Functional and morphological characteristics of renal allograft using artificial intelligence and machine learning technologies in post- transplant risk prediction

Background. Timely clinical decision-making regarding interventional management in transplant recipients to preserve allograft function depends on the accuracy of correlating in vitro biomaterial characteristics with native in vivo intermolecular interactions.

Aim. To develop a system for short‑term risk stratification and prediction in kidney transplant recipients based on the analysis of functional and morphological pattern dynamics using medical informatics methods.

Materials and methods. The analysis included data from 160 kidney transplant recipients comprising a total of 5,531 observations. Current risk assessment was performed using clinically validated in vitro threshold values reflecting allograft function and systemic inflammatory activity. A time‑series analysis model based on Long Short‑Term Memory (LSTM) recurrent neural network was employed for risk category prediction.

Results. The predictive system demonstrated high accuracy in short‑term risk classification (approximately 90%) when at least three consecutive days of dynamic monitoring were available. Risk state formation is determined not by isolated deviations of individual parameters, but by coordinated changes in functional and morphological patterns, primarily the most energy‑intensive cyclic ion transport processes and inflammatory markers.

Conclusion. The proposed approach may serve as a clinical decision support tool for timely therapy adjustment and allograft function preservation.

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