用深度学习自动评估老年人脑萎缩，准确度媲美专业医生。
Validation of a CT-brain analysis tool for measuring global cortical atrophy in older patient cohorts
- 开发深度学习模型自动计算脑萎缩评分（GCA），无需人工干预。
- 模型误差平均3.2分，与两名医生评分差异不显著（p>0.3）。
- 结果可用于大规模健康研究，有望实现临床点检应用。
目前脑萎缩量化依赖耗时的视觉评分，亟需自动化分析。本研究验证了自主开发的深度学习（DL）工具在测量老年患者（>65岁）全局脑萎缩（GCA）评分方面的有效性。使用急性内科（ORCHARD-EPR）、急性卒中（OCS）及历史样本的CT脑扫描数据，按60/20/20比例分为训练、调优和测试集。由两位受训医生评分（rater-1：864例，rater-2：20例）。通过平均绝对误差（MAE）和加权卡帕系数评估模型预测值与人工评分的一致性。总体上，模型与rater-1的MAE为3.2，其中ORCHARD-EPR为3.1，OCS为3.3，历史样本为2.6，约一半预测误差在-2至2之间。模型与rater-1的卡帕值为0.45，与rater-2为0.41，而两位医生间为0.28。单因素方差分析显示模型与两医生无显著差异（p=0.35），配对t检验亦表明模型与任一医生均无显著差异（均p>0.18）。GCA评分与年龄及认知功能评分均呈显著正相关（均p<0.001）。该深度学习工具可在真实世界扫描中准确、全自动地测量脑萎缩，适用于大规模健康数据研究，并为临床级点检工具提供可行性证明。
原文摘要 · Abstract (English)
Quantification of brain atrophy currently requires visual rating scales which are time consuming and automated brain image analysis is warranted. We validated our automated deep learning (DL) tool measuring the Global Cerebral Atrophy (GCA) score against trained human raters, and associations with age and cognitive impairment, in representative older (>65 years) patients. CT-brain scans were obtained from patients in acute medicine (ORCHARD-EPR), acute stroke (OCS studies) and a legacy sample. Scans were divided in a 60/20/20 ratio for training, optimisation and testing. CT-images were assessed by two trained raters (rater-1=864 scans, rater-2=20 scans). Agreement between DL tool-predicted GCA scores (range 0-39) and the visual ratings was evaluated using mean absolute error (MAE) and Cohen's weighted kappa. Among 864 scans (ORCHARD-EPR=578, OCS=200, legacy scans=86), MAE between the DL tool and rater-1 GCA scores was 3.2 overall, 3.1 for ORCHARD-EPR, 3.3 for OCS and 2.6 for the legacy scans and half had DL-predicted GCA error between -2 and 2. Inter-rater agreement was Kappa=0.45 between the DL-tool and rater-1, and 0.41 between the tool and rater- 2 whereas it was lower at 0.28 for rater-1 and rater-2. There was no difference in GCA scores from the DL-tool and the two raters (one-way ANOVA, p=0.35) or in mean GCA scores between the DL-tool and rater-1 (paired t-test, t=-0.43, p=0.66), the tool and rater-2 (t=1.35, p=0.18) or between rater-1 and rater-2 (t=0.99, p=0.32). DL-tool GCA scores correlated with age and cognitive scores (both p<0.001). Our DL CT-brain analysis tool measured GCA score accurately and without user input in real-world scans acquired from older patients. Our tool will enable extraction of standardised quantitative measures of atrophy at scale for use in health data research and will act as proof-of-concept towards a point-of-care clinically approved tool.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。