Comparing performance of deep convolutional neural network with orthopaedic surgeons on identification of total hip prosthesis design from plain radiographs

Abstract

A crucial step in preoperative planning for a revision total hip replacement (THR) surgery is accurate identification of failed implant design, especially if one or more well-fixed/functioning components are to be retained. Manual identification of the implant design from preoperative radiographic images can be time-consuming and inaccurate, which can ultimately lead to increased operating room time, more complex surgery, and increased healthcare costs. No automated system has been developed to accurately and efficiently identify THR implant designs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of nine different THR femoral implants from plain radiographs using deep convolutional neural network (CNN). We also compared the CNN performance with three board-certified and fellowship trained orthopaedic surgeons. The CNN achieved on-par accuracy with the orthopaedic surgeons while being significantly faster. The need for additional training data for less distinct designs was also highlighted. Such CNN can be used to automatically identify the design of a failed THR femoral implant preoperatively in just a fraction of a second, saving time and improving identification accuracy.

Shoulder Implant Manufacturer Detection by Using Deep Learning: Proposed Channel Selection Layer

“…Borjali et al [18], in 2020, used deep convolutional neural networks to determine the design of a total hip prosthesis. The performance of the CNN model was compared with the responses of orthopedic surgeons.…”

Improved diagnosis of tibiofemoral cartilage defects on MRI images using deep learning

“…14 In general, machine learning refers to a series of mathematical algorithms that enable the machine to "learn" the relationship between input and output data without being explicitly told how to do so 14 . Deep learning is a subset of machine learning that is mainly concerned with image analysis and extracting knowledge from complex imaging data sets such as medical images [14][15][16] . Radiologists and orthopaedic surgeons have applied deep learning to provide automatic interpretations of medical images to improve their diagnostic accuracy and speed 17,18,19 .…”