AI-Enhanced Lung Size Matching and Eligibility System

Lung transplantation is a life-saving procedure for patients with end-stage lung disease, and precise donor- recipient lung size matching is critical to improving transplant success rates. This paper introduces an automated system that estimates lung size and assesses transplant suitability using chest X-ray images based on computer vision techniques. The lung segmentation is achieved through a U-Net model, which successfully separates the lung region from X-ray images. Key anatomical feature landmarks such as width-at-base, width-at-hilum, R-ACPA, R-AMD, L-ACPA, and L-AMD are identified with computer vision for precise measurement of lung dimensions. The measured lung dimensions are compared with donor lung sizes to determine transplant suitability. By reducing reliance on subjective assessments and hand measurements, the technique increases precision, hastens the process of lung matching, and lessens the involvement of human mistakes. By automating the procedure of eligibility screening, radiologists and transplant surgeons are provided with reliable, fact-based data to work with, which ultimately enhances decision-making on lung transplantation. This study helps to show how deep learning and medical imaging technology can assist in enhancing organ transplantation as well as medical results.