Lancet子刊:华中科大:与专家水平相当——深度学习盆腔超声对“女性杀手”卵巢癌的诊断准确性

2022-03-18 MedSci原创 MedSci原创

启用DCNN的超声性能超过了放射科医生的平均诊断水平,与专家级超声图像阅读器的水平相匹配,并增强了放射科医生的准确性。

超声是卵巢癌术前诊断的重要非侵入性检查。深度学习在图像识别任务方面正在取得进展;为此,来自我国华中科技大学附属同济医院的妇产科专家领衔全国多中心旨在开发一个深度卷积神经网络(DCNN)模型,使超声图像的评估自动化,并促进比现有方法更准确的卵巢癌诊断。

在这项回顾性、多中心的诊断研究中,研究人员收集了2003年9月至2019年5月期间全国十家医院的盆腔超声图像。纳入了超声检查中附件病变的连续成年患者(年龄≥18岁)和健康对照组,排除了重复病例和没有附件或病理诊断的患者。

对于DCNN模型的开发,患者被分配到训练数据集(3755名卵巢癌患者的34 488张图像,101777名对照的541442张图像)。对于模型验证,患者被分配到内部验证数据集,外部验证数据集。利用这些数据集评估了DCNN的诊断价值,将DCNN与35名放射科医生进行了比较,并探讨了DCNN是否能提高6名放射科医生的诊断准确性。

对于DCNN检测卵巢癌,内部数据集的AUC为0.911(95% CI 0.886-0.936),外部验证数据集1为0.870(95% CI 0.822-0.918),而外部验证数据集2为0.831(95% CI 0.793-0.869)。在内部数据集(88.8% vs 85.7%)和外部验证数据集1(86.9% vs 81.1%)中,DCNN模型在检测卵巢癌方面比放射科医生更准确。

在DCNN辅助诊断后,诊断的准确性和敏感性比单独由放射科医生评估增加得更多(87.6% [85.0-90.2] vs 78.3% [72.1-84.5];82.7% [78.5-86.9] vs 70.4% [59.1-81.7])。六位放射科医生的DCNN辅助评估的平均准确率达到0.876,并且在DCNN辅助评估时,其准确率明显增强。

综上,启用DCNN的超声性能超过了放射科医生的平均诊断水平,与专家级超声图像阅读器的水平相匹配,并增强了放射科医生的准确性。

 

参考文献:

Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study. https://doi.org/10.1016/S2589-7500(21)00278-8

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    2022-12-12 howi
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