Artificial Intelligence in Automated Identification of Prosthesis with Focus on Total Shoulder Arthroplasty Implants: A Survey

Okonkwo Ogochukwu John, Fatima Umar Zambuk, Abdulsalam Ya'u Gital, Mustapha Abdulrahman Lawal, Ismail Zahraddeen Yakubu

Abstract


Arthroplasty is a surgical technique that involves modifying, realigning, and replacing a bone's broken surface with a human-made, durable substance to alleviate discomfort and reestablish joint functionality. Knee, hip, and shoulder replacement are all examples of Arthroplasty surgery. Total Shoulder Arthroplasty is a procedure that is commonly performed to treat injured shoulder joints. Injuries, calcification, deterioration in the shoulder cartilage tissue, injury to surrounding bones, and severe arthritis are all common causes of shoulder damage and malfunction. There are currently several manufacturers of shoulder prostheses who create various types of this prosthesis to match varied conditions and patients. When the quality of the prosthesis deteriorates years after replacement, reoperation and revision may be required. Finding the model, structure, and prosthesis manufacturer to position them correctly is the main surgical stage to reduce the usual issues. The patient and the primary doctor at the other location or country may not be aware of the prosthesis model and manufacturer in cases where a patient migrates from the place or country where surgery was performed. As a result, determining the model of the prosthesis and the manufacturer requires a thorough inspection and visual comparison of radiological pictures. This method delays reoperation and correction, is time-consuming, and is error-prone. Numerous studies have shown how to use different deep learning approaches to categorize implants based on their manufacturer and identify the type and structure of the implants. Researchers have offered a number of detection models for the duration of stay in the hospital, payment schemes, practical results, and patient expectations, and so on, with the need for more optimal solutions. However, no complete evaluation of the automatic prediction, identification, and classification of prosthesis based on manufacturer exists. As a result, this study discusses the various detection models utilized in arthroplasty. The review also provides an overview and taxonomy of arthroplasty, as well as research gaps and prospects.


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References


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