Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip

Potential benefits, unintended consequences, and future roles of artificial intelligence in orthopaedic surgery research

“…For example, by applying AI to image recognition, it may be possible to save time and resources by allowing for the development of preoperative risk assessment or surveillance tools. 3 It is undeniable that big data will transform medicine, and though high-quality data is imperative, ultimately all data must be appropriately analyzed, interpreted appropriately, and allow for change in the way we practice medicine. To this end, there currently exists much “hype” as to the potential of machine-learning; however, this currently remains disproportionate to the translation of such models in clinical practice.…”

Artificial intelligence in arthroplasty

“…While accurate identification of the manufacturer and type of implant is required for preoperative planning of revision arthroplasty, it is estimated that surgeons are unable to recognize the implant preoperatively and intraoperatively in about 10 and 2% of cases, respectively. Inability to identify implants can lead to unpreparedness which may contribute to increased surgical time, perioperative morbidity, and overall healthcare costs [45][46][47]. Deep learning has been shown its ability to recognize implant manufacturer and design in hip and knee arthroplasty [45][46][47][48][49].…”

“…Inability to identify implants can lead to unpreparedness which may contribute to increased surgical time, perioperative morbidity, and overall healthcare costs [45][46][47]. Deep learning has been shown its ability to recognize implant manufacturer and design in hip and knee arthroplasty [45][46][47][48][49].…”

“…In hip arthroplasty, deep learning algorithms are also capable of recognizing THA implants based on AP radiograph. Although the classification is based solely on identifying the design characteristics of the femoral stem, studies have found the results to be very accurate, even reaching an accuracy rate of 99.6 to 100% [45,47,49,50].…”

Letter to the Editor on “Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip”

“…We read with great interest the article by Jaret et al [1], wherein the author described a newly developed deep learning model based on 1972 retrospectively collected anterior-posterior plain radiographs from four institutes using convolutional neural network (CNN). This newly developed model can help distinguish 18 hip replacement models from four industry-leading manufacturers with an encouraging degree of accuracy, sensitivity, and specificity.…”