Harnessing operating room signals to estimate mean arterial pressure with AnesthNet

Abstract

Abstract Monitoring mean arterial pressure (MAP) is essential for ensuring safe general anesthesia. Current practices rely either on non-invasive cuff measurements, which suffer from poor temporal resolution, or invasive arterial lines, which provide excellent accuracy and resolution but carry a significant risk of complications. Therefore, identifying alternatives to arterial lines in the operating rooms is a pressing need. Despite the importance of this issue in the community, clinically viable non-invasive MAP monitoring methods have yet to emerge. Existing approaches often encounter reproducibility issues, notably on large, open-source databases, and are not always optimized for real-time predictions. To address these limitations, this study introduces AnesthNet, a deep learning architecture designed for MAP estimation, using data exclusively from non-invasive and routine sensors such as photoplethysmography, ECG, and cuff oscillometer. AnesthNet was evaluated against the best-performing state-of-the-art deep learning architectures, using international standards to assess their performance on two of the largest datasets to date: VitalDB (2,833 patients) and LaribDB (5,060 patients). AnesthNet achieved superior performances, reaching an MAE of 4.6 (± 4.7) mmHg on VitalDB and 3.8 (± 5.7) mmHg on LaribDB. Our model also outperformed other architectures for different delays in cuff values and yielded no significant latency during inference, meeting clinical real-time requirements.