Impact of Deep Learning-Based Computer-Aided Detection and Electronic Notification System for Pneumothorax on Time to Treatment: Clinical Implementation.

Journal: Journal of the American College of Radiology : JACR
PMID: 39566875

Abstract

OBJECTIVE: To assess whether the implementation of deep learning (DL) computer-aided detection (CAD) that screens for suspected pneumothorax (PTX) on chest radiography (CXR) combined with an electronic notification system (ENS) that simultaneously alerts both the radiologist and the referring clinician would affect time to treatment (TTT) in a real-world clinical practice.

Authors

  • Si Nae Oh
    Clinical Assistant Professor, Department of Family Medicine, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea. Electronic address: osinae@nhimc.or.kr.
  • Hyungkook Yang
    Director, Clinical Management Team, Lunit Care Inc, Seoul, Republic of Korea.
  • Chun Kyon Lee
    Professor, Department of Internal Medicine, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea.
  • Sang-Hoon Park
    Department of Media Engineering, Catholic University of Korea, 43-1, Yeoggok 2-dong, Wonmmi-gu, Bucheon-si, Gyeonggi-do 14662, Korea. timesea821@naver.com.
  • Chang Hoon Han
    Professor, Department of Internal Medicine, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea.
  • Ho Heo
    Professor, Department of General Surgery, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea.
  • Young Sung Kim
    Professor, Department of Family Medicine, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea.

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