Langner Taro, Martínez Mora Andrés, Strand Robin, Ahlström Håkan, Kullberg Joel
Departments of Surgical Sciences (T.L., A.M.M., R.S., H.A., J.K.) and Information Technology (R.S.), Uppsala University, Akademiska sjukhuset, ingång 78, 1tr, 751 85 Uppsala, Sweden; and Antaros Medical AB, Mölndal, Sweden (H.A., J.K.).
Radiol Artif Intell. 2022 Apr 6;4(3):e210178. doi: 10.1148/ryai.210178. eCollection 2022 May.
UK Biobank (UKB) has recruited more than 500 000 volunteers from the United Kingdom, collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Ongoing medical imaging of 100 000 participants with 70 000 follow-up sessions will yield up to 170 000 MRI scans, enabling image analysis of body composition, organs, and muscle. This study presents an experimental inference engine for automated analysis of UKB neck-to-knee body 1.5-T MRI scans. This retrospective cross-validation study includes data from 38 916 participants (52% female; mean age, 64 years) to capture baseline characteristics, such as age, height, weight, and sex, as well as measurements of body composition, organ volumes, and abstract properties, such as grip strength, pulse rate, and type 2 diabetes status. Prediction intervals for each end point were generated based on uncertainty quantification. On a subsequent release of UKB data, the proposed method predicted 12 body composition metrics with a 3% median error and yielded mostly well-calibrated individual prediction intervals. The processing of MRI scans from 1000 participants required 10 minutes. The underlying method used convolutional neural networks for image-based mean-variance regression on two-dimensional representations of the MRI data. An implementation was made publicly available for fast and fully automated estimation of 72 different measurements from future releases of UKB image data. MRI, Adipose Tissue, Obesity, Metabolic Disorders, Volume Analysis, Whole-Body Imaging, Quantification, Supervised Learning, Convolutional Neural Network (CNN) © RSNA, 2022.
英国生物银行（UKB）从英国招募了超过50万名志愿者，收集了有关遗传学、生活方式、血液生化等与健康相关的信息。对10万名参与者进行的正在进行的医学成像以及7万次随访将产生多达17万次MRI扫描，从而能够对身体成分、器官和肌肉进行图像分析。本研究提出了一种用于自动分析UKB颈至膝部身体1.5-T MRI扫描的实验推理引擎。这项回顾性交叉验证研究纳入了38916名参与者的数据（52%为女性；平均年龄64岁），以获取基线特征，如年龄、身高、体重和性别，以及身体成分测量、器官体积和抽象属性，如握力、脉搏率和2型糖尿病状态。基于不确定性量化生成每个终点的预测区间。在随后发布的UKB数据中，所提出的方法预测了12项身体成分指标，中位数误差为3%，并且产生的个体预测区间大多校准良好。处理1000名参与者的MRI扫描需要10分钟。底层方法使用卷积神经网络对MRI数据的二维表示进行基于图像的均值-方差回归。已公开提供了一个实现，用于对UKB图像数据未来版本中的72种不同测量进行快速且全自动的估计。MRI、脂肪组织、肥胖症、代谢紊乱、体积分析、全身成像、量化、监督学习、卷积神经网络（CNN） © RSNA，2022年
