Intraoperative integration of structured light scanning for automatic tissue classification: a feasibility study

Artificial intelligence in total and unicompartmental knee arthroplasty

The application of Artificial intelligence (AI) and machine learning (ML) tools in total (TKA) and unicompartmental knee arthroplasty (UKA) emerges with the potential to improve patient-centered decision-making and outcome prediction in orthopedics, as ML algorithms can generate patient-specific risk models. This review aims to evaluate the potential of the application of AI/ML models in the prediction of TKA outcomes and the identification of populations at risk.An extensive search in the following databases: MEDLINE, Scopus, Cinahl, Google Scholar, and EMBASE was conducted using the PIOS approach to formulate the research question. The PRISMA guideline was used for reporting the evidence of the data extracted. A modified eight-item MINORS checklist was employed for the quality assessment. The databases were screened from the inception to June 2022.Forty-four out of the 542 initially selected articles were eligible for the data analysis; 5 further articles were identified and added to the review from the PUBMED database, for a total of 49 articles included. A total of 2,595,780 patients were identified, with an overall average age of the patients of 70.2 years ± 7.9 years old. The five most common AI/ML models identified in the selected articles were: RF, in 38.77% of studies; GBM, in 36.73% of studies; ANN in 34.7% of articles; LR, in 32.65%; SVM in 26.53% of articles.This systematic review evaluated the possible uses of AI/ML models in TKA, highlighting their potential to lead to more accurate predictions, less time-consuming data processing, and improved decision-making, all while minimizing user input bias to provide risk-based patient-specific care.

Digital healthcare technologies: Modern tools to transform prosthetic care

INTRODUCTION

Digital healthcare technologies are transforming the face of prosthetic care. Millions of people with limb loss around the world do not have access to any form of rehabilitative healthcare. However, digital technologies provide a promising solution to augment the range and efficiency of prosthetists.

AREAS COVERED

The goal of this review is to introduce the digital technologies that have the potential to change clinical methods in prosthetic healthcare. Our target audience are researchers who are unfamiliar with the field of prostheses in general, especially with the newest technological developments. This review addresses technologies for: scanning of amputated limbs, limb-to-socket rectification, additive manufacturing of prosthetic sockets, and quantifying patient response to wearing sockets. This review does not address biomechatronic prostheses or biomechanical design practices.

EXPERT OPINION

Digital technologies will enable affordable prostheses to be built on a scale larger than with today's clinical practices. Large technological gaps need to be overcome to enable the mass production and distribution of prostheses digitally. However, recent advances in computational methods and CAD/CAM technologies are bridging this gap faster than ever before. We foresee that these technologies will return mobility and economic opportunity to amputees on a global scale in the near future.

Better precision of a new robotically assisted system for total knee arthroplasty compared to conventional techniques: A sawbone model study

BACKGROUND

The purpose of this study was to compare the accuracy of this new HURWA robotic-assisted total knee arthroplasty (TKA) technique to the accuracy of the conventional technique in a sawbone model.

METHODS

The HURWA robotic-assisted TKA system was applied in the robotic group. After bone resection, all of these sawbones were scanned by the use of a structured light scanning system. Measurements of bone resections, femoral coronal and sagittal measurements, and tibial coronal and sagittal measurements were recorded.

RESULTS

Compared to the conventional technique, the HURWA robotic-assisted system significantly improved the accuracy of the bone resection levels and angles. In the robotic group, the accuracy of all of the bone resection levels was below 0.6 mm (with standard deviation [SD] below 0.6 mm), and all of the bone resection angles were below 0.6° (with SD below 0.4°).

CONCLUSION

Our data suggest that this novel HURWA robotic-assisted system can significantly improve the accuracy of bone resection levels and angles.