A Novel Approach to Design 3D Models in Medical Education.
Ontology highlight
ABSTRACT: Supplementary information
The online version contains supplementary material available at 10.1007/s40670-021-01262-6.
SUBMITTER: Rosen D
PROVIDER: S-EPMC7983107 | biostudies-literature | 2021 Apr
REPOSITORIES: biostudies-literature
Publications
A Novel Approach to Design 3D Models in Medical Education.
Rosen Daniel D Nesic Olivera O
Medical science educator 20210322 2
In this paper, we describe a novel process for creating high-resolution, 3D PDF, 3D printed, and holographic anatomic and pathology models using inexpensive <i>consumer grade electronics</i>, which can be incorporated in any undergraduate or graduate medical school curriculum.<h4>Supplementary information</h4>The online version contains supplementary material available at 10.1007/s40670-021-01262-6. ...[more]
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Project description:BackgroundThree-dimensional (3D) printing technology enables the translation of 2-dimensional (2D) medical imaging into a physical replica of a patient's individual anatomy and may enhance the understanding of congenital heart defects (CHD). We aimed to evaluate the usefulness of a spectrum of 3D-printed models in teaching CHD to medical students.ResultsWe performed a prospective, randomized educational procedure to teach fifth year medical students four CHDs (atrial septal defect (ASD, n = 74), ventricular septal defect (VSD, n = 50), coarctation of aorta (CoA, n = 118) and tetralogy of Fallot (ToF, n = 105)). Students were randomized into printing groups or control groups. All students received the same 20 min lecture with projected digital 2D images. The printing groups also manipulated 3D printed models during the lecture. Both groups answered an objective survey (Multiple-choice questionnaire) twice, pre- and post-test, and completed a post-lecture subjective survey. Three hundred forty-seven students were included and both teaching groups for each CHD were comparable in age, sex and pre-test score. Overall, objective knowledge improved after the lecture and was higher in the printing group compared to the control group (16.3 ± 2.6 vs 14.8 ± 2.8 out of 20, p < 0.0001). Similar results were observed for each CHD (p = 0.0001 ASD group; p = 0.002 VSD group; p = 0.0005 CoA group; p = 0.003 ToF group). Students' opinion of their understanding of CHDs was higher in the printing group compared to the control group (respectively 4.2 ± 0.5 vs 3.8 ± 0.4 out of 5, p < 0.0001).ConclusionThe use of 3D printed models in CHD lectures improve both objective knowledge and learner satisfaction for medical students. The practice should be mainstreamed.
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Project description:ObjectivesThe present study aims to investigate the efficacy of utilizing three-dimensional (3D) printing technology in concert with Problem-Based Learning (PBL) and Case-Based Learning (CBL) pedagogical approaches in educating senior undergraduate clinical medical students on respiratory diseases.MethodsA cohort of 422 fourth-year clinical medicical students of from Anhui Medical University, pursuing a five-year program, were arbitrarily segregated into two distinct groups. The experimental group was subjected to a combined pedagogical approach, which included 3D printing technology, PBL and CBL (referred to as DPC). Conversely, the control group was exposed to conventional teaching methodologies for respiratory disease education. The effectiveness of the teaching methods was subsequently appraised using both theoretical test scores and custom questionnaires.ResultsPost-quiz scores indicated a statistically significant improvement in the DPC group as compared to the traditional group (P < 0.01). Self-evaluation and satisfaction questionnaires revealed that the DPC group's self-assessment scores outperformed the traditional group in several aspects, including clinical thinking ability, learning initiative, self-study ability, anatomical knowledge mastery, confidence in learning, ability to analyze and solve problems, comprehension of the knowledge, help to clinical thinking and level of satisfaction on the teaching methods (P < 0.01). However, within the unsatisfied DPC sub-group, none of these self-assessment aspects, except for comprehension of the knowledge, impacted the learning efficacy (P > 0.05).ConclusionThe deployment of the DPC pedagogical approach may confer unique experiential learning opportunities for students, potentially enhancing theoretical test scores and promoting self-evaluation and satisfaction in the context of respiratory disease education. Hence, it may be instrumental in augmenting the overall teaching efficacy.
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Project description:Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this paradigm, 3D imaging tasks in the most prominent imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D, losing rich 3D anatomical information and inevitably compromising the performance. To overcome this limitation, we have built a set of models, called Generic Autodidactic Models, nicknamed Models Genesis, because they are created ex nihilo (with no manual labeling), self-taught (learned by self-supervision), and generic (served as source models for generating application-specific target models). Our extensive experiments demonstrate that our Models Genesis significantly outperform learning from scratch in all five target 3D applications covering both segmentation and classification. More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging. This performance is attributed to our unified self-supervised learning framework, built on a simple yet powerful observation: the sophisticated yet recurrent anatomy in medical images can serve as strong supervision signals for deep models to learn common anatomical representation automatically via self-supervision. As open science, all pre-trained Models Genesis are available at https://github.com/MrGiovanni/ModelsGenesis.
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Project description:While nutritional interventions are first-line therapy for many chronic diseases, most medical trainees receive minimal nutrition education, leaving them unprepared to address nutritional issues with patients. An interactive, single-session, virtual nutrition curriculum was taught online to 80 physician assistant (PA) students. Topics included plant-based nutrition, dietary history-taking and counseling, and culinary medicine. Students were surveyed before, immediately after, and four weeks after the curriculum to assess changes to nutrition-related knowledge, attitudes, confidence, and personal dietary behaviors. Seventy-three PA students (91%) completed the pre-survey, 76 (95%) completed the post-survey, and 42 (52.5%) completed the delayed post-survey. Knowledge scores increased immediately post-intervention (48.9% to 78.9%; p < 0.001) and persisted four weeks later (78.9% to 75.8%; p = 0.54). Post-intervention, students felt more confident in dietary history-taking (55% vs. 95%; p = 0.001) and nutrition counseling (53% vs. 84%; p = 0.003) and agreed that dietary changes alone could reverse type 2 diabetes (74% vs. 97%; p = 0.027) and coronary artery disease (66% vs. 92%; p = 0.039). Curricula using virtual teaching kitchens may be a scalable approach to nutrition education for medical trainees.
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Project description:Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of three-dimensional (3D) structural information. We have developed a novel graph-based deep generative model that combines state-of-the-art machine learning techniques with structural knowledge. Our method ("DeLinker") takes two fragments or partial structures and designs a molecule incorporating both. The generation process is protein-context-dependent, utilizing the relative distance and orientation between the partial structures. This 3D information is vital to successful compound design, and we demonstrate its impact on the generation process and the limitations of omitting such information. In a large-scale evaluation, DeLinker designed 60% more molecules with high 3D similarity to the original molecule than a database baseline. When considering the more relevant problem of longer linkers with at least five atoms, the outperformance increased to 200%. We demonstrate the effectiveness and applicability of this approach on a diverse range of design problems: fragment linking, scaffold hopping, and proteolysis targeting chimera (PROTAC) design. As far as we are aware, this is the first molecular generative model to incorporate 3D structural information directly in the design process. The code is available at https://github.com/oxpig/DeLinker.
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Project description:ObjectivesThe use of 3D video in medical education has not been fully explored. This article aims to review the evidence on 3D video currently presented in the medical education literature, including its impact on curriculum activities, to reference future research in this field.MethodsAccording to the guidelines of Arksey and O'Malley, the authors used a systematic search strategy (the last search was in December 2022) to search nine literature databases published in English, and only primary studies were included. Two authors independently screened all articles based on the eligibility criteria and performed a thematic analysis of the included literature.ResultsOf 1,302 articles identified, 23 were included for insights into how opportunities for 3D video in medical education are created, how they are experienced, and how they influence and manifest behavior demonstrated partial congruency. Three themes were identified: (a) advantages of using 3D video in medical education; (b) the effect of using 3D video in medical education on students' academic achievement and ability; and (c) students' experience of 3D video in medical education.ConclusionsThe application of 3D video in medical education has won the support of most students and educators. However, the effect of using 3D video in medical education is still controversial. Medical educators should combine the curriculum's characteristics, the students' learning situation, and the existing educational resources and choose to use them after careful consideration.
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Project description:IntroductionOur objective was to evaluate the effectiveness of hands-on training at a bedside ultrasound (US) symposium ("Ultrafest") to improve both clinical knowledge and image acquisition skills of medical students. Primary outcome measure was improvement in multiple choice questions on pulmonary or Focused Assessment with Sonography in Trauma (FAST) US knowledge. Secondary outcome was improvement in image acquisition for either pulmonary or FAST.MethodsProspective cohort study of 48 volunteers at "Ultrafest," a free symposium where students received five contact training hours. Students were evaluated before and after training for proficiency in either pulmonary US or FAST. Proficiency was assessed by clinical knowledge through written multiple-choice exam, and clinical skills through accuracy of image acquisition. We used paired sample t-tests with students as their own controls.ResultsPulmonary knowledge scores increased by a mean of 10.1 points (95% CI [8.9-11.3], p<0.00005), from 8.4 to a posttest average of 18.5/21 possible points. The FAST knowledge scores increased by a mean of 7.5 points (95% CI [6.3-8.7] p<0.00005), from 8.1 to a posttest average of 15.6/21. We analyzed clinical skills data on 32 students. The mean score was 1.7 pretest and 4.7 posttest of 12 possible points. Mean improvement was 3.0 points (p<0.00005) overall, 3.3 (p=0.0001) for FAST, and 2.6 (p=0.003) for the pulmonary US exam.ConclusionThis study suggests that a symposium on US can improve clinical knowledge, but is limited in achieving image acquisition for pulmonary and FAST US assessments. US training external to official medical school curriculum may augment students' education.
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