A Hybrid Multi-Task Deep Learning Framework HDMV-Svy-Net for Automated Knee Implant Analysis

Objectives: Total knee arthroplasty (TKA) is being increasingly accepted as a way to improve walking and quality of life for people with knee diseases. Implant-related complications affect the surgical result and long-term implant survival. Clinical intervention is important to accurately assess the condition of an implant by the manual interpretation of medical images which suffers from subjectivity, inter-observer variations, and the growing clinical workload. Due to these limitations, there is an increasing interest in using AI-based techniques for automatic orthopaedic image analysis. The purpose of this study is to propose an elaborate Deep Learning-based framework for classifying, segmenting, and localizing knee-implant-related conditions on radiographs. The proposed framework aims to improve diagnostic performance and enable reliable clinical decision support with transfer learning, deep feature extraction, feature refinement, ensemble learning and YOLOv11 architectures. Methods: The dataset of a radiographic knee implant is further classified into 4 different types based on the condition of the implant. In the unified diagnostic framework, we incorporated a wide range of convolutional neural networks and transformer-based architectures that could effectively capture both local and global image representations.The image pre-processing techniques are used to improve the image quality and the model performance. Following this, various pretrained architectures were deployed to extract deep representative features. To enhance robustness and generalization performance, soft and hard voting ensemble strategies were used to combine refined feature representations. Classification and segmentation models based on YOLOv11 were employed to identify the condition of the implant to localize and segment the abnormalities related to it. Results: The experimental results reveal the effectiveness of the proposed framework HDMV-SVY-Net in both classification and segmentation tasks. YOLOv11x-cls, among the classifiers, achieved a score of 97.86% with a precision of 0.978, recall of 0.975, F1 of 0.976 and MCC of 0.971. In a similar vein, YOLOv11x-seg performed the best, achieving a precision of 0.96, recall of 0.94, mAP@0.5 of 0.97, mAP@0.5:0.95 of 0.89, segmentation mAP@0.5 of 0.97, segmentation mAP@0.5:0.95 of 0.88, IoU of 0.89 and Dice score of 0.942 at an inference time of only 7.0 ms. All this indicates the framework’s capability to achieve highly accurate classification while also providing precise segmentation and localisation of clinically significant implant anomalies. Conclusion: In conclusion, the integration of transfer learning, feature refinement, ensemble learning, and YOLOv11-based detection significantly improves the automated assessment of knee implants. The new framework not only achieves a high predictive accuracy but also provides reliable spatial characterization of implant-related pathologies, thus facilitating clinical decision-making. The developed system has good potential as an intelligent computer-aided diagnostic for orthopaedic imaging applications. Future studies will aim to increase the number of datasets from multiple centres to improve the generalizability of the models across various clinical settings and to validate the current framework in real-world orthopaedic practice.