ArticleBMC medical education2023
A new neuroanatomical two-dimensional fitting three-dimensional imaging techniques in neuroanatomy education.
Article in BMC medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
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Who cites it
3 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Effectiveness of technology-enhanced learning in neuroanatomy education among health professionals - a systematic review.BMC medical education · 2026Pooled it
- 3D visualization technology for Learning human anatomy among medical students and residents: a meta- and regression analysis.BMC medical education · 2024Pooled it
- 3D color multimodality fusion imaging as an augmented reality educational and surgical planning tool for extracerebral tumors.Neurosurgical review · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundNeuroanatomy is the most abstract and complex anatomy. Neurosurgeons have to spend plenty of time mastering the nuances of the autopsy. However, the laboratory that can meet the requirements of neurosurgery microanatomy is only owned by several large medical colleges because it is an expensive affair. Thus, laboratories worldwide are searching for substitutes,but the reality and local details might not meet the exact requirements of the anatomical structure. Herein, we compared the traditional teaching mode, the 3D image generated by the current advanced hand-held scanner and our self-developed 2D image fitting 3D imaging method in the comparative study of neuroanatomy education.
methodsTo examine the efficacy of two-dimensional fitting three-dimensional imaging techniques in neuroanatomy education. 60 clinical students of grade 2020 in Wannan Medical College were randomly divided into traditional teaching group, hand held scanner 3D imaging group and 2D fitting 3D method group, with 20 students in each group.First, the modeling images of the hand held scanner 3D imaging group and the 2D fitting 3D method group are analyzed and compared, and then the teaching results of the three groups are evaluated by objective and subjective evaluation methods. The objective evaluation is in the form of examination papers, unified proposition and unified score; The subjective evaluation is conducted in the form of questionnaires to evaluate.
resultsThe modeling and image analysis of the current advanced hand-held 3D imaging scanner and our self-developed 2D fitting 3D imaging method were compared.The images (equivalent to 1, 10, and 40 × magnification) of the model points and polygons using the Cinema 4D R19 virtual camera of 50, 500, and 2000 mm showed 1,249,955 points and 2,500,122 polygons in the skull data obtained using the hand-held scanner. The 3D model data of the skull consisted of 499,914 points, while the number of polygons reached up to 60,000,000, which was about fourfold that of the hand-held 3D scanning. This model used 8 K mapping technology, and hand-held scanner 3D imaging 3D scanning modeling used a 0.13 K map based on the map data, thereby indicating that the 2D fitting 3D imaging method is delicate and real. Comparative analysis of general data of three groups of students.The comparison of test results, clinical practice assessment and teaching satisfaction of the three groups shows that the performance of hand held scanner 3D imaging group is better than that of traditional teaching group (P < 0.01), and that of 2D fitting 3D method group is significantly better than that of traditional teaching group (P < 0.01).
conclusionsThe method used in this study can achieve real reduction. Compared to hand-held scanning, this method is more cost-effective than the cost of the equipment and the results. Moreover, the post-processing is easy to master, and the autopsy can be performed easily after learning, negating the need to seek professional help. It has a wide application prospect in teaching.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.