Towards accurate and efficient diagnoses in nephropathology: An AI-based approach for assessing kidney transplant rejection.

ABSTRACT: The Banff classification is useful for diagnosing renal transplant rejection. However, it has limitations due to subjectivity and varying concordance in physicians' assessments. Artificial intelligence (AI) can help standardize research, increase objectivity and accurately quantify morphological characteristics, improving reproducibility in clinical practice. This study aims to develop an AI-based solutions for diagnosing acute kidney transplant rejection by introducing automated evaluation of prognostic morphological patterns. The proposed approach aims to help accurately distinguish borderline changes from rejection. We trained a deep-learning model utilizing a fine-tuned Mask R-CNN architecture which achieved a mean Average Precision value of 0.74 for the segmentation of renal tissue st

SUBMITTER: Fayzullin A 

PROVIDER: S-EPMC11385065 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature