School of Biomedical Engineering and Imaging Sciences, King's College London, Rayne Institute, 4th Floor, Lambeth Wing, London SE17 7EH, United Kingdom.
King's College Hospital NHS Foundation Trust, United Kingdom.
Neuroimage. 2022 Apr 1;249:118871. doi: 10.1016/j.neuroimage.2022.118871. Epub 2022 Jan 5.
Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility. Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 'radiologically normal for age' head MRI examinations from two large UK hospitals for model training and testing (age range = 18-95 years), and demonstrate fast (< 5 s), accurate (mean absolute error [MAE] < 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE < 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy 'excessive for age'. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p < 0.0001). Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.
卷积神经网络 (CNN) 可以从结构磁共振成像 (MRI) 脑扫描中准确预测健康个体的实际年龄。这些模型有可能在常规临床检查中应用，以检测健康衰老的偏差，包括早期神经退行性变。这对于患者护理、药物开发和优化 MRI 数据采集都具有重要意义。然而，现有的大脑年龄模型通常是针对不属于常规检查的扫描进行优化的（例如，容积 T1 加权扫描），概括能力较差（例如，适用于来自不同扫描仪供应商和医院的数据等），或者依赖于计算成本高昂的预处理步骤，限制了实时临床应用。在这里，我们试图开发一种适合在常规临床头部 MRI 检查中使用的大脑年龄框架。我们使用基于深度学习的神经放射学报告分类器，从两家英国大型医院的 23302 份“年龄相符的放射学正常”头部 MRI 检查中生成了一个数据集，用于模型训练和测试（年龄范围为 18-95 岁），并展示了从临床级、最小处理的轴向 T2 加权和轴向弥散加权扫描中快速（<5 秒）、准确（平均绝对误差 [MAE] <4 岁）的年龄预测，并且在医院和扫描仪供应商之间具有通用性（Δ MAE <1 岁）。通过 228 名患者的 MRI 报告，我们测试了这些大脑年龄预测的临床相关性，这些患者的 MRI 报告由神经放射科医生独立报告为“年龄过大的萎缩”。这些患者的大脑预测年龄明显高于实际年龄（平均预测年龄差异=+5.89 岁，“年龄相符的放射学正常”平均预测年龄差异=+0.05 岁，p<0.0001）。我们的大脑年龄框架证明了在常规医院检查中用作筛查工具的可行性，以便实时自动检测年龄较大的大脑，这对于临床决策和优化患者路径具有重要意义。
