PURPOSE: Total knee arthroplasty (TKA) is considered the gold standard treatment for end-stage knee osteoarthritis. Common complications associated with TKA include implant loosening and periprosthetic fractures, which often require revision surgery or fixation. Challenges arise when medical records related to the knee prosthesis are lost, making it difficult to plan for revision surgery effectively. This study aims to develop an artificial intelligence (AI) system to classify the types of knee prosthetic implants using plain radiographs. METHODS: This retrospective experimental study includes seven types of knee prostheses commonly used in our hospital. The artificial intelligence (AI) system was trained using YOLO (You Only Look Once) version 9, utilizing a dataset of 3228 post-operative and follow-up knee arthroplasty X-ray images. The plain radiographic images were augmented, resulting in a dataset of 25,800 images. Model parameters were fine-tuned to optimize performance for implant classification. RESULTS: The mean age of the patients was 62.8 years. Right knee arthroplasty was performed in 48.3% of cases, while left knee arthroplasty was performed in 51.7%. The images of knee prostheses comprised 50.9% of the dataset from the anteroposterior (AP) view and 49.1% from the lateral view. The AI model demonstrated exceptional performance metrics, achieving precision, recall, and accuracy rates of 100%, with an F1 score of 1. Additionally, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated to be 100%. CONCLUSION: This AI model successfully classifies knee prosthetic implants from plain radiographs. This capability serves as a valuable tool for surgeons, enabling precise planning for revision surgeries and periprosthetic fracture fixation surgery, ultimately contributing to improved patient outcomes. The high accuracy achieved by the AI underscores its potential to enhance surgical efficiency and effectiveness in managing knee arthroplasty complications.