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Effects of 3D Scans on Veterinary Students' Learning Outcomes Compared to Traditional 2D Images in Anatomy Classes.

ABSTRACT: Students often struggle with interpreting traditional textbook images and translating them to anatomical structures. This study aimed to compare the impact of 3D scans versus 2D images on students' learning outcomes when learning anatomical structures on skulls from horses and pigs. Furthermore, the correlation between spatial ability and learning outcomes using 3D scans or 2D images was examined. Second-year veterinary medicine students either used 3D scans or 2D images, annotated with arrows or numbers as learning material. Students' anatomical knowledge was tested before and after the learning session, and spatial ability was assessed using the mental rotation test. All groups improved significantly in the post-test. However, the differences between groups were not significant, suggesting that 3D scans do not necessarily lead to higher learning outcomes. The analysis of the correlation between spatial ability and learning outcomes did not prove that students with weaker spatial ability benefit from 3D scans. Students preferred 3D scans over 2D images despite similar outcomes, suggesting they are valuable for learning. However, results show that the introduction of novel learning materials likely amplified the impact of reduced learning time on the 3D group, as these materials necessitated additional time for effective comprehension and integration.

SUBMITTER: Schirone R

PROVIDER: S-EPMC11311044 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Effects of 3D Scans on Veterinary Students' Learning Outcomes Compared to Traditional 2D Images in Anatomy Classes.

Schirone Rebecca R Corte Giuliano Mario GM Ehlers Jan P JP Herre Christina C Schmedding Maximiliane M Merle Roswitha R Pachtmann Joëlle J Bahramsoltani Mahtab M

Animals : an open access journal from MDPI 20240725 15

Students often struggle with interpreting traditional textbook images and translating them to anatomical structures. This study aimed to compare the impact of 3D scans versus 2D images on students' learning outcomes when learning anatomical structures on skulls from horses and pigs. Furthermore, the correlation between spatial ability and learning outcomes using 3D scans or 2D images was examined. Second-year veterinary medicine students either used 3D scans or 2D images, annotated with arrows o ...[more]

PMID: 39123697

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Project description:Understanding veterinary anatomy is an essential skill for the study of veterinary medicine as well as for diagnostic imaging and therapy. Dissection facilities are increasingly limited in some schools and its alternatives have often focussed on using two-dimensional images. However, the study of veterinary anatomy is mainly concerned with identifying structures and spatial relationships between them within a 3D space, and the use of 2D teaching approaches does not provide accurate information. We tested whether PlayDoh® student-built models could be an inexpensive potential tool beneficial to veterinary students learning anatomy in three distinct scenarios: (1) during a lecture, introducing a new concept; (2) during a flipped classroom approach where a video-podcast lecture was to be watched by the students prior to the lecture and (3) as a revision session where students brought their own questions and created, under supervision, their own models to respond to them. PlayDoh® sessions benefitted 172 first-year Veterinary Medicine and Animal Science students. The most accurate visualisation of anatomical structures in 3D was the principal benefit mentioned by the learners (35%). In addition, the learners noted that the technique would help with 'retention' (18%). According to the students' preferences, it was possible to create four groups: A, B, C and D. Group A encompassed the methodologies most liked by students and consisted of lectures, dissection and demonstrations. Group B included demonstrations and 3D modelling using PlayDoh®. Group C consisted of 3D modelling using PlayDoh®, books and online and, finally, group D included the methodologies least preferred by students, i.e. online and PBL. Our findings suggest that using 3D PlayDoh® modelling has potential as a method to enhance the learning of veterinary anatomy and may be most valuable to those students learning more complex subject areas that require a 3D teaching approach in practice.Supplementary informationThe online version contains supplementary material available at 10.1007/s40670-023-01892-y.

Project description:Introduction: Osteoporosis is currently diagnosed based on areal bone mineral density (aBMD) computed from 2D DXA scans. However, aBMD is a limited surrogate for femoral strength since it does not account for 3D bone geometry and density distribution. QCT scans combined with finite element (FE) analysis can deliver improved femoral strength predictions. However, non-negligible radiation dose and high costs prevent a systematic usage of this technique for screening purposes. As an alternative, the 3D-Shaper software (3D-Shaper Medical, Spain) reconstructs the 3D shape and density distribution of the femur from 2D DXA scans. This approach could deliver a more accurate estimation of femoral strength than aBMD by using FE analysis on the reconstructed 3D DXA. Methods: Here we present the first independent evaluation of the software, using a dataset of 77 ex vivo femora. We extend a prior evaluation by including the density distribution differences, the spatial correlation of density values and an FE analysis. Yet, cortical thickness is left out of this evaluation, since the cortex is not resolved in our FE models. Results: We found an average surface distance of 1.16 mm between 3D DXA and QCT images, which shows a good reconstruction of the bone geometry. Although BMD values obtained from 3D DXA and QCT correlated well (r 2 = 0.92), the 3D DXA BMD were systematically lower. The average BMD difference amounted to 64 mg/cm3, more than one-third of the 3D DXA BMD. Furthermore, the low correlation (r 2 = 0.48) between density values of both images indicates a limited reconstruction of the 3D density distribution. FE results were in good agreement between QCT and 3D DXA images, with a high coefficient of determination (r 2 = 0.88). However, this correlation was not statistically different from a direct prediction by aBMD. Moreover, we found differences in the fracture patterns between the two image types. QCT-based FE analysis resulted mostly in femoral neck fractures and 3D DXA-based FE in subcapital or pertrochanteric fractures. Discussion: In conclusion, 3D-Shaper generates an altered BMD distribution compared to QCT but, after careful density calibration, shows an interesting potential for deriving a standardized femoral strength from a DXA scan.

Project description:PurposeFluoroscopy is the standard imaging modality used to guide hip surgery and is therefore a natural sensor for computer-assisted navigation. In order to efficiently solve the complex registration problems presented during navigation, human-assisted annotations of the intraoperative image are typically required. This manual initialization interferes with the surgical workflow and diminishes any advantages gained from navigation. In this paper, we propose a method for fully automatic registration using anatomical annotations produced by a neural network.MethodsNeural networks are trained to simultaneously segment anatomy and identify landmarks in fluoroscopy. Training data are obtained using a computationally intensive, intraoperatively incompatible, 2D/3D registration of the pelvis and each femur. Ground truth 2D segmentation labels and anatomical landmark locations are established using projected 3D annotations. Intraoperative registration couples a traditional intensity-based strategy with annotations inferred by the network and requires no human assistance.ResultsGround truth segmentation labels and anatomical landmarks were obtained in 366 fluoroscopic images across 6 cadaveric specimens. In a leave-one-subject-out experiment, networks trained on these data obtained mean dice coefficients for left and right hemipelves, left and right femurs of 0.86, 0.87, 0.90, and 0.84, respectively. The mean 2D landmark localization error was 5.0 mm. The pelvis was registered within [Formula: see text] for 86% of the images when using the proposed intraoperative approach with an average runtime of 7 s. In comparison, an intensity-only approach without manual initialization registered the pelvis to [Formula: see text] in 18% of images.ConclusionsWe have created the first accurately annotated, non-synthetic, dataset of hip fluoroscopy. By using these annotations as training data for neural networks, state-of-the-art performance in fluoroscopic segmentation and landmark localization was achieved. Integrating these annotations allows for a robust, fully automatic, and efficient intraoperative registration during fluoroscopic navigation of the hip.

Dataset Information

Effects of 3D Scans on Veterinary Students' Learning Outcomes Compared to Traditional 2D Images in Anatomy Classes.

Publications

Effects of 3D Scans on Veterinary Students' Learning Outcomes Compared to Traditional 2D Images in Anatomy Classes.

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