A Systematical Review of the Literature on Screw Extraction from Implants During Orthopedic Surgery

Implant removal during fracture healing has always been a contentious but practical subject. Cold welding and the absence of screw heads make it difficult to remove these implants. Removing the screws associated with these complexities is, nevertheless, without doubt. Inevitably, there will come a time when such metal apparatus must be removed, and this will be done for various reasons and at different times, each of which is likely inconvenient. Methodology: Over five years (2014–2019), a study was conducted on medical records involving the removal of musculoskeletal implants. Data on segments, clinical nuances, evacuation indicators, and post-employment challenges were collected. Results: Thirty patients’complete medical histories were analyzed. Males outnumbered females by a ratio of 1.7 to 1, and ages varied from 6 to 76 (mean = 30.08).The femur, tibia, humerus, distal span, and clavicle were common removal sites. Nails (20.8%) and edge plates (54.2%) followed plates and screws as the most often discarded hardware (12.5%). Disease was the most frequently identified symptom, with 41.7%, followed by equipment dissatisfaction (28.6%) and patient requests (11.9%). Diseases related to work-related injuries accounted for 57% of the post-useful discomfort rate, while distress accounted for 15% and repeated fractures made up the remaining 2% (7%). The purpose of this work is to use artificial intelligence (AI) tools to thoroughly review the available literature on screw extraction during orthopedic surgery. The ability of AI to extract knowledge, interpret data, and recognize patterns makes it an effective tool for studying enormous amounts of scientific material. This review seeks to offer a thorough overview of the most recent developments, methods, and results in relation to screw extraction operations using AI-powered algorithms.