Background: The purpose of this study was to find a utility of a newly developed 3D-printed sinus model and to evaluate the educational benefit of simulation training with the models for functional endoscopic sinus surgery (FESS).
Material And Methods: Forty-seven otolaryngologists were categorized as experts (board-certified physicians with ≥200 experiences of FESS, = 9), intermediates (board-certified physicians with <200 experiences of FESS, = 19), and novices (registrars, = 19). They performed FESS simulation training on 3D-printed models manufactured from DICOM images of computed tomography (CT) scan of real patients. Their surgical performance was assessed with the objective structured assessment of technical skills (OSATS) score and dissection quality evaluated radiologically with a postdissection CT scan. First we evaluated the face, content, and constructive values. Second we evaluated the educational benefit of the training. Ten novices underwent training (training group) and their outcomes were compared to the remaining novices without training (control group). The training group performed cadaveric FESS surgeries before and after the repetitive training.
Results: The feedback from experts revealed high face and content value of the 3D-printed models. Experts, intermediates, and novices demonstrated statistical differences in their OSATS scores (74.7 ± 3.6, 58.3 ± 10.1, and 43.1 ± 11.1, respectively, < .001), and dissection quality (81.1 ± 13.1, 93.7 ± 15.1, and 126.4 ± 25.2, respectively, < .001). The training group improved their OSATS score (41.1 ± 8.0 to 61.1 ± 6.9, < .001) and dissection quality (122.1 ± 22.2 to 90.9 ± 10.3, = .013), while the control group not. After training, 80% of novices with no prior FESS experiences completed surgeries on cadaver sinuses.
Conclusion: Repeated training using the models revealed an initial learning curve in novices, which was confirmed in cadaveric mock FESS surgeries.
Level Of Evidence: N/A.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9392405 | PMC |
| http://dx.doi.org/10.1002/lio2.873 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Design and Validation of a High-Fidelity Left Atrial Cardiac Simulator for the Study and Advancement of Left Atrial Appendage Occlusion.
Cardiovasc Eng Technol
January 2025
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, MA, Cambridge, USA.
Purpose: Atrial fibrillation (AF) is the most common chronic cardiac arrhythmia that increases the risk of stroke, primarily due to thrombus formation in the left atrial appendage (LAA). Left atrial appendage occlusion (LAAO) devices offer an alternative to oral anticoagulation for stroke prevention. However, the complex and variable anatomy of the LAA presents significant challenges to device design and deployment.
View Article and Find Full Text PDFPrediction of Radiation Therapy Induced Cardiovascular Toxicity from Pretreatment CT Images in Patients with Thoracic Malignancy via an Optimal Biomarker Approach.
Acad Radiol
January 2025
Medical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 (M.L., M.A., J.K.U., Y.T., C.W., N.P., S.M., D.A.T.). Electronic address:
Rationale And Objectives: Cardiovascular toxicity is a well-known complication of thoracic radiation therapy (RT), leading to increased morbidity and mortality, but existing techniques to predict cardiovascular toxicity have limitations. Predictive biomarkers of cardiovascular toxicity may help to maximize patient outcomes.
Methods: The machine learning optimal biomarker (OBM) method was employed to predict development of cardiotoxicity (based on serial echocardiographic measurements of left ventricular ejection fraction and longitudinal strain) from computed tomography (CT) images in patients with thoracic malignancy undergoing RT.
Not Like They Used To: The Decline of Procedural Competency in Medical Training.
Ann Fam Med
January 2025
Department of General Pediatrics, Boston Children's Hospital, Boston, MassachusettsHarvard Medical School, Boston, MassachusettsLongwood Pediatrics, Boston, Massachusetts
As a primary care pediatrician trained before work hour restrictions were enacted, I spent hours mastering procedures that trainees today rarely perform. The changing landscape of health care clinician roles, technology, and work hour restrictions have all contributed to a remarkable decline in trainees' procedural competence which has significant negative effects for patients, health care systems, and physicians themselves. I suggest simulation, live training, mentoring, and scheduled opportunities as ways to reemphasize the importance of learning these technical skills.
View Article and Find Full Text PDFMass Casualty Incident Training in Immersive Virtual Reality: Quasi-Experimental Evaluation of Multimethod Performance Indicators.
J Med Internet Res
January 2025
Psychological Institute and Network Aging Research, Heidelberg University, Heidelberg, Germany.
Background: Immersive virtual reality (iVR) has emerged as a training method to prepare medical first responders (MFRs) for mass casualty incidents (MCIs) and disasters in a resource-efficient, flexible, and safe manner. However, systematic evaluations and validations of potential performance indicators for virtual MCI training are still lacking.
Objective: This study aimed to investigate whether different performance indicators based on visual attention, triage performance, and information transmission can be effectively extended to MCI training in iVR by testing if they can discriminate between different levels of expertise.
A safe-enhanced fully closed-loop artificial pancreas controller based on deep reinforcement learning.
PLoS One
January 2025
Institute of Visual Informatics, The National University of Malaysia (UKM), Bangi, Malaysia.
Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!