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• W3134700476 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." @default.
• W3134700476 created "2021-03-15" @default.
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• W3134700476 date "2021-09-06" @default.
• W3134700476 modified "2023-10-16" @default.
• W3134700476 title "Clinical usefulness of deep learning-based automated segmentation in intracranial hemorrhage" @default.
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• W3134700476 doi "https://doi.org/10.3233/thc-202533" @default.
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