Background: Students' ability to diagnose various blood disorders could be substantially improved by continuously reviewing approaches toward teaching hematology. This study aims to compare the effectiveness of light microscopes and projected images on students' learning and determine medical students' perception of these teaching methods.
Methods: A randomized trial was conducted using a crossover design. Two groups, each with 30 students, were subjected to teaching methods based on light microscopes and projected images alternatively.
Results: No differences were found in the two study groups' baseline characteristics, such as median age, sex, and prior academic performance, as well as in the pre-test scores. Post-test scores were significantly higher among students subjected to the projection method than in the control group (Mean ± SD = 9.8 ± 1.7 vs. 5.1 ± 1.3, < 0.001). In the post-cross-over assessment, 85% ( = 51) of students reported their satisfaction for the projected images, and 78% ( = 47) of students were willing to be taught by projection. Students perceived that the projection method facilitated participation and better involvement in discussions, improved learning, provided greater motivation, and eventually increased comprehension and efficiency.
Conclusion: The projection-based teaching method is more effective in improving knowledge and achieving intended learning outcomes. Students tend to prefer the projection method over the laboratory-based method and perceive it as an effective method to enhance their learning of hematology.
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http://dx.doi.org/10.3389/fmed.2024.1340359 | DOI Listing |
IET Syst Biol
January 2025
School of Computer, University of South China, Hengyang, Hunan, China.
Spatially resolved transcriptomics technologies potentially provide the extra spatial position information and tissue image to better infer spatial cell-cell interactions (CCIs) in processes such as tissue homeostasis, development, and disease progression. However, methods for effectively integrating spatial multimodal data to infer CCIs are still lacking. Here, the authors propose a deep learning method for integrating features through co-convolution, called SpaGraphCCI, to effectively integrate data from different modalities of SRT by projecting gene expression and image feature into a low-dimensional space.
View Article and Find Full Text PDFPulm Circ
January 2025
Department of Imaging and Pathology, Biomedical MRI KU Leuven Leuven Belgium.
The pulmonary vasculature plays a pivotal role in the development and progress of chronic lung diseases. Due to limitations of conventional two-dimensional histological methods, the complexity and the detailed anatomy of the lung blood circulation might be overlooked. In this study, we demonstrate the practical use of optical serial block face imaging (SBFI), ex vivo microcomputed tomography (micro-CT), and nondestructive optical tomography for visualization and quantification of the pulmonary circulation's 3D architecture from macro- to micro-structural levels in murine lung samples.
View Article and Find Full Text PDFFront Artif Intell
January 2025
CONAHCYT-Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, San Luis Potosí, Mexico.
This systematic review provides a state-of-art of Artificial Intelligence (AI) models such as Machine Learning (ML) and Deep Learning (DL) development and its applications in Mexico in diverse fields. These models are recognized as powerful tools in many fields due to their capability to carry out several tasks such as forecasting, image classification, recognition, natural language processing, machine translation, etc. This review article aimed to provide comprehensive information on the Machine Learning and Deep Learning algorithms applied in Mexico.
View Article and Find Full Text PDFSci Rep
January 2025
Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, 224001, Jiangsu, China.
Convolutional Neural Networks (CNNs) have achieved remarkable segmentation accuracy in medical image segmentation tasks. However, the Vision Transformer (ViT) model, with its capability of extracting global information, offers a significant advantage in contextual information compared to the limited receptive field of convolutional kernels in CNNs. Despite this, ViT models struggle to fully detect and extract high-frequency signals, such as textures and boundaries, in medical images.
View Article and Find Full Text PDFMed Image Anal
January 2025
Department of Applied Mathematics, Technical Medical Centre, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands.
The orientation of a blood vessel as visualized in 3D medical images is an important descriptor of its geometry that can be used for centerline extraction and subsequent segmentation, labeling, and visualization. Blood vessels appear at multiple scales and levels of tortuosity, and determining the exact orientation of a vessel is a challenging problem. Recent works have used 3D convolutional neural networks (CNNs) for this purpose, but CNNs are sensitive to variations in vessel size and orientation.
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