98%
921
2 minutes
20
Purpose: Robotic-assisted total knee arthroplasty (RA-TKA), which is increasingly used to improve surgical precision, can face adoption difficulties due to a learning curve marked by longer operating times. The aim of this study was to evaluate the learning curve associated with the VELYS™ robot in five surgeons from the same centre with different annual arthroplasty volumes using navigated assistance with personalised alignment. The primary aim was to assess the learning curve for each surgeon. Secondary aims were to identify the factors associated with extended operative times.
Methods: In this retrospective comparative study, 367 patients who underwent primary TKA between January and December 2024 were included, comprising 149 with robotic assistance and 218 with navigated assistance. The surgical learning curve, based on skin-to-skin operating time, was assessed using the cumulative summation method. Five surgeons were evaluated: two high-volume surgeons (>150 TKAs per year), a medium-volume surgeon (between 50 and 150) and two low-volume surgeons (<50). Pre- and intra-operative data (age, gender, body mass index, American Society of Anesthesiologists score, pre-operative hip-knee-ankle, range of motion, approach, size and implant constraint and type of assistance) were collected to identify extended operative time factors.
Results: The learning curve was reached after performing between 4 and 11 cases (11 procedures for surgeon no. 1, 4 for surgeon no. 2, 6 for surgeon no. 3, 4 for surgeon no. 4 and 4 for surgeon no. 5). The robotic operating time was 57.1 min compared to 54.1 min ( = 0.017) with navigation. The increase was statistically significant only for one low-volume surgeon ( = 0.008). Use of the robot ( < 0.001), surgeon ( < 0.001), use of a posterior-stabilised implant ( < 0.001) and varus of more than 10° ( = 0.0191) were independent factors associated with extended operative time.
Conclusion: The learning curve associated with VELYS™ was between 4 and 11 procedures. The small increase in operative time compared to navigation should not be a barrier to its adoption.
Level Of Evidence: Level III, case-control retrospective analysis.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409472 | PMC |
http://dx.doi.org/10.1002/jeo2.70401 | DOI Listing |
Eur J Radiol
September 2025
Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China. Electronic address:
Purpose: The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene promoter methylation in glioma.
Materials And Methods: This retrospective study included 410 patients from the University of California, San Francisco, USA, and 102 patients from our hospital. Seven models were constructed using preoperative contrast-enhanced T1-weighted MRI with gadobenate dimeglumine as the contrast agent.
JMIR Med Inform
September 2025
College of Medical Informatics, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China, 86 13500303273.
Background: Cirrhosis is a leading cause of noncancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improving health care outcomes. However, the quality and integrity of real-world electronic health records (EHRs) limit their utility in developing risk assessment tools.
View Article and Find Full Text PDFJMIR AI
September 2025
Faculty of Medicine, Universidade Federal de Alagoas, Av. Lourival Melo Mota, S/n - Tabuleiro do Martins, Maceió, 57072-900, Brazil, 558232141461.
Background: Artificial intelligence (AI) has the potential to transform global health care, with extensive application in Brazil, particularly for diagnosis and screening.
Objective: This study aimed to conduct a systematic review to understand AI applications in Brazilian health care, especially focusing on the resource-constrained environments.
Methods: A systematic review was performed.
Arq Gastroenterol
September 2025
Faculdade de Medicina da Universidade de São Paulo, Departamento de Gastroenterologia, São Paulo, SP, Brasil.
Background: Accurate evaluation of the invasion depth of superficial esophageal squamous cell carcinoma (SESCC) is crucial for optimal treatment. While magnifying endoscopy (ME) using the Japanese Esophageal Society (JES) classification is reported as the most accurate method to predict invasion depth, its efficacy has not been tested in the Western world. This study aims to evaluate the interobserver agreement of the JES classification for SESCC and its accuracy in estimating invasion depth in a Brazilian tertiary hospital.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Computer Science, Osun State University, Osogbo, Nigeria.
Probabilistic Random Forest is an extension of the traditional Random Forest machine learning algorithm that is one of the frequently used machine learning algorithms employed for species distribution modeling. However, with the use of complex dataset for predicting the presence or absence of the species, It is essential that feature extraction is important to generate optimal prediction that can affect the model accuracy and AUC score of the model simulation. In this paper, we integrated the Genetic Algorithm Optimization technique, which is popular for its excellent feature extraction technique, to enhance the predictive performance of the PRF Model.
View Article and Find Full Text PDF