AI 中文总结
研究旨在开发深度学习集成方法从CT衍生DRR估计成人性别、年龄、身高和体重。用三个多任务模型微调后加权平均组合，在多组数据上测试评估。结果表明该方法能有效估计，解剖覆盖广时误差低。
AI 中文摘要
目的：开发并验证一种深度学习集成方法，用于从诊断性CT生成的冠状位数字重建X线片（DRR）中估计成人性别、年龄、身高和体重。材料与方法：这项回顾性研究纳入了日本9家机构80004名成年人的128621次CT检查。使用冠状位DRR对三个多任务模型——ConvNeXt-Base、ViT-Base/16和MaxViT-Base进行微调，并通过加权平均进行组合。数据按机构分为训练集（114147次检查；7家机构）、调整集（4305次；1家机构）和测试集（10169次；1家机构）；在两个非日本数据集上评估泛化能力。分别用准确率和平均绝对误差（MAE）评估性别分类以及年龄、身高和体重回归。使用真实值与估计值比较体表面积（BSA）校正后的心脏和肝脏体积趋势。结果：在测试集（中位年龄69.9岁；10169例中的4899例[48.2%]为男性）中，总体性别分类准确率为0.997（95%CI，0.996 - 0.998），年龄、身高和体重的MAE分别为3.57岁（3.51 - 3.63）、2.59厘米（2.54 - 2.64）和3.40千克（3.34 - 3.47）。在覆盖胸部至骨盆的检查中，准确率为1.000，MAE分别为3.15岁、2.28厘米和3.18千克。根据估计值计算的BSA重现了使用真实值获得的与年龄相关的心肝体积趋势。在非日本数据集上，身高误差增加，但通过持续微调得以降低。结论：该集成方法可从CT衍生的DRR中估计成人性别、年龄、身高和体重，在解剖覆盖范围更广的检查中误差通常较低。
英文摘要
Purpose: To develop and validate a deep learning ensemble for estimating adult sex, age, height, and weight from coronal digitally reconstructed radiographs (DRRs) generated from diagnostic CT. Materials and Methods: This retrospective study included 128,621 CT examinations from 80,004 adults at nine institutions in Japan. Three multitask models-ConvNeXt-Base, ViT-Base/16, and MaxViT-Base-were fine-tuned using coronal DRRs and combined by weighted averaging. Data were split by institution into training (114,147 examinations; seven institutions), tuning (4,305; one institution), and test (10,169; one institution) sets; generalizability was assessed on two non-Japanese datasets. Accuracy and mean absolute error (MAE) were used to evaluate sex classification and age, height, and weight regression, respectively. Body surface area (BSA)-corrected heart and liver volume trends were compared using true versus estimated height and weight. Results: In the test set (median age, 69.9 years; 4,899 of 10,169 [48.2%] male), overall sex-classification accuracy was 0.997 (95% CI, 0.996-0.998), and MAEs were 3.57 years (3.51-3.63), 2.59 cm (2.54-2.64), and 3.40 kg (3.34-3.47) for age, height, and weight, respectively. In examinations covering the chest through pelvis, accuracy was 1.000, and MAEs were 3.15 years, 2.28 cm, and 3.18 kg, respectively. BSA calculated from estimated values reproduced age-related heart and liver volume trends obtained using true values. On non-Japanese datasets, height error increased but was reduced by continued fine-tuning. Conclusion: The ensemble estimated adult sex, age, height, and weight from CT-derived DRRs, with generally lower errors in examinations with broader anatomical coverage.
CommentsCode: https://github.com/jichi-labo/DRRBiometricsPredictor
