Background: A learning curve is graphical representation of the relationship between effort, such as repetitive practice or time spent, and the resultant learning based on specific outcomes. Group learning curves provide information for designing educational interventions or assessments. Little is known regarding the learning curves for Point-of-Care Ultrasound (POCUS) psychomotor skill acquisition of novice learners. As POCUS inclusion in education increases, a more thorough understanding of this topic is needed to allow educators to make informed decisions regarding curriculum design. The purpose of this research study is to: (A) define the psychomotor skill acquisition learning curves of novice Physician Assistant students, and (B) analyze the learning curves for the individual image quality components of depth, gain and tomographic axis.
Results: A total of 2695 examinations were completed and reviewed. On group-level learning curves, plateau points were noted to be similar for abdominal, lung, and renal systems around 17 examinations. Bladder scores were consistently good across all exam components from the start of the curriculum. For cardiac exams, students improved even after 25 exams. Learning curves for tomographic axis (angle of intersection of the ultrasound with the structure of interest) were longer than those for depth and gain. Learning curves for axis were longer than those for depth and gain.
Conclusion: Bladder POCUS skills can be rapidly acquired and have the shortest learning curve. Abdominal aorta, kidney, and lung POCUS have similar learning curves, while cardiac POCUS has the longest learning curve. Analysis of learning curves for depth, axis, and gain demonstrates that axis has the longest learner curve of the three components of image quality. This finding has previously not been reported and provides a more nuanced understanding of psychomotor skill learning for novices. Learners might benefit from educators paying particular attention to optimizing the unique tomographic axis for each organ system.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319692 | PMC |
http://dx.doi.org/10.1186/s13089-023-00329-2 | DOI Listing |
Biomed Phys Eng Express
January 2025
Radiation Oncology, Emory University, Emory Midtown Hospital, Atlanta, Georgia, 30322, UNITED STATES.
Although radiotherapy techniques are the primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity, and side effect. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on features extracted from pre-treatment imaging data. This study aims to compare different models in predicting radiation-induced xerostomia and sticky saliva in both early and late stage of HNC patients using CT and MRI image features along with demographics and dosimetric information.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Science, Faculty of Computing, Federal University of Lafia, Lafia, Nasarawa State, Nigeria.
Med Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
View Article and Find Full Text PDFNeurooncol Adv
January 2025
Institute for Artificial Intelligence in Medicine, University Hospital Essen, Germany.
Background: This study aimed to develop an automated algorithm to noninvasively distinguish gliomas from other intracranial pathologies, preventing misdiagnosis and ensuring accurate analysis before further glioma assessment.
Methods: A cohort of 1280 patients with a variety of intracranial pathologies was included. It comprised 218 gliomas (mean age 54.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!