DigitalTwin

Digital Twin: A machine learning-powered platform leveraging cough acoustics for non-invasive, continuous respiratory disease screening.

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Inspiration:

Digital Twin was born from a simple yet powerful idea:   What if a person’s cough could tell the story of their respiratory health?

In a world shaped by remote care and digital diagnostics, we envisioned a tool that could democratize access to early respiratory disease detection by making it affordable, user-friendly, and non-invasive. We were inspired by the opportunity to reduce diagnostic delays and empower communities with proactive, accessible healthcare.

What it does:

Digital Twin is a web-based application that allows users to record their cough and receive a prediction of potential respiratory illness. It leverages a machine learning model trained on cough audio data and integrates Vapi to enable real-time voice interaction. The output provides a preliminary classification to support early screening and awareness.

How we built it:

After extensive planning and discussion, we divided our workflow into specialized components:

  • Built and trained a machine learning model using labeled cough sound data  
  • Integrated the Vapi voice API for voice input capture and backend processing  
  • Designed a responsive web application interface  

Challenges we ran into:

  • Difficulty in finding a publicly available dataset of cough sounds from patients with respiratory diseases, which constrained model development and validation
  • Unfamiliarity with Vapi's API and voice processing tools, which required time to understand and implement effectively  
  • Limited frontend development experience, especially in deploying interactive and responsive web interfaces

To overcome these we:

  • Participated in workshops and studied documentation  
  • Conducted frequent internal check-ins and collaborative debugging  
  • Supported one another across roles to close skill gaps

Accomplishments that we're proud of:

  • Developed an interactive web platform from scratch, despite limited prior frontend experience  
  • Integrated a voice-based interface for health screening, enhancing accessibility and user experience

What we learned:

  • Acoustic data can serve as powerful digital biomarkers when paired with appropriate ML architectures
  • AI tooling can provide a low cost alternatives for remote settings where resourced are limited while providing many of the services which would require a team to do like customer service and support, web deployment, and resource allocation
  • Collaboration, communication, and flexibility are critical in overcoming steep learning curves

What's next for DigitalTwin:

  • Expand the dataset to include more real world data in order improve the quality of predictions as well as generalize across to more respiratory illnesses
  • Deploy on mobile platforms for increasing accessibility to limited resource settings
  • Expand regulatory compliance framework across other countries
  • Generalize platform to include other longitudinal monitoring of health (ie diabetic retinopathy)

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