Owing to improvements in image recognition via deep learning, machine-learning algorithms could eventually be applied to automated medical diagnoses that can guide clinical decision-making. However, these algorithms remain a ‘black box’ in terms of how they generate the predictions from the input data. Also, high-performance deep learning requires large, high-quality training datasets. Here, we report the development of an understandable deep-learning system that detects acute intracranial haemorrhage (ICH) and classifies five ICH subtypes from unenhanced head computed-tomography scans. By using a dataset of only 904 cases for algorithm training, the system achieved a performance similar to that of expert radiologists in two independent test datasets containing 200 cases (sensitivity of 98% and specificity of 95%) and 196 cases (sensitivity of 92% and specificity of 95%). The system includes an attention map and a prediction basis retrieved from training data to enhance explainability, and an iterative process that mimics the workflow of radiologists. Our approach to algorithm development can facilitate the development of deep-learning systems for a variety of clinical applications and accelerate their adoption into clinical practice.
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The training, validation and test datasets generated for this study are protected patient information. Some data may be available for research purposes from the corresponding author upon reasonable request.
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The authors would like to acknowledge NVIDIA for the use of a DevBox and providing feedback and support, which made this work possible. R.G.G. is funded in part by an NIH U01 grant under the grant number 5U01EB025153.
M.H.L. is a consultant of GE Healthcare and Takeda Pharmaceutical Company and receives an institutional research support from Siemens Healthcare. S.R.P. is a consultant of GE Healthcare. S.D. is a consultant of Nulogix and Doai and receives research supports from ZCAI, Tplus and MediBloc. The remaining authors declare no competing interests.
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Lee, H., Yune, S., Mansouri, M. et al. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng 3, 173–182 (2019). https://doi.org/10.1038/s41551-018-0324-9
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