Advances in Organ Transplantation and The Role of Artificial Intelligence in Enhancing Transplant Pathology: Review

Abstract

Background: Organ transplantation is a critical medical intervention for individuals with end-stage organ failure, yet the disparity between the demand for and supply of donor organs remains a significant challenge. Traditional diagnostic methods in transplant pathology often suffer from variability and subjectivity, which can lead to suboptimal patient outcomes. Recent advancements in artificial intelligence (AI) offer promising solutions to enhance diagnostic precision and efficiency.

Methods: This review examines peer-reviewed literature from databases such as Web of Science and PubMed, focusing on the application of deep learning-based AI techniques in transplant pathology up until June 2023. We analyze AI's impact on various transplant organs, including heart, lung, kidney, and liver, emphasizing its role in diagnosing transplant-related diseases and improving organ allocation processes.

Results: The integration of AI in transplant pathology has demonstrated significant improvements in diagnostic accuracy. For instance, AI models have achieved a high area under the curve (AUC) values in detecting cardiac allograft rejection and differentiating between rejection grades in kidney biopsies. Furthermore, AI-enhanced digital pathology tools have shown the potential to reduce inter-reader variability among pathologists and facilitate remote consultations.

Conclusion: The incorporation of AI into transplant pathology represents a transformative advancement in the field, promising to enhance diagnostic processes, optimize immunosuppressive therapy, and ultimately improve patient outcomes. However, challenges such as dataset variability and the need for multi-institutional validation remain. Continued research and development of robust AI models are essential for realizing their full potential in clinical practice.