Harnessing operating room signals to estimate mean arterial pressure with AnesthNet
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| Julkaisussa: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 33988-34000 |
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| Muut tekijät: | , , , |
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Nature Publishing Group
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| Linkit: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 2045-2322 | ||
| 024 | 7 | |a 10.1038/s41598-025-12341-8 |2 doi | |
| 035 | |a 3256003148 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 274855 |2 nlm | ||
| 100 | 1 | |a Perdereau, Jade |u INSERM, U942 MASCOT, Université Paris Cité, 75006, Paris, France (ROR: https://ror.org/05f82e368) (GRID: grid.508487.6) (ISNI: 0000 0004 7885 7602); Department of Anaesthesia and Critical Care, APHP, Hôpital Lariboisière, 75010, Paris, France (ROR: https://ror.org/02mqtne57) (GRID: grid.411296.9) (ISNI: 0000 0000 9725 279X); Inria, Université Paris-Saclay, Palaiseau, France (ROR: https://ror.org/03xjwb503) (GRID: grid.460789.4) (ISNI: 0000 0004 4910 6535) | |
| 245 | 1 | |a Harnessing operating room signals to estimate mean arterial pressure with AnesthNet | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 610 | 4 | |a Association for the Advancement of Medical Instrumentation Assistance Publique-Hopitaux de Paris | |
| 651 | 4 | |a Netherlands | |
| 653 | |a Physiology | ||
| 653 | |a Monitoring methods | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a International standards | ||
| 653 | |a Calibration | ||
| 653 | |a Vital signs | ||
| 653 | |a Signal processing | ||
| 653 | |a Electrocardiography | ||
| 653 | |a Hypotension | ||
| 653 | |a Causality | ||
| 653 | |a Anesthesia | ||
| 653 | |a EKG | ||
| 653 | |a Patients | ||
| 653 | |a Electronic health records | ||
| 653 | |a Blood pressure | ||
| 653 | |a Neural networks | ||
| 653 | |a Hemodynamics | ||
| 653 | |a Data collection | ||
| 653 | |a Libraries | ||
| 653 | |a Latency | ||
| 653 | |a Intensive care | ||
| 653 | |a Data warehouses | ||
| 653 | |a Critical care | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Joachim, Jona |u INSERM, U942 MASCOT, Université Paris Cité, 75006, Paris, France (ROR: https://ror.org/05f82e368) (GRID: grid.508487.6) (ISNI: 0000 0004 7885 7602); Department of Anaesthesia and Critical Care, APHP, Hôpital Lariboisière, 75010, Paris, France (ROR: https://ror.org/02mqtne57) (GRID: grid.411296.9) (ISNI: 0000 0000 9725 279X); LMS, CNRS, Institut Polytechnique de Paris, Palaiseau, France (ROR: https://ror.org/042tfbd02) (GRID: grid.508893.f) | |
| 700 | 1 | |a Vallée, Fabrice |u INSERM, U942 MASCOT, Université Paris Cité, 75006, Paris, France (ROR: https://ror.org/05f82e368) (GRID: grid.508487.6) (ISNI: 0000 0004 7885 7602); Department of Anaesthesia and Critical Care, APHP, Hôpital Lariboisière, 75010, Paris, France (ROR: https://ror.org/02mqtne57) (GRID: grid.411296.9) (ISNI: 0000 0000 9725 279X); LMS, CNRS, Institut Polytechnique de Paris, Palaiseau, France (ROR: https://ror.org/042tfbd02) (GRID: grid.508893.f) | |
| 700 | 1 | |a Cartailler, Jérôme |u INSERM, U942 MASCOT, Université Paris Cité, 75006, Paris, France (ROR: https://ror.org/05f82e368) (GRID: grid.508487.6) (ISNI: 0000 0004 7885 7602); Department of Anaesthesia and Critical Care, APHP, Hôpital Lariboisière, 75010, Paris, France (ROR: https://ror.org/02mqtne57) (GRID: grid.411296.9) (ISNI: 0000 0000 9725 279X) | |
| 700 | 1 | |a Moreau, Thomas |u Inria, Université Paris-Saclay, Palaiseau, France (ROR: https://ror.org/03xjwb503) (GRID: grid.460789.4) (ISNI: 0000 0004 4910 6535) | |
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 33988-34000 | |
| 786 | 0 | |d ProQuest |t Science Database | |
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