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Background: Minimally invasive cardiac surgery (MICS) has become a popular approach due to its potential benefits such as improved cosmesis, faster recovery, shorter hospital stays, and cost effectiveness compared to traditional median sternotomy. However, there have been some concerns regarding procedural efficiency and surgical outcomes especially in the early phase of the learning curve of these procedures.
Methods: In March 2025, a systematic review was conducted using MEDLINE, Embase, the Cochrane Library and Google Scholar databases to identify potential studies that quantitively assessed the learning curve in MICS using pre-defined metrics based on surgical times and/or clinical outcomes.
Results: Twenty-eight studies involving 13,257 patients met the inclusion criteria, most of which were retrospective, focusing on three types of MICS: mitral valve surgery (MIMVS), aortic valve replacement (MIAVR), and coronary artery bypass grafting (MICABG). The learning curve was assessed using arbitrary (split-group) and non-arbitrary (cumulative sum) methods. Common perioperative metrics included operative, cardiopulmonary bypass, aortic cross-clamp times, and postoperative complications. The reported number of cases needed to overcome the learning curve varied widely, ranging from 23 to 125 (mean: 39 [for repair] and 78 [for replacement]) for MIMVS, 40 to 138 (mean: 93) for MIAVR, and 16 to 100 (mean: 40) for MICABG.
Conclusions: Differences in surgical process and postoperative outcomes suggest a learning curve in MICS, though stable morbidity and mortality rates indicate the safe adoption of these procedures with appropriate training. Nonetheless, significant heterogeneity across studies prevents precise learning curve characterization, highlighting the need for standardized, multi-variable assessment frameworks.
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http://dx.doi.org/10.1016/j.athoracsur.2025.08.013 | DOI Listing |
BMC Oral Health
September 2025
Oral and Maxillofacial Radiology Department, Cairo university, Cairo, Egypt.
Aim: The purpose of this study was to assess the accuracy of a customized deep learning model based on CNN and U-Net for detecting and segmenting the second mesiobuccal canal (MB2) of maxillary first molar teeth on cone beam computed tomography (CBCT) scans.
Methodology: CBCT scans of 37 patients were imported into 3D slicer software to crop and segment the canals of the mesiobuccal (MB) root of the maxillary first molar. The annotated data were divided into two groups: 80% for training and validation and 20% for testing.
J Robot Surg
September 2025
Department of General Surgery, Giglio Hospital Foundation, Cefalu', Italy.
The adoption of robotic pancreatectomy has grown significantly in recent years, driven by its potential advantages in precision, minimally invasive access, and improved patient recovery. However, mastering these complex procedures requires overcoming a substantial learning curve, and the role of structured mentoring in facilitating this transition remains underexplored. This systematic review and meta-analysis aimed to comprehensively evaluate the number of cases required to achieve surgical proficiency, assess the impact of mentoring on skill acquisition, and analyze how outcomes evolve throughout the learning process.
View Article and Find Full Text PDFMed Eng Phys
October 2025
Biomedical Device Technology, Istanbul Aydın University, Istanbul, 34093, Istanbul, Turkey. Electronic address:
Deep learning approaches have improved disease diagnosis efficiency. However, AI-based decision systems lack sufficient transparency and interpretability. This study aims to enhance the explainability and training performance of deep learning models using explainable artificial intelligence (XAI) techniques for brain tumor detection.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
September 2025
From the Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America (J.S.S., B.M., S.H., A.H., J.S.), and Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India (H.S.).
Background And Purpose: The choroid of the eye is a rare site for metastatic tumor spread, and as small lesions on the periphery of brain MRI studies, these choroidal metastases are often missed. To improve their detection, we aimed to use artificial intelligence to distinguish between brain MRI scans containing normal orbits and choroidal metastases.
Materials And Methods: We present a novel hierarchical deep learning framework for sequential cropping and classification on brain MRI images to detect choroidal metastases.
Am J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.