LANGaware: Introducing the right solution for the early detection of neurodegenerative and psychiatric diseases. (31st December 2021)

• Record Type: Journal Article
• Authors: Rentoumi, Vassiliki; Vassiliou, Evangelos; Demiraj, Admir; Pittaras, Nikiforos; Mandalis, Petros; Alexandridou, Martha; Kemp, Hollie; Eleftheriou, Ioanna; Danezi, Maria; Hatzopoulou, Maria; Kamtsadeli, Vasiliki; Paliouras, George; Papatriantafyllou, John D
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.052941
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: There are multiple neurodegenerative diseases that directly affect speech [1]. However, its utilization as a robust indicator for cognitive impairment is under‐investigated. In many cases, mild cognitive decline progresses to a neurodegenerative disease and its detection is of utmost importance, since it is at this stage that treatment is most effective. One of our core goals is developing techniques for differentiating between patients with cognitive decline and healthy cohorts, by utilizing only speech samples [2]. Such samples are obtained from verbal elicitation tasks designed for cognitive assessment, e.g. picture descriptions and narration of everyday activities. Method: Audio recordings from cognitive assessment tasks are fed through our platform to a Natural Language Processing and Machine Learning pipeline, employing an automatic discovery procedure of predictive salient biomarkers, to train an advanced classification system. The biomarker collection includes features that characterize voice, speech, language structure, composition and usage, and are engineered to highlight symptoms of neurodegenerative disorders. Biomarkers undergo multiple stages of filtering, processing and transformation to train and fine‐tune the final model. Diagnostic performance of the output classifier is obtained on an unseen test set, to ensure a robust generalization of the platform. Result: Our platform utilizes the set of automatically selected, cross‐linguistic digital biomarkers to obtain sensitivity and specificity scores of 81% and 84% respectively, compared against medical expert diagnosis. The most salient biomarkers with respect to identifying our pathological cohort, relate to feature categories of Syntactic Complexity, Content Word usage, Lexical Repetition, Syntactic Errors and Function Words, with weight contributions of ∼ 16%, 14%, 13%, 12% and 11% respectively. Our platform provides additional, detailed population and patient‐based descriptive analytics to enhance transparency and explainability of the results. Conclusion: Early detection of cognitive decline facilitates early intervention, treatment and proactive care, delaying disease progression and reducing symptom severity. We believe that our platform provides an effective solution for risk factor estimation and our findings incentivize further research into speech analysis techniques for the prediction of cognitive decline. [1] Boschi, Veronica, et al., Frontiers in psychology 8 (2017): 269. [2] Vassiliki Rentoumi et al., Alzheimer's & Dementia, Wiley, volume 16, 2020.