Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was \"What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?\" Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes.

UNASSIGNED: Demand for total shoulder arthroplasty (TSA) has risen significantly and is projected to continue growing. From 2012 to 2017, the incidence of reverse total shoulder arthroplasty (rTSA) rose from 7.3 cases per 100,000 to 19.3 per 100,000. Anatomical TSA saw a growth from 9.5 cases per 100,000 to 12.5 per 100,000. Failure to identify implants in a timely manner can increase operative time, cost and risk of complications. Several machine learning models have been developed to perform medical image analysis. However, they have not been widely applied in shoulder surgery. The authors developed a machine learning model to identify shoulder implant manufacturers and type from anterior-posterior X-ray images.
UNASSIGNED: The model deployed was a convolutional neural network (CNN), which has been widely used in computer vision tasks. 696 radiographs were obtained from a single institution. 70% were used to train the model, while evaluation was done on 30%.
UNASSIGNED: On the evaluation set, the model performed with an overall accuracy of 93.9% with positive predictive value, sensitivity and F-1 scores of 94% across 10 different implant types (4 reverse, 6 anatomical). Average identification time was 0.110 s per implant.
UNASSIGNED: This proof of concept study demonstrates that machine learning can assist with preoperative planning and improve cost-efficiency in shoulder surgery.

Identifying the right accessories for installing the dental implant is a vital element that impacts the sustainability and the reliability of the dental prosthesis when the medical case of a patient is not comprehensive. Dentists need to identify the implant manufacturer from the x-ray image to determine further treatment procedures. Identifying the manufacturer is a high-pressure task under the scaling volume of patients pending in the queue for treatment. To reduce the burden on the doctors, a dental implant identification system is built based on a new proposed thinner VGG model with an on-demand client-server structure. We propose a thinner version of VGG16 called TVGG by reducing the number of neurons in the dense layers to improve the system\'s performance and gain advantages from the limited texture and patterns in the dental radiography images. The outcome of the proposed system is compared with the original pre-trained VGG16 to verify the usability of the proposed system.

Artificial Intelligence (AI)/Machine Learning (ML) applications have been proven efficient to improve diagnosis, to stratify risk, and to predict outcomes in many respective medical specialties, including in orthopaedics.
Regarding hip and knee reconstruction surgery, AI/ML have not made it yet to clinical practice. In this review, we present sound AI/ML applications in the field of hip and knee degenerative disease and reconstruction. From osteoarthritis (OA) diagnosis and prediction of its advancement, clinical decision-making, identification of hip and knee implants to prediction of clinical outcome and complications following a reconstruction procedure of these joints, we report how AI/ML systems could facilitate data-driven personalized care for our patients.

UNASSIGNED: Identification of implant model from primary knee arthroplasty in pre-op planning of revision surgery is a challenging task with added delay. The direct impact of this inability to identify the implants in time leads to the increase in complexity in surgery. Deep learning in the medical field for diagnosis has shown promising results in getting better with every iteration. This study aims to find an optimal solution for the problem of identification of make and model of knee arthroplasty prosthesis using automated deep learning models.
UNASSIGNED: Deep learning algorithms were used to classify knee arthroplasty implant models. The training, validation and test comprised of 1078 radiographs with a total of 6 knee arthroplasty implant models with anterior-posterior (AP) and lateral views. The performance of the model was calculated using accuracy, sensitivity, and area under the receiver-operating characteristic curve (AUC), which were compared against multiple models trained for comparative in-depth analysis with saliency maps for visualization.
UNASSIGNED: After training for a total of 30 epochs on all 6 models, the model performing the best obtained an accuracy of 96.38%, the sensitivity of 97.2% and AUC of 0.985 on an external testing dataset consisting of 162 radiographs. The best performing model correctly and uniquely identified the implants which could be visualized using saliency maps.
UNASSIGNED: Deep learning models can be used to differentiate between 6 knee arthroplasty implant models. Saliency maps give us a better understanding of which regions the model is focusing on while predicting the results.

OBJECTIVE: A crucial step in the preoperative planning for a revision total hip replacement (THR) surgery is the accurate identification of the 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.
METHODS: In this study, we present a novel approach to identifying THR femoral implants\' design from plain radiographs using a convolutional neural network (CNN). We evaluated a total of 402 radiographs of nine different THR implant designs including, Accolade II (130 radiographs), Corail (89 radiographs), M/L Taper (31 radiographs), Summit (31 radiographs), Anthology (26 radiographs), Versys (26 radiographs), S-ROM (24 radiographs), Taperloc Standard Offset (24 radiographs), and Taperloc High Offset (21 radiographs). We implemented a transfer learning approach and adopted a DenseNet-201 CNN architecture by replacing the final classifier with nine fully connected neurons. Furthermore, we used saliency maps to explain the CNN decision-making process by visualizing the most important pixels in a given radiograph on the CNN\'s outcome. We also compared the CNN\'s performance with three board-certified and fellowship-trained orthopedic surgeons.
RESULTS: The CNN achieved the same or higher performance than at least one of the surgeons in identifying eight of nine THR implant designs and underperformed all of the surgeons in identifying one THR implant design (Anthology). Overall, the CNN achieved a lower Cohen\'s kappa (0.78) than surgeon 1 (1.00), the same Cohen\'s kappa as surgeon 2 (0.78), and a slightly higher Cohen\'s kappa than surgeon 3 (0.76) in identifying all the nine THR implant designs. Furthermore, the saliency maps showed that the CNN generally focused on each implant\'s unique design features to make a decision. Regarding the time spent performing the implant identification, the CNN accomplished this task in ~0.06 s per radiograph. The surgeon\'s identification time varied based on the method they utilized. When using their personal experience to identify the THR implant design, they spent negligible time. However, the identification time increased to an average of 8.4 min (standard deviation 6.1 min) per radiograph when they used another identification method (online search, consulting with the orthopedic company representative, and using image atlas), which occurred in about 17% of cases in the test subset (40 radiographs).
CONCLUSIONS: CNNs such as the one developed in this study 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 in some cases improving identification accuracy.

The surgical management of complications surrounding patients who have undergone hip arthroplasty necessitates accurate identification of the femoral implant manufacturer and model. Failure to do so risks delays in care, increased morbidity, and further economic burden. Because few arthroplasty experts can confidently classify implants using plain radiographs, automated image processing using deep learning for implant identification may offer an opportunity to improve the value of care rendered.
We trained, validated, and externally tested a deep-learning system to classify total hip arthroplasty and hip resurfacing arthroplasty femoral implants as one of 18 different manufacturer models from 1972 retrospectively collected anterior-posterior (AP) plain radiographs from 4 sites in one quaternary referral health system. From these radiographs, 1559 were used for training, 207 for validation, and 206 for external testing. Performance was evaluated by calculating the area under the receiver-operating characteristic curve, sensitivity, specificity, and accuracy, as compared with a reference standard of implant model from operative reports with implant serial numbers.
The training and validation data sets from 1715 patients and 1766 AP radiographs included 18 different femoral components across four leading implant manufacturers and 10 fellowship-trained arthroplasty surgeons. After 1000 training epochs by the deep-learning system, the system discriminated 18 implant models with an area under the receiver-operating characteristic curve of 0.999, accuracy of 99.6%, sensitivity of 94.3%, and specificity of 99.8% in the external-testing data set of 206 AP radiographs.
A deep-learning system using AP plain radiographs accurately differentiated among 18 hip arthroplasty models from four industry leading manufacturers.

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.
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.
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.
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.

OBJECTIVE: To develop and evaluate the performance of deep convolutional neural networks (DCNN) to detect and identify specific total shoulder arthroplasty (TSA) models.
METHODS: We included 482 radiography studies obtained from publicly available image repositories with native shoulders, reverse TSA (RTSA) implants, and five different TSA models. We trained separate ResNet DCNN-based binary classifiers to (1) detect the presence of shoulder arthroplasty implants, (2) differentiate between TSA and RTSA, and (3) differentiate between the five TSA models, using five individual classifiers for each model, respectively. Datasets were divided into training, validation, and test datasets. Training and validation datasets were 20-fold augmented. Test performances were assessed with area under the receiver-operating characteristic curves (AUC-ROC) analyses. Class activation mapping was used to identify distinguishing imaging features used for DCNN classification decisions.
RESULTS: The DCNN for the detection of the presence of shoulder arthroplasty implants achieved an AUC-ROC of 1.0, whereas the AUC-ROC for differentiation between TSA and RTSA was 0.97. Class activation map analysis demonstrated the emphasis on the characteristic arthroplasty components in decision-making. DCNNs trained to distinguish between the five TSA models achieved AUC-ROCs ranging from 0.86 for Stryker Solar to 1.0 for Zimmer Bigliani-Flatow with class activation map analysis demonstrating an emphasis on unique implant design features.
CONCLUSIONS: DCNNs can accurately identify the presence of and distinguish between TSA & RTSA, and classify five specific TSA models with high accuracy. The proof of concept of these DCNNs may set the foundation for an automated arthroplasty atlas for rapid and comprehensive model identification.

Identifying the design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time-consuming and prone to error. Failure to identify the implant design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant designs. Such CNN can be used to automatically identify the design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners\' time, and reduce healthcare costs.