Background: Current conventional cognitive assessments are limited in their efficiency and sensitivity, often relying on a single score such as the total correct items. Typically, multiple features of response go uncaptured.

Objectives: We aim to explore a new set of automatically derived features from the Digit Span (DS) task that address some of the drawbacks in the conventional scoring and are also useful for distinguishing subjects with Mild Cognitive Impairment (MCI) from those with intact cognition.

Methods: Audio-recordings of the DS tests administered to 85 subjects (22 MCI and 63 healthy controls, mean age 90.2 years) were transcribed using an Automatic Speech Recognition (ASR) system. Next, five correctness measures were generated from Levenshtein distance analysis of responses: number correct, incorrect, deleted, inserted, and substituted words compared to the test item. These per-item features were aggregated across all test items for both Forward Digit Span (FDS) and Backward Digit Span (BDS) tasks using summary statistical functions, constructing a global feature vector representing the detailed assessment of each subject’s response. A support vector machine classifier distinguished MCI from cognitively intact participants.

Results: Conventional DS scores did not differentiate MCI participants from controls. The automated multi-feature DS-derived metric achieved 73% on AUC-ROC of the SVM classifier, independent of additional clinical features (77% when combined with demographic features of subjects); well above chance, 50%.

Conclusion: Our analysis verifies the effectiveness of introduced measures, solely derived from the DS task, in the context of differentiating subjects with MCI from those with intact cognition.

背景：当前传统的认知评估在效率和灵敏度方面受到限制，通常依赖于单一分数，例如总正确项目。通常，响应的多个特征未被捕获。

目标：我们的目标是探索从数字跨度（DS）任务中自动导出的一组新特征，这些特征解决了传统评分中的一些缺点，并且也有助于区分患有轻度认知障碍（MCI）的受试者和具有完整认知的受试者。

方法：使用自动语音识别 (ASR) 系统转录对 85 名受试者（22 名 MCI 和 63 名健康对照，平均年龄 90.2 岁）进行 DS 测试的录音。接下来，根据响应的 Levenshtein 距离分析生成五个正确性度量：与测试项目相比，正确、错误、删除、插入和替换单词的数量。使用汇总统计函数，在前向数字跨度 (FDS) 和后向数字跨度 (BDS) 任务的所有测试项目中聚合这些每项目特征，构建代表每个受试者响应的详细评估的全局特征向量。支持向量机分类器将 MCI 与认知完整的参与者区分开来。

结果：传统 DS 评分无法区分 MCI 参与者与对照组。自动化的多特征 DS 衍生指标在 SVM 分类器的 AUC-ROC 上达到了 73%，与其他临床特征无关（与受试者的人口统计特征相结合时为 77%）；远高于机会，50%。

结论：我们的分析验证了引入的措施的有效性，这些措施仅源自 DS 任务，在区分 MCI 受试者和认知完整受试者的背景下。