Digital Health

Wearable technology in knee braces is a promising new approach to provide biometric feedback that may allow patients to rehabilitate more effectively from knee injuries and after knee surgery.

Machine learning (ML) has enormous potential to act as a diagnostic adjunct within gynaecology to tackle two main issues: diagnostic uncertainty and delayed diagnoses.

We use computer vision techniques to analyse cardiac imaging in both health and disease. We use these models to understand the genetic basis of heart failure and to predict future health outcomes.

This initiative aims at using different frequency signals to achieve Synthetic UPsampling ARray (SUPAR) to improve angular resolution in multitude. Artificial intelligence (AI) tools will be employed for real time probing design and imaging.

The Research Initiative will focus on combining novel computational imaging methods with AI and deep learning techniques to address previously unsolvable or analytically intractable inverse problems in diverse fields such as realistic computer graphics, computer vision, signal processing, medical imaging and natural/physical sciences.

This initiative brings together researchers and scientists in the field of machine learning based image analysis. The aim is to develop new computational tools for improving image-based detection and diagnosis of disease.

This research will focus on developing modular and adaptive algorithms and tools that use sensory observation and measurement data and healthcare records to extract actionable information for effective and timely interventions.