Deep learning based on carotid transverse B-mode scan videos for the diagnosis of carotid plaque: a prospective multicenter study
Deep learning based on carotid transverse B-mode scan videos for the diagnosis of carotid plaque: a prospective multicenter study
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DOI：
10.1007/s00330-022-09324-y
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发表时间：
2022-12-13
影响因子：
5.9
通讯作者：
Ren, Jie
中科院分区：
文献类型：
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作者：
Liu, Jia;Zhou, Xinrui;Ren, Jie
目的超声(US)准确检测颈动脉斑块是预防中风的关键。然而，初级放射科医生(在颈动脉超声评估方面有大约一年的经验)的诊断表现相对较差。因此，我们的目标是开发一个基于美国视频的深度学习(DL)模型，以提高初级放射科医生在斑块检测方面的表现。方法在5家医院开展这项多中心前瞻性研究。CaroNet-动态从颈动脉横向超声视频中自动检测颈动脉斑块，从而实现临床检测。模型性能的评估使用专家注释(在颈动脉US评估方面有10年以上的经验)作为基本事实。研究了不同斑块特征和US扫描系统下模型的稳健性。此外，它的临床适用性是通过比较初级放射科医生在有和没有DL模型辅助下的诊断来评估的。结果共对825名患者的1647个视频进行了评估。该模型在内测和多中心外测上的灵敏度分别为87.03%和94.17%，特异度分别为82.07%和74.04%，受试者工作特性曲线下面积分别为0.845和0.841。此外，不同斑块特征和扫描系统之间的性能没有显著差异。使用DL模式后，初级放射科医师的工作表现有显著提高，尤其是在敏感度方面(最大从46.3%提高到94.44%)。结论基于与实际检查相对应的US视频的DL模型显示出稳健的斑块检测性能，并显著提高了初级放射科医生的诊断能力。
Objectives Accurate detection of carotid plaque using ultrasound (US) is essential for preventing stroke. However, the diagnostic performance of junior radiologists (with approximately 1 year of experience in carotid US evaluation) is relatively poor. We thus aim to develop a deep learning (DL) model based on US videos to improve junior radiologists' performance in plaque detection. Methods This multicenter prospective study was conducted at five hospitals. CaroNet-Dynamic automatically detected carotid plaque from carotid transverse US videos allowing clinical detection. Model performance was evaluated using expert annotations (with more than 10 years of experience in carotid US evaluation) as the ground truth. Model robustness was investigated on different plaque characteristics and US scanning systems. Furthermore, its clinical applicability was evaluated by comparing the junior radiologists' diagnoses with and without DL-model assistance. Results A total of 1647 videos from 825 patients were evaluated. The DL model yielded high performance with sensitivities of 87.03% and 94.17%, specificities of 82.07% and 74.04%, and areas under the receiver operating characteristic curve of 0.845 and 0.841 on the internal and multicenter external test sets, respectively. Moreover, no significant difference in performance was noted among different plaque characteristics and scanning systems. Using the DL model, the performance of the junior radiologists improved significantly, especially in terms of sensitivity (largest increase from 46.3 to 94.44%). Conclusions The DL model based on US videos corresponding to real examinations showed robust performance for plaque detection and significantly improved the diagnostic performance of junior radiologists.