Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors (ρ = 0.814, 95% CI [0.700,0.888]) and video (ρ = 0.736, 95% C.I [0.584, 0.838]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment (ρ = 0.644, 95% C.I [0.585,0.696]).

自动评估损伤和疾病严重程度是数据驱动医学的一个关键挑战。我们提出了一个框架来应对这一挑战，该框架利用专门针对健康个体训练的人工智能模型。基于置信度的异常特征 (COBRA) 评分利用这些模型在出现受损或患病患者时置信度的下降来量化他们与健康人群的偏差。我们应用 COBRA 评分来解决当前中风患者上身损伤临床评估的一个关键限制。黄金标准 Fugl-Meyer 评估 (FMA) 需要训练有素的评估员亲自进行 30-45 分钟的管理，这限制了监测频率，并妨碍医生根据每位患者的进展情况调整康复方案。 COBRA 分数在一分钟内自动计算，结果显示与独立测试队列中的 FMA 密切相关，针对两种不同的数据模式：可穿戴传感器 ( ρ = 0.814, 95% CI [0.700,0.888]) 和视频 ( ρ = 0.736，95% CI [0.584，0.838]）。为了证明该方法对其他情况的普遍适用性，还应用 COBRA 评分来量化磁共振成像扫描中膝骨关节炎的严重程度，再次与独立临床评估实现显着相关性（ ρ = 0.644，95% CI [0.585， 0.696]）。