Introduction: Anatomy is the cornerstone of medical education. Different teaching methods can be combined. This study was designed to evaluate the influence of students' drawing of the anatomical region before and after the dissection session on their memorization of the studied anatomical region.
Method: Four hundred and sixteen second-year medical students in the faculty of medicine of Damascus were included in this study during the 2013-2014 academic year. Students were randomly divided into three blinded groups. Two groups had to draw the anatomical region respectively before and after the dissection session, while the third group did not have to draw. The memorization of the region was evaluated twice, one and seven weeks after the course. Means were compared using a t-test.
Results: Scores were significantly higher at 1 and 7 weeks tests in groups who were asked to draw either before or after the dissection compared to those who were not asked to draw. No statistical difference was found between the two groups who drew.
Conclusion: The authors recommend the use of drawing in teaching anatomy.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.morpho.2015.11.001 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Automated spinopelvic measurements on radiographs with artificial intelligence: a multi-reader study.
Radiol Med
January 2025
Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Purpose: To develop an artificial intelligence (AI) algorithm for automated measurements of spinopelvic parameters on lateral radiographs and compare its performance to multiple experienced radiologists and surgeons.
Methods: On lateral full-spine radiographs of 295 consecutive patients, a two-staged region-based convolutional neural network (R-CNN) was trained to detect anatomical landmarks and calculate thoracic kyphosis (TK), lumbar lordosis (LL), sacral slope (SS), and sagittal vertical axis (SVA). Performance was evaluated on 65 radiographs not used for training, which were measured independently by 6 readers (3 radiologists, 3 surgeons), and the median per measurement was set as the reference standard.
Anatomical limits of the parapharyngeal space using a transoral endoscopic approach - Radioanatomical study in cadaver.
Eur Arch Otorhinolaryngol
January 2025
Otorhinolaryngology Department, Hospital Clínico Universitario de Valladolid, Valladolid, Spain.
Purpose: The aim of this study is to obtain the anatomical limits of the parapharyngeal space by transoral surgical approach, in order to objectively determine the types of lesions according to location, where this type of approach is more indicated.
Methods: A prospective, experimental, radio-anatomical study was performed on 10 cryopreserved human heads(20 sides). A transoral approach of the parapharyngeal space was performed determining its anatomical limits by CT navigation.
A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.
NPJ Digit Med
January 2025
Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.
View Article and Find Full Text PDFObjective: To explore the lived experiences and extent of cognitive symptoms in Long COVID (LC) in a UK-based sample.
Design: This study implemented a mixed-methods design. Eight focus groups were conducted to collect qualitative data, and the Framework Analysis was used to reveal the experiences and impact of cognitive symptoms.
Assessment of deep learning technique for fully automated mandibular segmentation.
Am J Orthod Dentofacial Orthop
February 2025
Department of Orthodontics, Faculty of Dentistry, Çanakkale Onsekiz Mart University, Çanakkale, Turkey.
Introduction: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.
Methods: A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model.
Want 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!