Automatic Identification of Failure in Hip Replacement: An Artificial Intelligence Approach

Comparison of an AI-driven planning tool and manual radiographic measurements in total knee arthroplasty

Background

Accurate preoperative planning in total knee arthroplasty (TKA) is essential. Traditional manual radiographic planning can be time-consuming and potentially prone to inaccuracies. This study investigates the performance of an AI-based radiographic planning tool in comparison with manual measurements in patients undergoing total knee arthroplasty, using a retrospective observational design to assess reliability and efficiency.

Methods

We retrospectively compared the Autoplan tool integrated within the mediCAD software (mediCAD Hectec GmbH, Altdorf, Germany), routinely implemented in our institutional workflow, to manual measurements performed by two orthopedic specialists on pre- and postoperative radiographs of 100 patients who underwent elective TKA. The following parameters were measured: leg length, mechanical axis deviation (MAD), mechanical lateral proximal femoral angle (mLPFA), anatomical mechanical angle (AMA), mechanical lateral distal femoral angle (mLDFA), joint line convergence angle (JLCA), mechanical medial proximal tibial angle (mMPTA), and mechanical tibiofemoral angle (mTFA).Intraclass correlation coefficients (ICCs) were calculated to assess measurement reliability, and the time required for each method was recorded.

Results

The Autoplan tool demonstrated high reliability (ICC > 0.90) compared with manual measurements for linear parameters (e.g., leg length and MAD). However, the angular measurements of mLPFA, JLCA, and AMA exhibited poor reliability (ICC < 0.50) among all raters. The Autoplan tool significantly reduced the time required for measurements compared to manual measurements, with a mean time saving of 44.3 seconds per case (95 % CI: 43.5-45.1 seconds, p < 0.001).

Conclusion

AI-assisted tools like the Autoplan tool in mediCAD offer substantial time savings and demonstrate reliable measurements for certain linear parameters in preoperative TKA planning. However, the observed low reliability in some measurements, even amongst experienced human raters, suggests inherent challenges in the radiographic assessment of angular parameters. Further development is needed to improve the accuracy of automated angular measurements, and to address the inherent variability in their assessment.

Hip prosthesis failure prediction through radiological deep sequence learning

BACKGROUND

Existing deep learning studies for the automated detection of hip prosthesis failure only consider the last available radiographic image. However, using longitudinal data is thought to improve the prediction, by combining temporal and spatial components. The aim of this study is to develop artificial intelligence models for predicting hip implant failure from multiple subsequent plain radiographs.

METHODS

A cohort of 224 patients was considered for model development and a balanced cohort of 14 patients was used for external validation. A sequence of two or three anteroposterior radiographic images per patient was considered to track the prosthesis over time. A combination of a convolutional neural network (CNN) and a recurrent section was used. For the CNN, a pretrained autoencoder, a pretrained RadImageNet DenseNet and a pretrained custom DenseNet were considered. The recurrent section was implemented using either a single Gated Recurrent Unit (GRU) layer or a Long Short-Term Memory block.

RESULTS

Considering 3 images as input provided a positive predictive value (PPV) of 0.966 and an f1 score of 0.933 on the validation set. Regarding the 2-image models, using the postoperative and the last image resulted in PPV of 0.933 and f1 score of 0.918, whereas using the second-to-last image with the post-operative one reached a PPV of 0.882 and f1 score of 0.923. On the external validation set, the 3-image model reached an accuracy of 0.786.

CONCLUSION

This study demonstrated the potential of the developed models, based on a series of plain radiographs, to predict hip prosthesis failure.

Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis

The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across various organs while reducing physician workload. AI's error detection capabilities further enhance diagnostic accuracy. Looking ahead, the integration of AI with ultrasound is expected to deepen, promoting trends in standardization, personalized treatment, and intelligent healthcare, particularly in underserved areas. Despite its potential, comprehensive assessments of AI's diagnostic accuracy and ethical implications remain limited, necessitating rigorous evaluations to ensure effectiveness in clinical practice. This review provides a systematic evaluation of AI technologies in ultrasound medicine, highlighting their transformative potential to improve global healthcare outcomes.

Application of deep learning algorithms in classification and localization of implant cutout for the postoperative hip

OBJECTIVE

This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs.

MATERIALS AND METHODS

The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity.

RESULTS

The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions.

CONCLUSION

The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs.

Artificial Intelligence in Surgery: A Systematic Review of Use and Validation

Background: Artificial Intelligence (AI) holds promise for transforming healthcare, with AI models gaining increasing clinical use in surgery. However, new AI models are developed without established standards for their validation and use. Before AI can be widely adopted, it is crucial to ensure these models are both accurate and safe for patients. Without proper validation, there is a risk of integrating AI models into practice without sufficient evidence of their safety and accuracy, potentially leading to suboptimal patient outcomes. In this work, we review the current use and validation methods of AI models in clinical surgical settings and propose a novel classification system. Methods: A systematic review was conducted in PubMed and Cochrane using the keywords "validation", "artificial intelligence", and "surgery", following PRISMA guidelines. Results: The search yielded a total of 7627 articles, of which 102 were included for data extraction, encompassing 2,837,211 patients. A validation classification system named Surgical Validation Score (SURVAS) was developed. The primary applications of models were risk assessment and decision-making in the preoperative setting. Validation methods were ranked as high evidence in only 45% of studies, and only 14% of the studies provided publicly available datasets. Conclusions: AI has significant applications in surgery, but validation quality remains suboptimal, and public data availability is limited. Current AI applications are mainly focused on preoperative risk assessment and are suggested to improve decision-making. Classification systems such as SURVAS can help clinicians confirm the degree of validity of AI models before their application in practice.

Leveraging transfer learning for predicting total knee arthroplasty failure from post-operative radiographs

Purpose

The incidence of both primary and revision total knee arthroplasty (TKA) is expected to rise, making early recognition of TKA failure crucial to prevent extensive revision surgeries. This study aims to develop a deep learning (DL) model to predict TKA failure using radiographic images.

Methods

Two patient cohorts who underwent primary TKA were retrospectively collected: one was used for the model development and the other for the external validation. Each cohort encompassed failed and non-failed subjects, according to the need for TKA revision surgery. Moreover, for each patient, one anteroposterior and one lateral radiographic view obtained during routine TKA follow-up, were considered. A transfer learning fine-tuning approach was employed. After pre-processing, the images were analyzed using a convolutional neuronal network (CNN) that was originally developed for predicting hip prosthesis failure and was based on the Densenet169 pre-trained on Imagenet. The model was tested on 20% of the images of the first cohort and externally validated on the images of the second cohort. Metrics, such as accuracy, sensitivity, specificity and area under the receiving operating characteristic curve (AUC), were calculated for the final assessment.

Results

The trained model correctly classified 108 out of 127 images in the test set, providing a classification accuracy of 0.85, sensitivity of 0.80, specificity of 0.89 and AUC of 0.86. Moreover, the model correctly classified 1547 out of 1937 in the external validation set, providing a balanced accuracy of 0.79, sensitivity of 0.80, specificity of 0.78 and AUC of 0.86.

Conclusions

The present DL model predicts TKA failure with moderate accuracy, regardless of the cause of revision surgery. Additionally, the effectiveness of the transfer learning fine-tuning approach, leveraging a previously developed DL model for hip prosthesis failure, has been successfully demonstrated.

Level of Evidence

Level III, diagnostic study.

Guideline for RSA and CT-RSA implant migration measurements: an update of standardizations and recommendations

Opening remarks: These guidelines are the result of discussions within a diverse group of RSA researchers. They were approved in December 2023 by the board and selected members of the International Radiostereometry Society to update the guidelines by Valstar et al. [1]. By adhering to these guidelines, RSA studies will become more transparent and consistent in execution, presentation, reporting, and interpretation. Both authors and reviewers of scientific papers using RSA may use these guidelines, summarized in the Checklist, as a reference. Deviations from these guidelines should have the underlying rationale stated.

Changes in serum inflammatory factors after hip arthroplasty and analysis of risk factors for prosthesis loosening

OBJECTIVE

To explore the relationship of serum levels of IL-1β, IL-6, and TNF-α with prosthesis loosening after hip arthroplasty, and to establish a predictive model for prosthesis loosening.

METHODS

We retrospectively analyzed the data of 501 patients who underwent hip arthroplasty in Xi'an International Medical Center Hospital from January 2020 to August 2022. Based on radiological diagnosis, the patients were divided into a prosthesis loosening group and a non-loosening group. Clinical data including postoperative serum levels of inflammatory cytokines were collected. Univariant analysis, Lasso regression, decision tree, and random forest models were used to screen feature variables. Based on the screening results, a nomogram model for predicting the risk of prosthesis loosening was established and then validated using ROC curve, and calibration curve, and other methods.

RESULTS

There were 50 cases in the loosening group and 451 cases in the non-loosening group. Postoperative levels of IL-1β, IL-6, and TNF-α were found to be significantly higher in the loosening group (P<0.0001). Univariant analysis showed that osteoporosis and postoperative infection were risk factors for prosthesis loosening (P<0.001). The machine learning algorithm identified osteoporosis, postoperative infection, IL-1β, IL-6, and TNF-α as 5 relevant variables. The predictive model based on these 5 variables exhibited an area under the ROC curve of 0.763. The calibration curve and DCA curve verified the accuracy and practicality of the model.

CONCLUSION

Serum levels of IL-1β, IL-6, and TNF-α were significantly elevated in patients with postoperative prosthesis loosening. Osteoporosis, postoperative infection, and inflammatory cytokines are independent risk factors for prosthesis loosening. The predictive model we established through machine learning can effectively determine the risk of prosthesis loosening. Monitoring inflammatory cytokines and postoperative infections, combined with prevention of osteoporosis, can help reduce the risk of prosthesis loosening.

AI-based hip prosthesis failure prediction through evolutional radiological indices

INTRODUCTION

This study aimed to develop artificial intelligence models for predicting hip implant failure from radiological features. Analyzing the evolution of the periprosthetic bone and implant's position throughout the entire follow-up period has shown the potential to be more relevant in outcome prediction than simply considering the latest radiographic images. Thus, we investigated an AI-based model employing a small set of evolutional parameters derived from conventional radiological features to predict hip prosthesis failure.

MATERIALS AND METHODS

One hundred sixty-nine radiological features were annotated from historical anteroposterior and lateral radiographs for 162 total hip arthroplasty patients, 32 of which later underwent implant failure. Linear regression on each patient's chronologically sorted radiological features was employed to derive 169 corresponding evolutional parameters per image. Three sets of machine learning predictors were developed: one employing the original features (standard model), one the evolutional ones (evolutional model), and the last their union (hybrid model). Each set included a model employing all the available features (full model) and a model employing the few most predictive ones according to Gini importance (minimal model).

RESULTS

The evolutional and hybrid predictors resulted highly effective (area under the ROC curve (AUC) of full models = 0.94), outperforming the standard one, whose AUC was only 0.82. The minimal hybrid model, employing just four features, three of which evolutional, scored an AUC of 0.95, proving even more accurate than the full one, exploiting 173 features. This tool could be shaped to be either highly specific (sensitivity: 80%, specificity: 98.6%) or highly sensitive (sensitivity: 90%, specificity: 92.4%).

CONCLUSION

The proposed predictor may represent a highly sensitive screening tool for clinicians, capable to predict THA failure with an advance between a few months and more than a year through only four radiological parameters, considering either their value at the latest visit or their evolution through time.

A multi-branch network to detect post-operative complications following hip arthroplasty on X-ray images

Background: Postoperative complications following total hip arthroplasty (THA) often require revision surgery. X-rays are usually used to detect such complications, but manually identifying the location of the problem and making an accurate assessment can be subjective and time-consuming. Therefore, in this study, we propose a multi-branch network to automatically detect postoperative complications on X-ray images. Methods: We developed a multi-branch network using ResNet as the backbone and two additional branches with a global feature stream and a channel feature stream for extracting features of interest. Additionally, inspired by our domain knowledge, we designed a multi-coefficient class-specific residual attention block to learn the correlations between different complications to improve the performance of the system. Results: Our proposed method achieved state-of-the-art (SOTA) performance in detecting multiple complications, with mean average precision (mAP) and F1 scores of 0.346 and 0.429, respectively. The network also showed excellent performance at identifying aseptic loosening, with recall and precision rates of 0.929 and 0.897, respectively. Ablation experiments were conducted on detecting multiple complications and single complications, as well as internal and external datasets, demonstrating the effectiveness of our proposed modules. Conclusion: Our deep learning method provides an accurate end-to-end solution for detecting postoperative complications following THA.

Machine Learning for Detecting Total Knee Arthroplasty Implant Loosening on Plain Radiographs

(1) Background: The purpose of this study was to investigate whether the loosening of total knee arthroplasty (TKA) implants could be detected accurately on plain radiographs using a deep convolution neural network (CNN). (2) Methods: We analyzed data for 100 patients who underwent revision TKA due to prosthetic loosening at a single institution from 2012 to 2020. We extracted 100 patients who underwent primary TKA without loosening through a propensity score, matching for age, gender, body mass index, operation side, and American Society of Anesthesiologists class. Transfer learning was used to prepare a detection model using a pre-trained Visual Geometry Group (VGG) 19. For transfer learning, two methods were used. First, the fully connected layer was removed, and a new fully connected layer was added to construct a new model. The convolutional layer was frozen without training, and only the fully connected layer was trained (transfer learning model 1). Second, a new model was constructed by adding a fully connected layer and varying the range of freezing for the convolutional layer (transfer learning model 2). (3) Results: The transfer learning model 1 gradually increased in accuracy and ultimately reached 87.5%. After processing through the confusion matrix, the sensitivity was 90% and the specificity was 100%. Transfer learning model 2, which was trained on the convolutional layer, gradually increased in accuracy and ultimately reached 97.5%, which represented a better improvement than for model 1. Processing through the confusion matrix affirmed that the sensitivity was 100% and the specificity was 97.5%. (4) Conclusions: The CNN algorithm, through transfer learning, shows high accuracy for detecting the loosening of TKA implants on plain radiographs.

Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions

As the global health care system grapples with steadily rising costs, increasing numbers of admissions, and the chronic defection of doctors and nurses from the profession, appropriate measures need to be put in place to reverse this course before it is too late [...].

Combining deep learning and machine learning for the automatic identification of hip prosthesis failure: Development, validation and explainability analysis

AIM

Revision hip arthroplasty has a less favorable outcome than primary total hip arthroplasty and an understanding of the timing of total hip arthroplasty failure may be helpful. The aim of this study is to develop a combined deep learning (DL) and machine learning (ML) approach to automatically detect hip prosthetic failure from conventional plain radiographs.

METHODS

Two cohorts of patients (of 280 and 352 patients) were included in the study, for model development and validation, respectively. The analysis was based on one antero-posterior and one lateral radiographic view obtained from each patient during routine post-surgery follow-up. After pre-processing, three images were obtained: the original image, the acetabulum image and the stem image. These images were analyzed through convolutional neural networks aiming to predict prosthesis failure. Deep features of the three images were extracted for each model and two feature-based pipelines were developed: one utilizing only the features of the original image (original image pipeline) and the other concatenating the features of the three images (3-image pipeline). The obtained features were either used directly or reduced through principal component analysis. Both support vector machine (SVM) and random forest (RF) classifiers were considered for each pipeline.

RESULTS

The SVM applied to the 3-image pipeline provided the best performance, with an accuracy of 0.958 ± 0.006 in the internal validation and an F1-score of 0.874 in the external validation set. The explainability analysis, besides identifying the features of the complete original images as the major contributor, highlighted the role of the acetabulum and stem images on the prediction.

CONCLUSIONS

This study demonstrated the potentialities of the developed DL-ML procedure based on plain radiographs in the detection of the failure of the hip prosthesis.

Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis

Background: prosthetic loosening after hip and knee arthroplasty is one of the most common causes of joint arthroplasty failure and revision surgery. Diagnosis of prosthetic loosening is a difficult problem and, in many cases, loosening is not clearly diagnosed until accurately confirmed during surgery. The purpose of this study is to conduct a systematic review and meta-analysis to demonstrate the analysis and performance of machine learning in diagnosing prosthetic loosening after total hip arthroplasty (THA) and total knee arthroplasty (TKA). Materials and Methods: three comprehensive databases, including MEDLINE, EMBASE, and the Cochrane Library, were searched for studies that evaluated the detection accuracy of loosening around arthroplasty implants using machine learning. Data extraction, risk of bias assessment, and meta-analysis were performed. Results: five studies were included in the meta-analysis. All studies were retrospective studies. In total, data from 2013 patients with 3236 images were assessed; these data involved 2442 cases (75.5%) with THAs and 794 cases (24.5%) with TKAs. The most common and best-performing machine learning algorithm was DenseNet. In one study, a novel stacking approach using a random forest showed similar performance to DenseNet. The pooled sensitivity across studies was 0.92 (95% CI 0.84-0.97), the pooled specificity was 0.95 (95% CI 0.93-0.96), and the pooled diagnostic odds ratio was 194.09 (95% CI 61.60-611.57). The I2 statistics for sensitivity and specificity were 96% and 62%, respectively, showing that there was significant heterogeneity. The summary receiver operating characteristics curve indicated the sensitivity and specificity, as did the prediction regions, with an AUC of 0.9853. Conclusions: the performance of machine learning using plain radiography showed promising results with good accuracy, sensitivity, and specificity in the detection of loosening around THAs and TKAs. Machine learning can be incorporated into prosthetic loosening screening programs.