Accexible Speech Analysis for MCI Detection and Monitoring in Colombia

Accexible114 enrolled

Overview

Mild Cognitive Impairment (MCI) is frequently underdiagnosed due to its subtle clinical presentation. This study evaluates the diagnostic performance of AcceXible, a speech analysis-based machine learning platform, compared to the Montreal Cognitive Assessment (MoCA) for MCI detection and monitoring in Colombian patients. A diagnostic test accuracy study will be conducted within a primary care setting (EPS Sanitas), including prior validation of the AcceXible protocol in the Colombian healthcare context. The study pursues two primary aims: (1) to validate the AcceXible tool in a Colombian population, and (2) to demonstrate that AcceXible achieves high diagnostic accuracy for early MCI detection and longitudinal monitoring relative to the MoCA.

Study Type

OBSERVATIONAL

Enrollment

114

Conditions

MCI

Interventions

AccexibleDEVICE

Speech Analysis Tool

Eligibility

Sex: ALLMin age: 60 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Ability to independently operate a mobile or portable device with internet connectivity and integrated microphone * Provision of voluntary written informed consent prior to study participation Exclusion Criteria: * Diagnosed psychiatric disorder or cognitive impairment not attributable to neurodegenerative etiology * Visual impairment sufficient to preclude reading on-screen text or perceiving visual stimuli * Illiteracy * Hearing impairment sufficient to preclude comprehension of verbal instructions or detection of auditory test-onset cues

Outcomes

Primary Outcomes

Diagnostic accuracy of acceXible compared to MoCA for MCI detection

Sensitivity and specificity of the acceXible speech-based platform relative to the MoCA (reference standard; cutoff \<30 = abnormal) for detecting MCI in adults aged ≥55 years. Additional metrics: area under the ROC curve (AUC), positive and negative predictive values (PPV, NPV), and likelihood ratios.

Time frame: Baseline