LangAware: A robust ML‐based platform for the quick and accurate assessment of neurodegenerative diseases
Vassiliki Rentoumi, George Paliouras, Antonis A. Mougias, Irene Kotrotsou, Peter Garrard · Alzheimer s & Dementia · 2020
Abstract Background Although Alzheimer's disease (AD) is associated with changes in spoken language, these have seldom been subjected to systematic analysis on a large scale. We evaluated the effectiveness of LangAware to detect the language indicators that are coupled with early AD, thus assisting with diagnosis. We evaluated LangAware using recordings of speech samples obtained from AD patients and matched healthy controls (NC) derived from various elicitation tasks in two languages, English and Greek*. Method English and Greek datasets were analyzed employing feature selection techniques to choose the most prominent multi‐level linguistic analysis features differentiating the AD from the NC group in both languages. The platform’s diagnostic performance was evaluated on its ability to classify "unseen" audio recordings employing these salient features. Result Evaluation results indicated that LangAware achieved equally high classification scores for both English and Greek. Most significantly, these scores were achieved by employing a custom set of LangAware‐developed cross‐linguistic markers. Conclusion The current evaluation verified the robustness of the platform’s predictive models using audio datasets in two languages. Based on the findings, we conclude that LangAware could provide a time and cost‐effective platform for cognitive screening across languages and across tasks pertaining to neurodegenerative diseases in a range of clinical settings. Such findings also advocate in favor of the robustness of LangAware platform in pursuing cognitive assessment on spontaneous speech across languages. *Peter Garrard and Antonis A. Mougias contributed to this work by providing data for the evaluation of the LangAware platform.