Objectives: The objectives of this study were: 1) to develop and implement a set of automated performance metrics into the Western myringotomy simulator, and 2) to establish construct validity.
Study Design: Prospective simulator-based assessment study.
Setting: The Auditory Biophysics Laboratory at Western University, London, Ontario, Canada.
Participants: Eleven participants were recruited from the Department of Otolaryngology-Head & Neck Surgery at Western University: four senior otolaryngology consultants and seven junior otolaryngology residents.
Interventions: Educational simulation.
Main Outcome Measure: Discrimination between expert and novice participants on five primary automated performance metrics: 1) time to completion, 2) surgical errors, 3) incision angle, 4) incision length, and 5) the magnification of the microscope.
Methods: Automated performance metrics were developed, programmed, and implemented into the simulator. Participants were given a standardized simulator orientation and instructions on myringotomy and tube placement. Each participant then performed 10 procedures and automated metrics were collected. The metrics were analyzed using the Mann-Whitney U test with Bonferroni correction.
Results: All metrics discriminated senior otolaryngologists from junior residents with a significance of p < 0.002. Junior residents had 2.8 times more errors compared with the senior otolaryngologists. Senior otolaryngologists took significantly less time to completion compared with junior residents. The senior group also had significantly longer incision lengths, more accurate incision angles, and lower magnification keeping both the umbo and annulus in view.
Conclusions: Automated quantitative performance metrics were successfully developed and implemented, and construct validity was established by discriminating between expert and novice participants.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/MAO.0000000000001867 | DOI Listing |
Invest Radiol
January 2025
From the Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany (A. Schwarz, A. Simon, A.M.); Siemens Healthineers AG, Forchheim, Germany (A. Schwarz, C.H., J.D., A. Simon); Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany (F.K.W., S.G., M.S.); and Institut for Radiology, Pediatric and Neuroradiology, Helios Hospital, Schwerin, Germany (H.-J.R.).
Objective: Respiratory motion can affect image quality and thus affect the diagnostic accuracy of CT images by masking or mimicking relevant lung pathologies. CT examinations are often performed during deep inspiration and breath-hold to achieve optimal image quality. However, this can be challenging for certain patient groups, such as children, the elderly, or sedated patients.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Anatomy and Cell Biology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, Japan.
Mathematical modeling has been utilized to explain biological pattern formation, but the selections of models and parameters have been made empirically. In the present study, we propose a data-driven approach to validate the applicability of mathematical models. Specifically, we developed methods to automatically select the appropriate mathematical models based on the patterns of interest and to estimate the model parameters.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Studying the changes in cellular transcriptional profiles induced by small molecules can significantly advance our understanding of cellular state alterations and response mechanisms under chemical perturbations, which plays a crucial role in drug discovery and screening processes. Considering that experimental measurements need substantial time and cost, we developed a deep learning-based method called Molecule-induced Transcriptional Change Predictor (MiTCP) to predict changes in transcriptional profiles (CTPs) of 978 landmark genes induced by molecules. MiTCP utilizes graph neural network-based approaches to simultaneously model molecular structure representation and gene co-expression relationships, and integrates them for CTP prediction.
View Article and Find Full Text PDFEndocr Pathol
January 2025
Department of Computer Engineering, Koc University, Istanbul, Turkey.
Pancreatic neuroendocrine tumors (PanNETs) are a heterogeneous group of neoplasms that include tumors with different histomorphologic characteristics that can be correlated to sub-categories with different prognoses. In addition to the WHO grading scheme based on tumor proliferative activity, a new parameter based on the scoring of infiltration patterns at the interface of tumor and non-neoplastic parenchyma (tumor-NNP interface) has recently been proposed for PanNET categorization. Despite the known correlations, these categorizations can still be problematic due to the need for human judgment, which may involve intra- and inter-observer variability.
View Article and Find Full Text PDFHernia
January 2025
Department of Surgery, Corewell Health East William Beaumont University Hospital, 3601 W 13 Mile Road, Royal Oak, MI, 48073, USA.
Purpose: Traumatic abdominal intercostal/flank hernias present a perplexing challenge for surgeons seeking to repair them. There has been a paucity of studies describing robotic repairs of such hernias. We aim to evaluate the effectiveness of the Robotic-assisted Extended Total Extraperitoneal/Transversus Abdominus Release (rETEP/TAR) method in repairing traumatic abdominal intercostal and flank hernias.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!