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Clinical usefulness of deep learning-based automated segmentation in intracranial hemorrhage
Chang Ho Kim1,1, Myong Hun Hahm2,1, Dong Eun Lee1
1Department of Emergency Medicine, School of Medicine, Kyungpook National University, Daegu, Korea.
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.
