【摘要】 构建了一种基于深度学习的自动诊断和分类青少年特发性脊柱侧凸的模型。该模型包括关键点检测和Cobb角测量。回顾性收集站立位脊柱全长X线图像共748例，其中602例用于训练和优化模型，146例用于测试模型性能。结果表明该模型具有较好的诊断和分类性能，准确率为94.5%，其Cobb角的测量结果与专家测量结果相比，94.9%都在临床可接受范围内，平均绝对误差为2.1°，一致性也较好（r2≥0.955 2,P<0.001）。未来该模型可应用于临床，提高医生诊断效率。更多还原

【Abstract】 A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed.This model mainly included key points detection and Cobb angle measurement.748 full-length standing spinal X-ray images were retrospectively collected,of which 602 images were used to train and validate the model,and 146 images were used to test the model performance.The results showed that the model had good diagnostic and classification performance,with an accuracy of94.5%.Compared with experts’measurement,94.9%of its Cobb angle measurement results were within the clinically acceptable range.The average absolute difference was 2.1°,and the consistency was also excellent (r2≥0.955 2,P<0.001).In the future,this model could be applied clinically to improve doctors’diagnostic efficiency.更多还原