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NCT Number: NCT07218263

AI Platform for Fatigue and Depression Detection

This observational study evaluates the accuracy of the Okaya AI platform in detecting fatigue and depression in cardiology patients, comparing its assessments to PHQ-9 and Fatigue Assessment Scale scores.

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Key information

Conditions

Depression Disorders Behavior Behavioral Symptoms Cardiovascular Depression Fatigue Fatigue Symptom Pathological Conditions, Signs and Symptoms Signs and Symptoms

Age range

18 year–99 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Indiana University, Indianapolis, Indiana, United States

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About this study

Patients frequently experience fatigue and depression, which are often underdiagnosed due to limitations in traditional screening tools. This study introduces the Okaya platform, a browser-based AI system that analyzes facial and vocal biomarkers collected during conversational check-ins. The platform uses computer vision and natural language processing to extract features such as eye contact, facial affect, pitch, volume, and speech patterns. These features are processed through regression models to generate a composite AI based score. The study aims to validate this score against PHQ-9 and FAS assessments. Participants will complete a single baseline check-in using the Okaya platform and complete standard questionnaires. No interventions will be provided.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age ≥18, English-speaking, able to consent

Exclusion criteria

  • Active substance use, nonverbal, cognitive disability, active suicidal/homicidal ideation

Treatment and study plan

Participants will complete PHQ-9, FAS, and Okaya assessments.

Diagnostic Test

AI-based conversational assessment using facial and vocal features to evaluate fatigue and depression.

Primary outcomes

  1. Correlation between Okaya (AI based) score and PHQ-9

    Time frame: Baseline

    Regression analysis comparing Okaya scores to PHQ-9

  2. Correlation between Okaya (AI) based score and PHQ-9 and FAS

    Time frame: Baseline

    Regression analysis comparing Okaya scores to standard assessments

  3. Correlation between Okaya (AI based) score and FAS

    Time frame: Baseline

    Regression analysis comparing Okaya scores to FAS

Secondary outcomes

  1. Usability and patient satisfaction

    Time frame: Baseline

    Ease-of-use ratings on the Okaya platform

Study contacts

Contact information is provided by the study sponsor or research team.

Brijesh Patel, DO

CONTACT

[email protected]

317-963-8637

Sponsors and collaborators

Lead sponsor

Brijesh Patel

Other

Collaborators

  • SmartTec Inc

Registry information

Official study title

Efficacy of a Novel Web-based Fatigue and Cognitive Assessment Platform in Detecting Fatigue and Depression

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Oct 20, 2025
Registry last updated
Feb 25, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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