Using deep learning to predict postoperative pain in reverse shoulder arthroplasty patients
We successfully developed an artificial neural network to predict postsurgical pain after rTSA. Additional efforts are still required to refine the models' performance, such as including further parameters predictive of pain and considering other machine learning algorithms. In a clinical setting, the implementation of such a prediction model could optimize surgical indications and help manage patient expectations more effectively.
Authors
- Andrea Cina (ORCID: https://orcid.org/0000-0002-6016-6200)
- Asimina Lazaridou (ORCID: https://orcid.org/0000-0002-9203-0009)
- Tim Schneller (ORCID: https://orcid.org/0009-0007-6949-9849)
- Philipp Moroder (ORCID: https://orcid.org/0000-0002-0987-9926)
- Markus Scheibel (ORCID: https://orcid.org/0000-0002-4485-0256)
Institutions
- Brigham and Women's Hospital (US)
- ETH Zurich (CH)
- Schulthess-Klinik (CH)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- JSES International
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1016/j.jseint.2024.11.020
- Citations
- 3
- Primary Topic
- Shoulder Injury and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.72