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首页> 外文期刊>Nature Communications >A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images
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A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

机译:检测计算机断层造影图像中颅内动脉瘤的临床应用深度学习模型

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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.
机译:颅内动脉瘤是一种常见的危及生命的疾病。计算断层造影血管造影建议作为标准诊断工具;然而,解释可能是耗时和挑战性的。我们介绍了一个特定的深度学习的模型,培训了1,177数字减法血管造影验证验证骨移除计算断层造影血管造影案例。该模型对图像质量具有良好的耐受性,并用不同的制造商进行测试。模拟实际研究是在连续的内部和外部队列中进行的,其中它与放射科学家和专家神经外部相比,它实现了改善的患者水平敏感性和病变水平敏感性。使用特定的疑似急性缺血性卒中群体,发现99.0%的预测阴性案件可以达到高信任,导致人类工作量的潜在减少。经验预期研究是为了确定算法是否可以与临床医生的评估相比改善患者的护理。

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