Can artificial intelligence algorithms recognize knee arthroplasty implants from X-ray radiographs?

Year 2023, Volume: 5 Issue: 4, 479 - 483, 27.10.2023

Fatih Gölgelioğlu , Aydoğan Aşkın , Mehmet Cihat Gündoğdu , Mehmet Fatih Uzun , Bige Kağan Dedetürk , Mustafa Yalın

https://doi.org/10.38053/acmj.1356979

Abstract

Aims: This study aimed to investigate the use of a convolutional neural network (CNN) deep learning approach to accurately identify total knee arthroplasty (TKA) implants from X-ray radiographs.
Methods: This retrospective study employed a deep learning CNN system to analyze pre-revision and post-operative knee X-rays from TKA patients. We excluded cases involving unicondylar and revision knee replacements, as well as low-quality or unavailable X-ray images and those with other implants. Ten cruciate-retaining TKA replacement models were assessed from various manufacturers. The training set comprised 69% of the data, with the remaining 31% in the test set, augmented due to limited images. Evaluation metrics included accuracy and F1 score, and we developed the software in Python using the TensorFlow library for the CNN method. A computer scientist with AI expertise managed data processing and testing, calculating specificity, sensitivity, and accuracy to assess CNN performance.
Results: In this study, a total of 282 AP and lateral X-rays from 141 patients were examined, encompassing 10 distinct knee prosthesis models from various manufacturers, each with varying X-ray counts. The CNN technique exhibited flawless accuracy, achieving a 100% identification rate for both the manufacturer and model of TKA across all 10 different models. Furthermore, the CNN method demonstrated exceptional specificity and sensitivity, consistently reaching 100% for each individual implant model.
Conclusion: This study underscores the impressive capacity of deep learning AI algorithms to precisely identify knee arthroplasty implants from X-ray radiographs. It highlights AI’s ability to detect subtle changes imperceptible to humans, execute precise computations, and handle extensive data. The accurate recognition of knee replacement implants using AI algorithms prior to revision surgeries promises to enhance procedure efficiency and outcomes.

Keywords

Artroplasty, implant, artificial intelligence, detection

Ethical Statement

The current study was carried out with the permission of the Fırat University Medical Faculty Ethics Committee (Date: 29.12.2022, Decision No: 16-21).

References

• Sloan M, Premkumar A, Sheth NP. Projected volume of primary total joint arthroplasty in the U.S., 2014 to 2030. J Bone Joint Surg Am. 2018;100(17):1455-1460.
• Postler A, Lützner C, Beyer F, Tille E, Lützner J. Analysis of total knee arthroplasty revision causes. BMC Musculoskelet Disord. 2018;19(1):55.
• Delanois RE, Mistry JB, Gwam CU, Mohamed NS, Choksi US, Mont MA. Current epidemiology of revision total knee arthroplasty in the United States. J Arthroplasty. 2017;32(9):2663-2668.
• Wilson N, Broatch J, Jehn M, Davis C, 3rd. National projections of time, cost and failure in implantable device identification: Consideration of unique device identification use. Healthc (Amst). 2015;3(4):196-201.
• Wilson NA, Jehn M, York S, Davis CM, 3rd. Revision total hip and knee arthroplasty implant identification: implications for use of Unique Device Identification 2012 AAHKS member survey results. J Arthroplasty. 2014;29(2):251-5.
• Wang J, Liu S. Visual information computing and processing model based on artificial neural network. Comput Intell Neurosci. 2022;2022:4713311.
• Rohde S, Münnich N. Artificial intelligence in orthopaedic and trauma surgery imaging. Orthopadie (Heidelb). 2022;51(9):748-756.
• Lisacek-Kiosoglous AB, Powling AS, Fontalis A, Gabr A, Mazomenos E, Haddad FS. Artificial intelligence in orthopaedic surgery. Bone Joint Res. 2023;12(7):447-454.
• Murphy M, Killen C, Burnham R, Sarvari F, Wu K, Brown N. Artificial intelligence accurately identifies total hip arthroplasty implants: a tool for revision surgery. Hip Int. 2022;32(6):766-770.
• Karnuta JM, Luu BC, Roth AL, et al. Artificial intelligence to identify arthroplasty implants from radiographs of the knee. J Arthroplasty. 2021;36(3):935-940.
• Yi PH, Wei J, Kim TK, et al. Automated detection & classification of knee arthroplasty using deep learning. Knee. 2020;27(2):535-542.
• Gurung B, Liu P, Harris PDR, et al. Artificial intelligence for image analysis in total hip and total knee arthroplasty: a scoping review. Bone Joint J. 2022;104-B(8):929-937.
• Nich C, Behr J, Crenn V, Normand N, Mouchère H, d’Assignies G. Applications of artificial intelligence and machine learning for the hip and knee surgeon: current state and implications for the future. Int Orthop. 2022;46(5):937-944.
• von Eisenhart-Rothe R, Hinterwimmer F, Graichen H, Hirschmann MT. Artificial intelligence and robotics in TKA surgery: promising options for improved outcomes?. Knee Surg Sports Traumatol Arthrosc. 2022;30(8):2535-2537.
• Lakhani P. Deep convolutional neural networks for endotracheal tube position and X-ray image classification: challenges and opportunities. J Digit Imaging. 2017;30(4):460-468.
• Lee H, Mansouri M, Tajmir S, Lev MH, Do S. A deep-learning system for fully-automated peripherally inserted central catheter (PICC) tip detection. J Digit Imaging. 2018;31(4):393-402.
• Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574-582.
• Klemt C, Uzosike AC, Cohen-Levy WB, Harvey MJ, Subih MA, Kwon YM. The ability of deep learning models to identify total hip and knee arthroplasty implant design from plain radiographs. J Am Acad Orthop Surg. 2022;30(9):409-415.
• Purnomo G, Yeo SJ, Liow MHL. Artificial intelligence in arthroplasty. Arthroplasty. 2021;3(1):37.
• Clement ND, Simpson A. Artificial intelligence in orthopaedics. Bone Joint Res. 2023;12(8):494-496.
• Kulkarni S, Seneviratne N, Baig MS, Khan AHA. Artificial intelligence in medicine: where are we now? Acad Radiol. 2020; 27(1):62-70.
• Myers TG, Ramkumar PN, Ricciardi BF, Urish KL, Kipper J, Ketonis C. Artificial intelligence and orthopaedics: an introduction for clinicians. J Bone Joint Surg Am. 2020;102(9):830-840.
• Lenguerrand E, Whitehouse MR, Kunutsor SK, et al. Mortality and re-revision following single-stage and two-stage revision surgery for the management of infected primary knee arthroplasty in England and Wales: evidence from the National Joint Registry. Bone Joint Res. 2022;11(10):690-699.