Artificial Intelligence: Influencing Our Lives in Joint Arthroplasty

A multiview deep learning-based prediction pipeline augmented with confident learning can improve performance in determining knee arthroplasty candidates

PURPOSE

Preoperative prudent patient selection plays a crucial role in knee osteoarthritis management but faces challenges in appropriate referrals such as total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA) and nonoperative intervention. Deep learning (DL) techniques can build prediction models for treatment decision-making. The aim is to develop and evaluate a knee arthroplasty prediction pipeline using three-view X-rays to determine the suitable candidates for TKA, UKA or are not arthroplasty candidates.

METHODS

A study was conducted using three-view (anterior-posterior, lateral and patellar) X-rays and surgical data of patients undergoing TKA, UKA or nonarthroplasty interventions from sites A and B. Data from site A were used to derive and validate models. Data from site B were used as external test set. A DL pipeline combining YOLOv3 and ResNet-18 with confident learning (CL) was developed. Multiview Convolutional Neural Network, EfficientNet-b4, ResNet-101 and the proposed model without CL were also trained and tested. The models were evaluated using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, precision, specificity, sensitivity and F1 score.

RESULTS

The data set comprised a total of 1779 knees. Of which 1645 knees were from site A as a derivation set and an internal validation cohort. The external validation cohort consisted of 134 knees. The internal validation cohort demonstrated superior performance for the proposed model augmented with CL, achieving an AUC of 0.94 and an accuracy of 85.9%. External validation further confirmed the model's generalisation, with an AUC of 0.93 and an accuracy of 82.1%. Comparative analysis with other neural network models showed the proposed model's superiority.

CONCLUSIONS

The proposed DL pipeline, integrating YOLOv3, ResNet-18 and CL, provides accurate predictions for knee arthroplasty candidates based on three-view X-rays. This prediction model could be useful in performing decision making for the type of arthroplasty procedure in an automated fashion.

LEVEL OF EVIDENCE

Level III, diagnostic study.

Using Google web search to analyze and evaluate the application of ChatGPT in femoroacetabular impingement syndrome

Background

Chat Generative Pre-trained Transformer (ChatGPT) is a new machine learning tool that allows patients to access health information online, specifically compared to Google, the most commonly used search engine in the United States. Patients can use ChatGPT to better understand medical issues. This study compared the two search engines based on: (i) frequently asked questions (FAQs) about Femoroacetabular Impingement Syndrome (FAI), (ii) the corresponding answers to these FAQs, and (iii) the most FAQs yielding a numerical response.

Purpose

To assess the suitability of ChatGPT as an online health information resource for patients by replicating their internet searches.

Study design

Cross-sectional study.

Methods

The same keywords were used to search the 10 most common questions about FAI on both Google and ChatGPT. The responses from both search engines were recorded and analyzed.

Results

Of the 20 questions, 8 (40%) were similar. Among the 10 questions searched on Google, 7 were provided by a medical practice. For numerical questions, there was a notable difference in answers between Google and ChatGPT for 3 out of the top 5 most common questions (60%). Expert evaluation indicated that 67.5% of experts were satisfied or highly satisfied with the accuracy of ChatGPT's descriptions of both conservative and surgical treatment options for FAI. Additionally, 62.5% of experts were satisfied or highly satisfied with the safety of the information provided. Regarding the etiology of FAI, including cam and pincer impingements, 52.5% of experts expressed satisfaction or high satisfaction with ChatGPT's explanations. Overall, 62.5% of experts affirmed that ChatGPT could serve effectively as a reliable medical resource for initial information retrieval.

Conclusion

This study confirms that ChatGPT, despite being a new tool, shows significant potential as a supplementary resource for health information on FAI. Expert evaluations commend its capacity to provide accurate and comprehensive responses, valued by medical professionals for relevance and safety. Nonetheless, continuous improvements in its medical content's depth and precision are recommended for ongoing reliability. While ChatGPT offers a promising alternative to traditional search engines, meticulous validation is imperative before it can be fully embraced as a trusted medical resource.

The Viability of an Artificial Intelligence/Machine Learning Prediction Model to Determine Candidates for Knee Arthroplasty

BACKGROUND

The purpose of this study is to assess the viability of a knee arthroplasty prediction model using 3-view X-rays that helps determine if patients with knee pain are candidates for total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), or are not arthroplasty candidates.

METHODS

Analysis was performed using radiographic and surgical data from a high-volume joint replacement practice. The dataset included 3 different X-ray views (anterior-posterior, lateral, and sunrise) for 2,767 patients along with information of whether that patient underwent an arthroplasty surgery (UKA or TKA) or not. This resulted in a dataset including 8,301 images from 2,707 patients. This dataset was then split into a training set (70%) and holdout test set (30%). A computer vision model was trained using a transfer learning approach. The performance of the computer vision model was evaluated on the holdout test set. Accuracy and multiclass receiver operating characteristic area under curve was used to evaluate the performance of the model.

RESULTS

The artificial intelligence model achieved an accuracy of 87.8% on the holdout test set and a quadratic Cohen's kappa score of 0.811. The multiclass receiver operating characteristic area under curve score for TKA was calculated to be 0.97; for UKA a score of 0.96 and for No Surgery a score of 0.98 was achieved. An accuracy of 93.8% was achieved for predicting Surgery versus No Surgery and 88% for TKA versus not TKA was achieved.

CONCLUSION

The artificial intelligence/machine learning model demonstrated viability for predicting which patients are candidates for a UKA, TKA, or no surgical intervention.

Using a Google Web Search Analysis to Assess the Utility of ChatGPT in Total Joint Arthroplasty

BACKGROUND

Rapid technological advancements have laid the foundations for the use of artificial intelligence in medicine. The promise of machine learning (ML) lies in its potential ability to improve treatment decision making, predict adverse outcomes, and streamline the management of perioperative healthcare. In an increasing consumer-focused health care model, unprecedented access to information may extend to patients using ChatGPT to gain insight into medical questions. The main objective of our study was to replicate a patient's internet search in order to assess the appropriateness of ChatGPT, a novel machine learning tool released in 2022 that provides dialogue responses to queries, in comparison to Google Web Search, the most widely used search engine in the United States today, as a resource for patients for online health information. For the 2 different search engines, we compared i) the most frequently asked questions (FAQs) associated with total knee arthroplasty (TKA) and total hip arthroplasty (THA) by question type and topic; ii) the answers to the most frequently asked questions; as well as iii) the FAQs yielding a numerical response.

METHODS

A Google web search was performed with the following search terms: "total knee replacement" and "total hip replacement." These terms were individually entered and the first 10 FAQs were extracted along with the source of the associated website for each question. The following statements were inputted into ChatGPT: 1) "Perform a google search with the search term 'total knee replacement' and record the 10 most FAQs related to the search term" as well as 2) "Perform a google search with the search term 'total hip replacement' and record the 10 most FAQs related to the search term." A Google web search was repeated with the same search terms to identify the first 10 FAQs that included a numerical response for both "total knee replacement" and "total hip replacement." These questions were then inputted into ChatGPT and the questions and answers were recorded.

RESULTS

There were 5 of 20 (25%) questions that were similar when performing a Google web search and a search of ChatGPT for all search terms. Of the 20 questions asked for the Google Web Search, 13 of 20 were provided by commercial websites. For ChatGPT, 15 of 20 (75%) questions were answered by government websites, with the most frequent one being PubMed. In terms of numerical questions, 11 of 20 (55%) of the most FAQs provided different responses between a Google web search and ChatGPT.

CONCLUSION

A comparison of the FAQs by a Google web search with attempted replication by ChatGPT revealed heterogenous questions and responses for open and discrete questions. ChatGPT should remain a trending use as a potential resource to patients that needs further corroboration until its ability to provide credible information is verified and concordant with the goals of the physician and the patient alike.

Applications of artificial intelligence in nuclear medicine image generation

Recently, the application of artificial intelligence (AI) in medical imaging (including nuclear medicine imaging) has rapidly developed. Most AI applications in nuclear medicine imaging have focused on the diagnosis, treatment monitoring, and correlation analyses with pathology or specific gene mutation. It can also be used for image generation to shorten the time of image acquisition, reduce the dose of injected tracer, and enhance image quality. This work provides an overview of the application of AI in image generation for single-photon emission computed tomography (SPECT) and positron emission tomography (PET) either without or with anatomical information [CT or magnetic resonance imaging (MRI)]. This review focused on four aspects, including imaging physics, image reconstruction, image postprocessing, and internal dosimetry. AI application in generating attenuation map, estimating scatter events, boosting image quality, and predicting internal dose map is summarized and discussed.

Mechanical behavior of a titanium alloy scaffold mimicking trabecular structure

Background

Additively manufactured porous metallic structures have recently received great attention for bone implant applications. The morphological characteristics and mechanical behavior of 3D printed titanium alloy trabecular structure will affect the effects of artificial prosthesis replacement. However, the mechanical behavior of titanium alloy trabecular structure at present clinical usage still is lack of in-depth study from design to manufacture as well as from structure to mechanical function.

Methods

A unit cell of titanium alloy was designed to mimick trabecular structure. The controlled microarchitecture refers to a repeating array of unit-cells, composed of titanium alloy, which make up the scaffold structure. Five kinds of unit cell mimicking trabecular structure with different pore sizes and porosity were obtained by modifying the strut sizes of the cell and scaling the cell as a whole. The titanium alloy trabecular structure was fabricated by 3D printing based on Electron Beam Melting (EBM). The paper characterized the difference between the designs and fabrication of trabecular structures, as well as mechanical properties and the progressive collapse behavior and failure mechanism of the scaffold.

Results

The actual porosities of the EBM-produced bone trabeculae are lower than the designed, and the load capacity of a bearing is related to the porosity of the structure. The larger the porosity of the structure, the smaller the stiffness and the worse the load capacity is. The fracture interface of the trabecular structure under compression is at an angle of 45^o with respect to the compressive axis direction, which conforms to Tresca yield criterion. The trabeculae-mimicked unit cell is anisotropy. Under quasi-static loading, loading speed has no effect on mechanical performance of bone trabecular specimens. There is no difference of the mechanical performance at various orientations and sites in metallic workspace. The elastic modulus of the scaffold decreases by 96%–93% and strength reduction 96%–91%, compared with titanium alloy dense metals structure. The apparent elastic modulus of the unit-cell-repeated scaffold is 0.39–0.618 GPa, which is close to that of natural bone and stress shielding can be reduced.

Conclusion

We have systematically studied the structural design, fabrication and mechanical behavior of a 3D printed titanium alloy scaffold mimicking trabecula bone. This study will be benefit of the application of prostheses with proper structures and functions.