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Hypothesis Driven Gross Anatomy Learning for Brain and Cranial Cavity Dissection Using MRI Imaging of Cadavers and Delivered Utilizing XR‐Web Technology
Author(s) -
Nakamatsu Nicole A.,
Mikami Brandi,
Thompson Jesse D.,
Davis McKay,
Rettenmeier Christoph,
Maziero Danilo,
Stenger V. Andrew,
Labrash Steven,
Lenze Stacy,
Torigoe Trevor,
Lozanoff Beth K.,
Lee U-Young,
Kaya Brock,
Smith Alice,
Miles J. Douglas,
Lozanoff Scott K.
Publication year - 2020
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2020.34.s1.04898
Subject(s) - dissection (medical) , gross anatomy , likert scale , medicine , cadaver , upload , workflow , radiology , medical physics , anatomy , psychology , computer science , developmental psychology , database , operating system
As part of the AAMC accreditation process, medical school faculty must “ define patient types and clinical conditions that all students are expected to encounter ” and generate learning experiences (LCME Standard 6: www.lcme.org/publications ). The purpose of this study was to develop a multi‐departmental workflow for gross anatomy dissection utilizing our unique “patient” group represented by our Willed Body Program (WBP). This workflow was applied to the Head and Neck dissection series. MRI scans of WBP donors were obtained, subsequent to embalming, and were uploaded to rad3d.com. “Subject (S)”, “Medical History (M)” and “Physical Exam (PE)” data were uploaded followed by “Radiology (R)” and “Pathology (P)” reports. Students were presented with initial assessment information that was discussed within groups. Hypotheses were generated and recorded with Google Forms. Students were subsequently presented with R and P reports while using the software during the interactive session. Students also accessed relevant 3D segmented, photogrammetric and illustrative models. Diagnostic features were reviewed and diagnoses were rendered, which were subsequently tested in a dissection exercise. The models were also viewed as XR models using zSpace computers. Results of the hypothesis testing revealed that students were able to identify neurological abnormalities related to radiological findings in cadavers. However, some hypotheses were disconnected from the official cause of death. A survey of seven questions was conducted to assess student opinion (n=73) using a 5‐point Likert scale. Results showed that students found MRI scans of cadavers to be useful while dissecting and that MRI scans provided an understanding of relevant anatomy, as demonstrated by a mean score of 4.14 (SD 1.1) and 4.34 (SD 0.9). 78.1% of students used Rad3D software to view MRI scans of cadavers. However, difficulty of use was found to be average as demonstrated by a mean score of 2.92 (SD 1.0). 41.1% of students used zSpace technology with a majority of students agreeing that it provided an understanding of spatial relationships of the diseased structures, as demonstrated by a mean score of 3.60 (SD 1.0), while 97.3% of students reported wanting more interactive sessions using MRI scans of cadavers. Based on these results, we conclude that cadaveric MRI scan visualization promotes medical student hypothesis generation and is useful in students’ understanding of anatomical dissections and Problem‐Based Learning cases. Furthermore, we conclude that this approach is consistent with student directed learning and deserves further exploration as the basis for gross anatomy dissection in the medical curriculum. Support or Funding Information Supported, in part, by XLR8UH and Quake VC.