Diagnostic accuracy and time efficiency of a novel deep learning algorithm for the assessment of intracranial hemorrhage
Author Block: C. Booz1, T. Vogl1, V. Koch1, L. D. Gruenewald1, A-I. Nica1, T. D'Angelo2, M. Dimitrova1, G. M. Bucolo1, I. Yel1; 1Frankfurt/DE, 2Messina/IT
Purpose: To evaluate diagnostic accuracy and time efficiency of a deep learning-based pipeline using a Dense U-net architecture for the assessment of intracranial hemorrhage (ICH) in unenhanced head CT scans.
Methods or Background: This retrospective study included 1004 CT scans of 1004 patients (mean age, 71 ± 11 years; 496 men and 508 women) who had undergone an unenhanced head CT scan for the assessment of ICH. All CT scans were analyzed by the algorithm and a board-certified radiologist independently for the presence of ICH. In case of ICH presence, ICH had to be defined as intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), subarachnoid hemorrhage (SAH), subdural hemorrhage (SDH) and epidural hemorrhage (EDH). Additionally, the time until first temporary diagnosis of ICH was measured. Three experienced board-certified radiologists analyzed the CT scans in consensus reading sessions to establish the standard of reference for hemorrhage presence and classification.
Results or Findings: The reference standard revealed a total of 1108 different ICH presences (IPH, n=344; IVH, n=52; SAH, n=326; SDH, n=356; EDH, n=30). The algorithm showed a high diagnostic accuracy for the assessment of ICH with a sensitivity of 92%, specificity of 95% and an accuracy of 93%. Concerning the most frequently present different ICH types in this study, the sensitivity was 92%, 93% and 93% (IPH, SAH and SDH, respectively), and the specificity was 95%, 96% and 95% (IPH, SAH and SDH, respectively). Regarding analysis time, the algorithm was significantly faster compared to the temporary report of the assigned radiologist (16 ± 3 s vs 273 ± 11 s, p < 0.001).
Conclusion: A novel deep learning algorithm provides high diagnostic accuracy combined with time efficiency for the identification and classification of ICH in unenhanced CT scans.
Limitations: Single-center retrospective study
Funding for this study: No funding was received.
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: The local IRB approved this study.