Quantum machine learning for predicting anastomotic leak: a clinical study

Abstract

Abstract Anastomotic leak is a life-threatening complication following colorectal surgery. This study benchmarks Quantum Neural Networks (QNNs) against hyperparameter-tuned classical models (logistic regression, multi-layer perceptrons, boosting algorithms) for anastomotic leak prediction. Using a 200-patient clinical dataset strictly bounded by a priori medical constraints, we simulated QNNs with ZZFeatureMap encoding and EfficientSU2/RealAmplitudes ansatze under realistic hardware noise. To ensure statistical reliability, performance metrics were averaged across 10 independent optimization runs. The EfficientSU2-BFGS configuration achieved the highest mean AUC of $$0.797 \pm 0.024$$ , while RealAmplitudes with CMA-ES maximized Average Precision ( $$0.504 \pm 0.121$$ ). Crucially, at a fixed, clinically necessary sensitivity of $$83\%$$ , specific QNN configurations achieved significantly higher specificity (up to $$66\%$$ ) and Negative Predictive Value (up to $$96\%$$ ) compared to classical models (maximum $$44\%$$ and $$94\%$$ , respectively), effectively minimizing false positives. However, classical models maintained superior probability calibration for continuous risk stratification. We conclude that QNNs offer robust discriminative performance for clinical screening, warranting further validation on larger, independent cohorts.