evaluated by calculating the sensitivity and specificity for detecting and characterizing the lesions.

The constructed standard dataset can be utilized for evaluating the machine-learningbased AI algorithm for CDSS.
Files in This Item:
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Seung-seob(김승섭) ORCID logo https://orcid.org/0000-0001-6071-306X
Shin, Jaeseung(신재승) ORCID logo https://orcid.org/0000-0002-6755-4732
Choi, Byoung Wook(최병욱) ORCID logo https://orcid.org/0000-0002-8873-5444
Choi, Jin Young(최진영) ORCID logo https://orcid.org/0000-0002-9025-6274
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