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Artificial Intelligence empowered recruitment for clinical trials
Author(s) -
Linz Nicklas,
ter Huurne Daphne BG,
Langel Kai,
Ramakers Inez HGB,
König Alexandra
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.050304
Subject(s) - clinical dementia rating , clinical trial , rating scale , dementia , medicine , phone , disease , cognition , cognitive assessment system , psychology , cognitive impairment , psychiatry , developmental psychology , linguistics , philosophy
Background Recruitment of clinical drug trials for Alzheimer's disease (AD) is a lengthy process with an ultimately high screening failure rate, as current research focuses on prodromal stages. We evaluate the option of pre‐screening potential participants for AD trials via an automated phone‐based assessment using speech analysis. Method 140 participants were recruited at the memory clinic in Maastricht as part of the MUMC+ study (SCI, MCI, ADD). They underwent a in‐person baseline assessment (0M) at the clinic. Cognitive assessments were performed and speech was recorded during each assessment using the Delta platform. For the next assessments (6M), participants were contacted via telephone by a trained research nurse. Cognitive assessments were performed on the phone and speech was recorded during each assessment. Speech from each assessment point was analysed automatically to extract relevant features. Machine learning models to predict disease status, Clinical Dementia Rating Scale (CDR) scores, and Mini‐Mental‐Status‐Examination scores were trained and evaluated on the data. Result Models based on speech features extracted from the phone assessment were able to predict disease status with an AUC of 0.93±0.06, CDR score with a Mean Absolute Error (MAE) of 1.9±0.8, and MMSE score with an MAE of 2.3±1.1. Adding longitudinal data from baseline assessments increased accuracy across all models. Conclusion Automated pre‐screening through speech analysis could be an effective tool to increase the efficiency and effectiveness of recruitment for AD drug trials.