Background: Two-dimensional (2D) view is known to cause practical difficulties for surgeons in conventional laparoscopy. Our goal was to evaluate whether the new-generation, Three-Dimensional Laparoscopic Vision System (3D LVS) provides greater benefit in terms of execution time and error number during the performance of surgical tasks.
Methods: This study tests the hypothesis that the use of the new generation 3D LVS can significantly improve technical ability on complex laparoscopic tasks in an experimental model. Twenty-four participants (8 experienced, 8 minimally experienced, and 8 inexperienced) were evaluated for 10 different tasks in terms of total execution time and error number. The 4-point lickert scale was used for subjective assessment of the two imaging modalities.
Results: All tasks were completed by all participants. Statistically significant difference was determined between 3D and 2D systems in the tasks of bead transfer and drop, suturing, and pick-and-place in the inexperienced group; in the task of passing through two circles with the needle in the minimally experienced group; and in the tasks of bead transfer and drop, suturing and passing through two circles with the needle in the experienced group. Three-dimensional imaging was preferred over 2D in 6 of the 10 subjective criteria questions on 4-point lickert scale.
Conclusions: The majority of the tasks were completed in a shorter time using 3D LVS compared to 2D LVS. The subjective Likert-scale ratings from each group also demonstrated a clear preference for 3D LVS. New 3D LVS has the potential to improve the learning curve, and reduce the operating time and error rate during the performances of laparoscopic surgeons. Our results suggest that the new-generation 3D HD LVS will be helpful for surgeons in laparoscopy (Clinical Trial ID: NCT01799577, Protocol ID: BEHGynobs-4).
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s00464-014-3949-0 | DOI Listing |
Rev Sci Instrum
January 2025
Bennu Climate, Inc. and Symbolic Systems Program, Stanford University, Stanford, California 94305, USA.
The Linac Coherent Light Source (LCLS) is the world's first x-ray free electron laser. It is a scientific user facility operated by the SLAC National Accelerator Laboratory, at Stanford, for the U.S.
View Article and Find Full Text PDFThe primary aim of this descriptive cross-sectional study was to examine the relationship between ocular motility and motor skills in school-age children. Participants included 142 schoolchildren (mean age: 7.08 ± 0.
View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFFront Neurosci
January 2025
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.
Motor evoked potentials (MEPs) are an important measure in transcranial magnetic stimulation (TMS) when assessing neuronal excitability in clinical diagnostics related to motor function, as well as in neuroscience research. However, manual feature extraction from large datasets can be time-consuming and prone to human error, and valuable features, such as MEP polyphasia and duration, are often neglected. Several packages have been developed to simplify the process; however, they are often tailored to specific studies or are not accessible.
View Article and Find Full Text PDFMethodsX
June 2025
Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, Maharashtra, India.
Integrated Circuits are made of various transistors that are embedded on a silicon wafer, these wafers are difficult to process and hence are prone to defects. Defecting these defects manually is a time consuming and labour-intensive task and hence automation is necessary. Deep Learning approach is better suited in this case as it is able to generalize defects if trained properly and can be a solution to segmentation and classification of defects automatically.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!