Deep Learning-Based Image Analysis for Complement-Dependent Cytotoxicity (CDC) Crossmatch Imaging and FISH Techniques: A Comprehensive Research Proposal

Context & Background

Organ transplantation requires precise immunophenotypic crossmatching to prevent hyperacute rejection. Complement-dependent cytotoxicity (CDC) testing evaluates recipient antibody reactions against donor cells, which is currently graded manually under microscopes.

Problems to be Addressed

Manual slide scoring is subjective, slow, and prone to variability, which can lead to delayed surgeries or life-threatening transplant mismatches.

Aims and Objectives

1. Automate cell viability scoring on CDC crossmatch images.
2. Develop deep learning algorithms for FISH (Fluorescence In-Situ Hybridization) slide segmentation.
3. Integrate automated diagnostic reports with clinical pathology software.

Methodology

In collaboration with Max Healthcare and IIIT Delhi, the project builds custom convolutional neural networks (CNNs) for cell detection and viability classification (live vs. dead cells). The models are trained on curated clinical image datasets.

Expected Outcomes

An automated CDC crossmatch scoring tool, deep learning image classification libraries, and publications in transplantation informatics.