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Deep Learning Techniques for Clinical Evaluation of Computed Tomography Angiographic Images of Intracranial Aneurysm

By 4th December 2021No Comments

The following study was conducted by Scientists from Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China; Department of Radiology, Lianyungang First People’s Hospital, Lianyungang, Jiangsu, China; Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Computer Science Department, School of EECS, Peking University, Beijing, China; Department of Radiology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China; DeepWise AI lab., Beijing, China; Department of Radiology, Tianjin First Central Hospital, Tianjin, China; Department of Neurosurgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China; Department of Diagnostic Radiology, Jinling Hospital, Sothern Medical University, Nanjing, Jiangsu, China. Study is published in Nature Communications Journal as detailed below.

Nature Communications; Volume 11, Article Number: 6090 (2020)

A Clinically Applicable Deep-Learning Model for Detecting Intracranial Aneurysm in Computed Tomography Angiography Images


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.


Nature Communications



Shi, Z., Miao, C., Schoepf, U.J. et al. A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images. Nat Commun 11, 6090 (2020).