Deep Learning Analysis of Intraoperative Physiological Signals for Predicting Surgical Outcomes in Head and Neck Free Flap Surgery: Preliminary Study

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Intraoperative monitoring systems play a vital role in surgical safety and decision-making. This study explored whether high-resolution physiological signals routinely available during surgery can be leveraged by deep learning to predict adverse events after head and neck free flap reconstruction. In this retrospective study, intraoperative waveforms, including arterial pressure, plethysmography, and electrocardiogram, from 187 patients who underwent free flap surgery were analyzed. A deep learning model based on the Mamba architecture was trained to predict three outcomes: flap failure, return to the operating room for exploration, and other surgical complications. Conventional logistic regression using static clinical variables and feature-based machine learning models were evaluated for comparison. On a patient-wise stratified held-out test set, the deep learning model achieved AUROCs of 0.86, 0.62, and 0.93 for flap failure, re-exploration, and other complications, respectively. Precision was 0.50, 0.40, and 1.00, whereas recall was 0.50, 0.40, and 0.25, indicating high precision but modest recall. Predictive performance varied across outcomes. These findings demonstrate the feasibility of waveform-based deep learning for perioperative risk stratification in reconstructive surgery, although the preliminary nature, limited sample size, and lack of external validation should be considered.

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