Machine Learning-Driven Assessment of Early Graft Function in Living Donor Kidney Transplantation Using Intraoperative Laser Speckle Contrast Imaging

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ABSTRACT Background Although living donor kidney transplantation (LDKT) generally achieves excellent outcomes, 5–12% of recipients experience early graft dysfunction, which is associated with poorer long-term survival. Current predictive tools rely mainly on clinical parameters and lack intraoperative applicability. Laser speckle contrast imaging (LSCI) enables real-time, non-contact assessment of renal microcirculation, and may provide complementary insight when integrated with machine learning (ML). Methods In this prospective cohort study, we performed intraoperative LSCI measurement in 110 adult LDKT recipients at Erasmus Medical Center. Early graft function was assessed by estimated glomerular filtration rate (eGFR) at 1 week posttransplant, with patients classified as Group I (eGFR ≥ 30 mL/min/1.73 m²) and Group II (eGFR < 30 mL/min/1.73 m²). Two predefined feature sets were used for model development: (i) a Clinical Model (selected clinical variables) and (ii) a Combined Model (clinical + convolutional neural network [CNN]-derived LSCI features). Four ML algorithms (support vector machine [SVM], logistic regression, random forest [RF], and XGBoost) were trained using 5-fold cross-validation with Synthetic Minority Oversampling Technique (SMOTE) and evaluated on independent test sets across 30 repeated iterations. Results Of 110 recipients, 15 (17%) had eGFR < 30 mL/min/1.73 m² at 1 week. Patients in Group II received kidneys from older donors with lower predonation eGFR, had higher BMI, more cardiovascular comorbidity, and greater intraoperative blood loss. The Combined Model consistently outperformed the Clinical Model across all algorithms. For example, SVM achieved higher accuracy (0.89 [95% CI, 0.85–0.92] vs. 0.79 [0.75–0.84]) and logistic regression yielded higher recall (0.86 [0.83–0.88] vs. 0.76 [0.74–0.79]). In independent test sets, Combined Models maintained better performance, with SVM achieving the highest F1 score (0.60 [0.50–0.71]) and RF achieving the highest recall (0.88 [0.50–1.00]). Grad-CAM visualizations confirmed that CNN-extracted features localized to physiologically relevant perfusion regions. LSCI also enabled real-time detection and correction of vascular complications in two cases. Conclusions Integrating intraoperative LSCI features with clinical variables using ML significantly improved prediction of low one-week eGFR (< 30 mL/min/1.73m²) in LDKT compared with clinical data alone. LSCI also enabled real-time detection of vascular complications, underscoring its role as both a predictive and intraoperative guidance tool. Larger multicenter studies are warranted to validate its generalizability and explore applications in other transplant scenarios.

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