Deep-learning-based postoperative desaturation prediction using spirometry image data

Postoperative desaturation, a significant concern following laparoscopic and robot-assisted procedures, is associated with increased mortality rates and prolonged stays in the recovery room. Although modern anesthesia workstations offer real-time spirometry pressure-volume loops for each breath, their clinical utility remains limited. This study aimed to develop a deep learning model using a convolutional neural network to analyze spirometry pressure-volume loops in patients undergoing laparoscopic or robot-assisted surgeries and to design a clinically applicable index using gradient-weighted class activation maps to enhance interpretability. This retrospective study was conducted across two tertiary hospitals, involving data from 207 patients who underwent laparoscopic or robot-assisted abdominal surgeries for model training. Data from 45 patients at an independent hospital were used for external validation. Thus, data from a total of 252 patients were used. The primary outcome measure was the model's ability to predict postoperative desaturation. The performance of the model, along with that of the derived clinical index, was evaluated using the area under the receiver operating characteristic curve (AUROC). The deep learning model predicted postoperative desaturation with AUROC (95% confidence interval) values of 0.825 [0.744-0.905] and 0.688 [0.656-0.720] on internal and external validation datasets, respectively. The desaturation predictive index demonstrated moderate predictive performance on both internal and external datasets (AUROC: 0.774 and 0.722, respectively). These findings support the efficacy of both the deep learning model and the predictive index in forecasting postoperative desaturation, thus highlighting the potential of real-time spirometry in postoperative monitoring research.