Therapy AI
Project Type: Tech | AI | MERN STACK DEVELOPMENT | UREAL ENGINE
Project Timeline: 1-1/2 years
Emily is a depression detection system developed by Discret Digital. It uses audio and video features to help diagnose patients. By asking interactive questions, Emily records the patient’s responses and analyzes them, along with PHQ-8 question scores, to provide valuable insights. This system utilizes spectrograms for depression detection.
Emily uses advanced technology to help manage depression. It provides exercise recommendations based on each patient’s depression level.
Story Behind Therapy AI
Conceptualization and Planning
We developed a depression detection system called therapyAI. It uses sound and video features to accurately diagnose people. We conducted thorough research and planning to understand its requirements, feasibility, and potential impact.
Design and Prototyping
A team of experts worked together to create Emily’s user interface and interaction flow. They designed prototypes to visualize the recording process, questionnaires, and report generation.
Backend Development and Data Processing
We developed special tools to process audio and video files. We used algorithms to clean up the audio, extract important information, and create visual representations of the sound.
Video processing modules are used to analyze recorded videos by extracting features.
PHQ-8 Scoring and Severity Calculation
We used deep learning techniques to create a depression detection model. It was trained on labeled data and can accurately classify depression using audio and video features. We also integrated a scoring system based on the Patient Health Questionnaire-8 (PHQ-8) to evaluate the severity of depression.
End-of-Session Report Generation
The reports combined the results from the depression detection module and PHQ-8 scoring. They provided insights into the patient’s depression status, severity level, and exercise recommendations.
Testing, Refinement, and Validation
Therapy AI underwent thorough testing and validation to ensure its accuracy and reliability. Feedback from healthcare professionals and patients helped refine the system and improve user experience. Emily was then deployed in healthcare settings, allowing clinicians to use it for depression detection and assessment. Ongoing updates and improvements were made to enhance the system’s performance and adapt to changing needs
