Our Projects

Our projects center around applying AI and ML tools in medical settings around the globe. Check out our Github repository for more!

Cardiopulmonary Bypass Data Analysis

Affiliation: Dell Medical School

This project aims to develop computational and AI models to predict various postoperative outcomes in patients undergoing cardiac surgery with cardiopulmonary bypass (CPB). CPB is a critical procedure wherein the patient's heart and lungs functions are temporarily replaced by a bypass machine during surgery. Throughout this process, a wealth of data is collected, including blood gas analysis, laboratory values, hemodynamic parameters, and other clinical variables. By leveraging advanced computational techniques and machine learning algorithms, this project seeks to analyze this data to predict outcomes such as survival, acute kidney injury, and other relevant clinical endpoints.

Pediatric Clinical Prediction

Affiliation: Dell Medical School

This project focuses on leveraging artificial intelligence (AI) to analyze physiological signals, including heart rate, ECG rate, and other clinically measured heart data, to identify trends and patterns for clinical prediction in pediatric patients. By harnessing advanced computational techniques, this initiative aims to develop a predictive model tailored specifically for pediatric populations. The model will utilize data-driven approaches to identify predictive features and patterns in physiological signals, enabling early detection of clinical events and improved patient outcomes in pediatric healthcare settings.

CT Segmentation to 3D Printing

Affiliation: Dell Medical School

This project aims to develop a AI model that segment the heart and its vessels from CT images to create a 3D surface mash of the anatomies of interest which will be used for 3D printing. The input of the project will be CT images and output will be segmented stereolithography (STL) files. Challenge need to be overcome is the difficulty of surface rendering of the CT images for the anatomy of interest.