AI Platform for Fatigue and Depression Detection

Brijesh Patel

Status: Not yet enrolling

Conditions: Fatigue Symptom; Depression Disorders; Cardiovascular

Treatments: Diagnostic Test: Participants will complete PHQ-9, FAS, and Okaya assessments.

Study type: Observational

Funder types: Other; Industry

Identifiers

NCT07218263

IU-Okaya-2025

IRB #25854 (Other Identifier)

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 100 estimated patients

Sex: All

Ages: 18 to 99 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

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

Trial design

100 participants in 1 patient group

Single Group Assignment

Description:

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

Treatment:

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