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Effect of Annotated Computed Tomography Images on Students’ Recognition of Deep Structures

Abstract

The study examines the effect of annotated computed tomography images on students’ recognition of deep anatomical structures, with emphasis on the use of clearly labelled CT images to improve the identification and understanding of structures that may be difficult to appreciate through surface anatomy, conventional diagrams, or physical specimens. Computed tomography provides detailed cross-sectional representations of the human body and allows structures to be viewed according to their location, shape, density, and relationships with surrounding tissues. Annotation of these images may provide additional visual guidance and support students’ interpretation of complex anatomical regions. The study will assess students’ ability to recognize selected deep anatomical structures before and after exposure to annotated computed tomography images. The specific objectives will include determining students’ baseline ability to identify deep structures on CT images, exposing students to structured learning activities using annotated images, assessing their recognition of the structures following the intervention, and comparing their performance before and after exposure to the teaching method. A quasi-experimental research design will be adopted for the study. An appropriate sample of undergraduate students will be selected using a suitable sampling technique and exposed to structured anatomy teaching involving annotated CT images of selected anatomical regions. The teaching activities will involve identification of deep structures, interpretation of anatomical relationships, recognition of sectional levels, and correlation of CT appearances with anatomical models, diagrams, and established anatomical knowledge. Data will be collected using standardized image-based recognition assessments and analyzed using descriptive statistics such as frequency, percentage, mean, and standard deviation, with appropriate inferential tests applied where necessary. The study is expected to reveal improved recognition of deep anatomical structures among students following exposure to annotated CT images. Students may demonstrate better identification of structures, understanding of spatial relationships, recognition of anatomical levels, and interpretation of structures that are difficult to visualize using conventional teaching resources. Greater difficulties may remain in distinguishing closely related structures, interpreting complex sectional images, and recognizing structures with similar appearances or variable anatomical positions. The findings will be useful to anatomists, anatomy educators, radiologists, radiographers, medical students, physiotherapy students, and other healthcare trainees involved in anatomical imaging and clinical education. The study may provide useful information on the educational value of annotated CT images in developing students’ ability to interpret deep anatomy and may assist educators in integrating image-based resources with conventional lectures, models, specimens, and anatomical atlases. The study will conclude by determining the effect of annotated computed tomography images on students’ recognition of deep anatomical structures. It is recommended that appropriately selected and clearly annotated CT images should be incorporated into anatomy teaching to strengthen students’ understanding of deep and cross-sectional structures. Further studies involving larger and more diverse student populations should be conducted to evaluate the effectiveness of annotated imaging resources across different anatomical regions and levels of anatomy education.

Keywords: Annotated computed tomography images, deep anatomical structures, anatomy education, undergraduate students, computed tomography, radiological anatomy, anatomical structure recognition, cross-sectional anatomy, medical imaging, anatomical relationships, image-based learning, anatomy learning, practical anatomy, radiographic anatomy, medical education.