Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a novel 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 ($\rho = 0.845$, 95% CI [0.743,0.908]) and video ($\rho = 0.746$, 95% C.I [0.594, 0.847]). 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 ($\rho = 0.644$, 95% C.I [0.585,0.696]).

翻译：客观评估损伤与疾病严重程度是数据驱动医学中的关键挑战。我们提出了一种新颖框架来应对这一挑战，该框架利用仅基于健康个体训练的AI模型。基于置信度的异常表征评分（COBRA）通过捕捉模型在面对患病或损伤患者时置信度的下降，量化其与健康人群的偏差。我们应用COBRA评分解决了当前脑卒中患者上肢损伤临床评估的主要局限性。金标准Fugl-Meyer评估（FMA）需由受过训练的评估者现场实施30-45分钟，这限制了监测频率，并阻碍医生根据患者进展调整康复方案。COBRA评分可在1分钟内自动计算，并在独立测试队列中证明与两种数据模态的FMA高度相关：可穿戴传感器（ρ=0.845，95%CI [0.743,0.908]）和视频（ρ=0.746，95%CI [0.594,0.847]）。为展示该方法对其他疾病的普适性，COBRA评分还被应用于基于磁共振成像扫描量化膝骨关节炎严重程度，结果同样与独立临床评估显著相关（ρ=0.644，95%CI [0.585,0.696]）。