Artificial Intelligence Machine Learning Algorithms Versus Standard Linear Demographic Analysis in Predicting Implant Size of Anatomic and Reverse Total Shoulder Arthroplasty.

Journal: Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews

PMID: 39106479

Abstract

BACKGROUND: Accurate and precise templating is paramount for anatomic total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RSA) to enhance preoperative planning, streamline surgery, and improve implant positioning. We aimed to evaluate the predictive potential of readily available patient demographic data in TSA and RSA implant sizing, independent of implant design.

Authors

Amir Boubekri

Loyola University Medical Center, Department of Orthopaedic Surgery & Rehabilitation, Maywood, IL, USA.
Michael Murphy

NHSBT Oxford, John Radcliffe Hospital, Oxford, UK.
Michael Scheidt
Krishin Shivdasani

Loyola University Medical Center, Department of Orthopaedic Surgery & Rehabilitation, Maywood, IL, USA.
Joshua Anderson

Loyola University Medical Center, Department of Orthopaedic Surgery & Rehabilitation, Maywood, IL, USA.
Nickolas Garbis

Loyola University Medical Center, Department of Orthopaedic Surgery & Rehabilitation, Maywood, IL, USA.
Dane Salazar

Loyola University Medical Center, Department of Orthopaedic Surgery & Rehabilitation, Maywood, IL, USA.

Keywords

Aged Aged, 80 and over Algorithms Arthroplasty, Replacement, Shoulder Artificial Intelligence Female Humans Linear Models Machine Learning Male Middle Aged Prosthesis Design Retrospective Studies Shoulder Joint Shoulder Prosthesis

External Resources

View on PubMed Access via DOI PubMed (39106479)

Artificial Intelligence Machine Learning Algorithms Versus Standard Linear Demographic Analysis in Predicting Implant Size of Anatomic and Reverse Total Shoulder Arthroplasty.

Abstract

Authors

Keywords

External Resources

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