Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network.

Area of Science:

• Orthopedic surgery
• Artificial intelligence in medicine
• Medical imaging analysis

Background:

• Preoperative planning for revision total joint arthroplasty relies on accurate identification of failed implant designs.
• Manual identification from radiographs is time-consuming, error-prone, and can negatively impact surgical outcomes and costs.
• Current methods lack efficiency and accuracy in identifying diverse implant designs.

Purpose of the Study:

• To develop and validate a fully automatic and interpretable deep convolutional neural network (CNN) for identifying total hip replacement (THR) implant designs.
• To assess the accuracy and efficiency of the CNN in classifying common THR implant designs from plain radiographs.
• To demonstrate the potential of AI in improving preoperative planning for revision arthroplasty.

Main Methods:

• A deep convolutional neural network (CNN) model was developed for image analysis.
• The CNN was trained and tested on plain radiographic images of total hip replacement (THR) implants.
• The model's performance was evaluated based on accuracy in identifying different implant designs.

Main Results:

• The developed CNN achieved 100% accuracy in identifying three common total hip replacement (THR) implant designs.
• The automated identification process takes only a few seconds.
• The system demonstrated high precision and reliability in implant design classification.

Conclusions:

• A fully automatic and interpretable CNN can accurately and rapidly identify THR implant designs from radiographs.
• This AI-driven approach significantly enhances preoperative planning for revision total joint arthroplasty.
• The technology promises to improve patient outcomes, reduce surgical complications, and lower healthcare expenditures.