Jaret M Karnuta1, Bryan C Luu2, Alexander L Roth1, Heather S Haeberle3, Antonia F Chen4, Richard Iorio4, Jonathan L Schaffer1, Michael A Mont5, Brendan M Patterson1, Viktor E Krebs1, Prem N Ramkumar6. 1. Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH. 2. Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH; Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, TX. 3. Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH; Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY. 4. Department of Orthopaedic Surgery, Brigham & Women''s Hospital, Boston, MA. 5. Department of Orthopaedic Surgery, Lenox Hill Hospital, Northwell Health, New York, NY. 6. Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, OH; Department of Orthopaedic Surgery, Brigham & Women''s Hospital, Boston, MA.
Abstract
BACKGROUND: Revisions and reoperations for patients who have undergone total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and distal femoral replacement (DFR) necessitates accurate identification of implant manufacturer and model. Failure risks delays in care, increased morbidity, and further financial burden. Deep learning permits automated image processing to mitigate the challenges behind expeditious, cost-effective preoperative planning. Our aim was to investigate whether a deep-learning algorithm could accurately identify the manufacturer and model of arthroplasty implants about the knee from plain radiographs. METHODS: We trained, validated, and externally tested a deep-learning algorithm to classify knee arthroplasty implants from one of 9 different implant models from retrospectively collected anterior-posterior (AP) plain radiographs from four sites in one quaternary referral health system. The performance was evaluated by calculating the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, and accuracy when compared with a reference standard of implant model from operative reports. RESULTS: The training and validation data sets were comprised of 682 radiographs across 424 patients and included a wide range of TKAs from the four leading implant manufacturers. After 1000 training epochs by the deep-learning algorithm, the model discriminated nine implant models with an AUC of 0.99, accuracy 99%, sensitivity of 95%, and specificity of 99% in the external-testing data set of 74 radiographs. CONCLUSIONS: A deep learning algorithm using plain radiographs differentiated between 9 unique knee arthroplasty implants from four manufacturers with near-perfect accuracy. The iterative capability of the algorithm allows for scalable expansion of implant discriminations and represents an opportunity in delivering cost-effective care for revision arthroplasty.
BACKGROUND: Revisions and reoperations for patients who have undergone total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and distal femoral replacement (DFR) necessitates accurate identification of implant manufacturer and model. Failure risks delays in care, increased morbidity, and further financial burden. Deep learning permits automated image processing to mitigate the challenges behind expeditious, cost-effective preoperative planning. Our aim was to investigate whether a deep-learning algorithm could accurately identify the manufacturer and model of arthroplasty implants about the knee from plain radiographs. METHODS: We trained, validated, and externally tested a deep-learning algorithm to classify knee arthroplasty implants from one of 9 different implant models from retrospectively collected anterior-posterior (AP) plain radiographs from four sites in one quaternary referral health system. The performance was evaluated by calculating the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, and accuracy when compared with a reference standard of implant model from operative reports. RESULTS: The training and validation data sets were comprised of 682 radiographs across 424 patients and included a wide range of TKAs from the four leading implant manufacturers. After 1000 training epochs by the deep-learning algorithm, the model discriminated nine implant models with an AUC of 0.99, accuracy 99%, sensitivity of 95%, and specificity of 99% in the external-testing data set of 74 radiographs. CONCLUSIONS: A deep learning algorithm using plain radiographs differentiated between 9 unique knee arthroplasty implants from four manufacturers with near-perfect accuracy. The iterative capability of the algorithm allows for scalable expansion of implant discriminations and represents an opportunity in delivering cost-effective care for revision arthroplasty.
Authors: Florian Hinterwimmer; Igor Lazic; Christian Suren; Michael T Hirschmann; Florian Pohlig; Daniel Rueckert; Rainer Burgkart; Rüdiger von Eisenhart-Rothe Journal: Knee Surg Sports Traumatol Arthrosc Date: 2022-01-10 Impact factor: 4.114