Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review

Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.

Robotic-assisted versus conventional total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials

OBJECTIVES

Robotic knee arthroplasty procedures have emerged as a new trend, garnering attention from orthopedic surgeons globally. It has been hypothesized that the use of robotics enhances the accuracy of prosthesis positioning and alignment restoration. The objective of this study was to provide a high-level, evidence-based comparison between robotic total knee replacements and conventional methods, focusing on radiological and functional outcomes.

METHODS

We searched five databases from their inception until June 1, 2022, specifically targeting randomized controlled trials (RCTs) that compared the outcomes of robotic and conventional total knee replacements. We were interested in outcomes such as knee range of motion, clinical and function knee society scores, the Western Ontario and McMaster University score (WOMAC), the Hospital of Special Surgery score, complications, and radiological alignment. This review was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes guidelines. We assessed the risk of bias using the revised Cochrane risk-of-bias tool for randomized trials (RoB 2).

RESULTS

Our search returned seven RCTs suitable for our analysis, which included a total of 1942 knees; 974 of these knees were implanted using robotic arms while the remaining 968 utilized jig-based knee systems. Our findings indicated that robotic knees had significantly better post-operative anatomical (OR - 0.82; 95% CI, - 1.027 to - 0.58, p value < 0.00001) and mechanical restoration (OR - 0.95; 95% CI, - 1.49 to - 0.41, p value < 0.0006). While knee range of motion (OR - 2.23; 95% CI - 4.89-0.43, p value 0.1) and femoral prosthesis position (OR - 0.98; 95% CI, - 2.03-0.08, p value 0.07) also favored robotic knees, these differences did not reach statistical significance. Both clinical and functional outcomes, as well as the rate of complications, were found to be statistically similar between the groups undergoing robotic and traditional knee replacement surgeries.

CONCLUSION

This meta-analysis indicates that robotic total knee replacements offer superior post-operative anatomical and mechanical alignment compared to conventional total knee replacements. Despite this, clinical and functional outcomes, as well as complication rates, were similar between the two. These findings should be considered in light of potential confounding factors. More randomized controlled trials with the latest robotic systems are needed to confirm any superior functional and clinical outcomes from robotic-assisted surgeries.

LEVEL OF EVIDENCE

I.