An AI‐based approach to detect cognitive impairment from digital voice recordings of neuropsychological assessments. (20th December 2022)

• Record Type: Journal Article
• Authors: Amini, Samad; Hao, Boran; Zhang, Lifu; Song, Mengting; Gupta, Aman; Karjadi, Cody; Kolachalama, Vijaya B.; Au, Rhoda; Paschalidis, Ioannis
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 7
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• 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.064029
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Reliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide‐spread screening for dementia and accelerate preventative efforts. Method: We used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub‐tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants' demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI. Result: We evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held‐out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub‐tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task. Conclusion: The proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develop a remote screening tool that could be easily adapted to any language.