用CT生成的X光片，用深度学习精准估算成人的性别、年龄、身高和体重。
Deep Learning Estimation of Sex, Age, Height, and Weight from CT-derived Digitally Reconstructed Radiographs
- 集成三个深度模型，通过加权平均提升估计精度。
- 性别识别准确率99.7%，年龄误差仅3.57年，身高体重误差均小于3.5厘米和千克。
- 在覆盖胸到盆部的图像上表现更优，适合临床快速获取人体参数。
目的：开发并验证一种深度学习集成模型，用于从诊断性CT生成的冠状面数字化重建放射图像（DRRs）中估计成人性别、年龄、身高和体重。方法：本回顾性研究纳入日本九家机构的128,621例成人CT检查（共80,004人）。采用三种多任务模型——ConvNeXt-Base、ViT-Base/16和MaxViT-Base——在冠状面DRRs上进行微调，并通过加权平均组合。数据按机构划分为训练集（114,147例；七家机构）、调参集（4,305例；一家机构）和测试集（10,169例；一家机构），并在两个非日本数据集上评估泛化能力。使用准确率评估性别分类，均方误差（MAE）评估年龄、身高和体重回归。以真实值与估计值计算体表面积（BSA），比较心脏和肝脏体积随年龄变化的趋势。结果：在测试集（中位年龄69.9岁，4,899/10,169为男性，占比48.2%）中，总体性别分类准确率为0.997（95%置信区间：0.996–0.998），年龄、身高和体重的平均绝对误差（MAE）分别为3.57年（3.51–3.63）、2.59厘米（2.54–2.64）和3.40公斤（3.34–3.47）。在覆盖胸至盆部的检查中，准确率达1.000，对应MAE分别为3.15年、2.28厘米和3.18公斤。基于估计身高体重计算的体表面积，能复现真实值下心肝体积随年龄的变化趋势。在非日本数据集上，身高误差有所上升，但通过持续微调可降低。结论：该集成模型能有效从CT衍生的DRRs中估计成人性别、年龄、身高和体重，尤其在解剖覆盖范围更广的图像上误差更低。
原文摘要 · Abstract (English)
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.
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