A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.
Longjiang ZhangChongchang MiaoU Joseph SchoepfRock H SavageDanielle M DargisChengwei PanXue ChaiXiu Li LiShuang XiaXin ZhangYan GuYonggang ZhangBin HuWenda XuChangsheng ZhouSong LuoHao WangLi MaoKongming LiangLili WenLongjiang ZhouYizhou YuGuang Ming LuLong Jiang ZhangPublished in: Nature communications (2020)
Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to clinicians' assessment.
Keyphrases
- deep learning
- image quality
- coronary artery
- acute ischemic stroke
- artificial intelligence
- computed tomography
- palliative care
- convolutional neural network
- machine learning
- optical coherence tomography
- healthcare
- endothelial cells
- ejection fraction
- dual energy
- pulmonary embolism
- optic nerve
- quality improvement
- soft tissue
- pain management
- bone mineral density
- risk assessment