NeuralCipher Research overview
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Background Cognitive decline affects up to 80% of Parkinson’s disease (PD) patients, significantly impacting quality of life. Speech changes occur early in PD and may reflect underlying cognitive deterioration before formal diagnosis. Natural language processing (NLP) offers automated, objective methods to analyze speech patterns for cognitive markers, yet evidence remains fragmented across disciplines. Objective To systematically review and meta-analyze studies using NLP techniques to detect early cognitive decline in PD through speech analysis, evaluating diagnostic accuracy, methodological approaches, and speech-based biomarkers. Methods Following PRISMA guidelines, we will search PubMed, Embase, Web of Science, IEEE Xplore, PsycINFO, and linguistics databases from inception to December 2025. Eligible studies must apply NLP to speech samples from PD patients for cognitive assessment. Two reviewers will independently screen studies using Covidence. Quality assessment will employ QUADAS-2 for diagnostic studies and PROBAST+AI for prediction models. Data Synthesis Narrative synthesis will describe NLP methodologies, speech features, and cognitive outcomes. Meta-analysis using bivariate random-effects models will pool diagnostic accuracy measures where appropriate. Subgroup analyses will examine performance by NLP approach, speech task type, and cognitive domain assessed. Discussion This protocol establishes methodology for the first comprehensive synthesis of NLP-based speech analysis for cognitive detection in PD, addressing critical gaps in existing reviews and providing a framework for evaluating this emerging diagnostic approach.
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