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Article type: Research Article
Authors: Kim, Chang Hoa; 1 | Hahm, Myong Hunb; 1 | Lee, Dong Euna | Choe, Jae Younga | Ahn, Jae Yuna | Park, Sin-Youlc | Lee, Suk Heed | Kwak, Youngseoke | Yoon, Sang-Youle | Kim, Ki-Hongf | Kim, Myungsooe | Chang, Sung Hyune | Son, Jeongwooc | Cho, Junghwang | Park, Ki-Sue; * | Kim, Jong Kuna; *
Affiliations: [a] Department of Emergency Medicine, School of Medicine, Kyungpook National University, Daegu, Korea | [b] Department of Radiology, School of Medicine, Kyungpook National University, Daegu, Korea | [c] Department of Emergency Medicine College of Medicine, Yeungnam University, Daegu, Korea | [d] Department of Emergency Medicine Daegu Catholic University Medical Center, Daegu, Korea | [e] Department of Neurosurgery, School of Medicine, Kyungpook National University, Daegu, Korea | [f] Department of Neurosurgery, School of Medicine of Daegu Catholic University, Daegu, Korea | [g] CAIDE Systems Inc., USA
Correspondence: [*] Corresponding authors: Ki-Su Park, Department of Neurosurgery, School of Medicine, Kyungpook National University, 130 Dongdeok-ro, Jung-gu, Daegu 41944, Korea. E-mail: kiss798@gmail.com. Jong Kun Kim, Department of Emergency Medicine, School of Medicine, Kyungpook National University, 130 Dongdeok-ro, Jung-gu, Daegu 41944, Korea. E-mail: kim7155@knu.ac.kr.
Note: [1] Chang Ho Kim and Myong Hun Hahm contributed equally to this work.
Abstract: BACKGROUND:Doctors with various specializations and experience order brain computed tomography (CT) to rule out intracranial hemorrhage (ICH). Advanced artificial intelligence (AI) can discriminate subtypes of ICH with high accuracy. OBJECTIVE: The purpose of this study was to investigate the clinical usefulness of AI in ICH detection for doctors across a variety of specialties and backgrounds. METHODS: A total of 5702 patients’ brain CTs were used to develop a cascaded deep-learning-based automated segmentation algorithm (CDLA). A total of 38 doctors were recruited for testing and categorized into nine groups. Diagnostic time and accuracy were evaluated for doctors with and without assistance from the CDLA. RESULTS: The CDLA in the validation set for differential diagnoses among a negative finding and five subtypes of ICH revealed an AUC of 0.966 (95% CI, 0.955–0.977). Specific doctor groups, such as interns, internal medicine, pediatrics, and emergency junior residents, showed significant improvement with assistance from the CDLA (p= 0.029). However, the CDLA did not show a reduction in the mean diagnostic time. CONCLUSIONS: Even though the CDLA may not reduce diagnostic time for ICH detection, unlike our expectation, it can play a role in improving diagnostic accuracy in specific doctor groups.
Keywords: Intracranial hemorrhages, diagnosis, artificial intelligence, deep learning, ROC curve
DOI: 10.3233/THC-202533
Journal: Technology and Health Care, vol. 29, no. 5, pp. 881-895, 2021
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