Utilizing image processing to create an app for cell image counting and characterization

Abstract

Organ transplantation represents a beacon of hope for individuals facing end-stage organ failure, with the success hinging on donor-recipient compatibility. Complement-dependent cytotoxicity (CDC) crossmatch imaging and Fluorescence In Situ Hybridization (FISH) techniques are vital for assessing this compatibility by detecting cytotoxic antibodies that may lead to graft rejection. Current manual interpretation practices, dependent on physician expertise, introduce subjectivity and variability in graft rejection risk assessment. The integration of AI/ML-driven image analysis provides a promising avenue to address these limitations, offering an objective and standardized approach. This research project is dedicated to developing and evaluating AI/ML-driven image analysis techniques for CDC crossmatch imaging and FISH. The primary objectives are to develop and optimize tailored AI/ML algorithms, train a predictive modeling system for graft rejection risk assessment, validate its performance against traditional methods, and assess its clinical utility and impact. The project adopts a Retro-Prospective study design, utilizing fluorescence microscopy and collaborating with Max Healthcare for data. Anticipated outcomes include the enhancement of objectivity and standardization in graft rejection risk assessment, ultimately contributing to improved patient outcomes, optimized organ allocation, and cost-effective healthcare.