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Background: The effectiveness of practical surgical training is characterised by an inherent learning curve. Decisive are individual initial starting capabilities, learning speed, ideal learning plateaus, and resulting learning potentials. The quantification of learning curves requires reproducible tasks with varied levels of difficulty. The hypothesis of this study is that the use of three-dimensional (3D) vision is more advantageous than two-dimensional vision (2D) for the learning curve in laparoscopic training.
Methods: Forty laparoscopy novices were recruited and randomised to a 2D Group and a 3D Group. A laparoscopy box trainer with two standardised tasks was used for training of surgical tasks. Task 1 was a positioning task, while Task 2 called for laparoscopic knotting as a more complex process. Each task was repeated at least ten times. Performance time and the number of predefined errors were recorded. 2D performance after 3D training was assessed in an additional final 2D cycle undertaken by the 3D Group.
Results: The calculated learning plateaus of both performance times and errors were lower for 3D. Independent of the vision mode the learning curves were smoother (exponential decay) and efficiency was learned faster than precision. The learning potentials varied widely depending on the corresponding initial values and learning plateaus. The final 2D performance time of the 3D-trained group was not significantly better than that of the 2D Group. The final 2D error numbers were similar for all groups.
Conclusions: Stereoscopic vision can speed up laparoscopic training. The 3D learning curves resulted in better precision and efficiency. The 3D-trained group did not show inferior performance in the final 2D cycle. Consequently, we encourage the training of surgical competences like suturing and knotting under 3D vision, even if it is not available in clinical routine.
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http://dx.doi.org/10.1007/s00464-020-07768-1 | DOI Listing |
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.
PLoS One
September 2025
Neck-Shoulder and Lumbocrural Pain Hospital of Shandong First Medical University, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.
Background: Metabolic syndrome (MetS) and sarcopenia are major global public health problems, and their coexistence significantly increases the risk of death. In recent years, this trend has become increasingly prominent in younger populations, posing a major public health challenge. Numerous studies have regarded reduced muscle mass as a reliable indicator for identifying pre-sarcopenia.
View Article and Find Full Text PDFJMIR Med Inform
September 2025
Department of Radiology, Air Force Medical Center, Air Force Medical University, Fucheng Road 30, Haidian District, Beijing, CN.
Background: Lateral malleolar avulsion fracture (LMAF) and subfibular ossicle (SFO) are distinct entities that both present as small bone fragments near the lateral malleolus on imaging, yet require different treatment strategies. Clinical and radiological differentiation is challenging, which can impede timely and precise management. On imaging, magnetic resonance imaging (MRI) is the diagnostic gold standard for differentiating LMAF from SFO, whereas radiological differentiation on computed tomography (CT) alone is challenging in routine practice.
View Article and Find Full Text PDFInt J Cardiovasc Imaging
September 2025
Klinikum Fürth, Friedrich-Alexander-University Erlangen- Nürnberg, Fürth, Germany.
Myocarditis is an inflammation of heart tissue. Cardiovascular magnetic resonance imaging (CMR) has emerged as an important non-invasive imaging tool for diagnosing myocarditis, however, interpretation remains a challenge for novice physicians. Advancements in machine learning (ML) models have further improved diagnostic accuracy, demonstrating good performance.
View Article and Find Full Text PDFJ Neurooncol
September 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
Rationale And Objectives: Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis.
Materials And Methods: Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected.