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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15788185 |
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| _version_ | 1866902136047009792 |
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| author | Anuja Anilrao Ghasad Dr. Sarika Khandelwal |
| author_facet | Anuja Anilrao Ghasad Dr. Sarika Khandelwal |
| contents | <p>This research introduces a novel AI-integrated Internet of Things (IoT) system designed to assist surgeons during hernia procedures through real-time, adaptive feedback. Leveraging machine learning algorithms and sensor fusion, the system provides context-aware insights to reduce intraoperative errors and optimize decision-making. Evaluations based on experimental setups and clinical simulations demonstrated notable improvements in surgical precision, latency, and adaptability. The implementation confirms that AI-IoT convergence holds significant promise for improving patient safety and procedural efficacy in surgical settings.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15788185 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Implementation of an efficient AI-powered IoT interface for assisting Hernia surgery via real-time incremental learning feedback Anuja Anilrao Ghasad Dr. Sarika Khandelwal <p>This research introduces a novel AI-integrated Internet of Things (IoT) system designed to assist surgeons during hernia procedures through real-time, adaptive feedback. Leveraging machine learning algorithms and sensor fusion, the system provides context-aware insights to reduce intraoperative errors and optimize decision-making. Evaluations based on experimental setups and clinical simulations demonstrated notable improvements in surgical precision, latency, and adaptability. The implementation confirms that AI-IoT convergence holds significant promise for improving patient safety and procedural efficacy in surgical settings.</p> |
| title | Implementation of an efficient AI-powered IoT interface for assisting Hernia surgery via real-time incremental learning feedback |
| url | https://doi.org/10.5281/zenodo.15788185 |