Employing the Artificial Intelligence Object Detection Tool YOLOv8 for Real-Time Pain Detection: A Feasibility Study
Despite limitations, this study highlights the promise of using real-time computer vision models for pain detection, with potential applications in clinical settings. Future research will focus on evaluating the model's generalizability across diverse clinical scenarios and its integration into clinical workflows to improve patient care.
Authors
- Federica Monaco
- Alessandro Simonini (ORCID: https://orcid.org/0000-0003-3788-7672)
- Marco Cascella (ORCID: https://orcid.org/0000-0002-5236-3132)
- Francesca Gargano (ORCID: https://orcid.org/0000-0002-8962-5685)
- Giuliano Lo Bianco (ORCID: https://orcid.org/0000-0003-2705-1548)
- Alfonso Maria Ponsiglione (ORCID: https://orcid.org/0000-0003-1346-515X)
- Ornella Piazza (ORCID: https://orcid.org/0000-0002-0316-5930)
- Mohammed Naveed Shariff (ORCID: https://orcid.org/0009-0004-2430-7370)
Institutions
- University of Salerno (IT)
- Fondazione Ospedale Salesi (IT)
- Fondazione Istituto G. Giglio di Cefalù (IT)
- Campus Bio Medico University Hospital (IT)
- University of Naples Federico II (IT)
Publication Details
- Journal
- Journal of Pain Research
- Published
- 2024-11-01
- DOI
- https://doi.org/10.2147/jpr.s491574
- Citations
- 10
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 4.22