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Training the next generation of physicians for artificial intelligence-assisted clinical neuroradiology: ASNR MICCAI Brain Tumor Segmentation (BraTS) 2025 Lighthouse Challenge education platform
0
Zitationen
48
Autoren
2025
Jahr
Abstract
High-quality reference standard image data creation by neuroradiology experts for automated clinical tools can be a powerful tool for neuroradiology & artificial intelligence education. We developed a multimodal educational approach for students and trainees during the MICCAI Brain Tumor Segmentation Lighthouse Challenge 2025, a landmark initiative to develop accurate brain tumor segmentation algorithms. Fifty-six medical students & radiology trainees volunteered to annotate brain tumor MR images for the BraTS challenges of 2023 & 2024, guided by faculty-led didactics on neuropathology MRI. Among the 56 annotators, 14 select volunteers were then paired with neuroradiology faculty for guided one-on-one annotation sessions for BraTS 2025. Lectures on neuroanatomy, pathology & AI, journal clubs & data scientist-led workshops were organized online. Annotators & audience members completed surveys on their perceived knowledge before & after annotations & lectures respectively. Fourteen coordinators, each paired with a neuroradiologist, completed the data annotation process, averaging 1322.9+/-760.7 hours per dataset per pair and 1200 segmentations in total. On a scale of 1-10, annotation coordinators reported significant increase in familiarity with image segmentation software pre- and post-annotation, moving from initial average of 6+/-2.9 to final average of 8.9+/-1.1, and significant increase in familiarity with brain tumor features pre- and post-annotation, moving from initial average of 6.2+/-2.4 to final average of 8.1+/-1.2. We demonstrate an innovative offering for providing neuroradiology & AI education through an image segmentation challenge to enhance understanding of algorithm development, reinforce the concept of data reference standard, and diversify opportunities for AI-driven image analysis among future physicians.
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Autoren
- Raisa Amiruddin
- Nikolay Yordanov
- Nazanin Maleki
- Pascal Fehringer
- Athanasios Gkampenis
- Anastasia Janas
- Kiril Krantchev
- Ahmed W. Moawad
- Fabian Umeh
- Salma AS Abosabie
- Sara A. Abosabie
- Abdullah Alotaibi
- Mohamed Ghonim
- Mohanad Ghonim
- Sedra Abou Ali Mhana
- Nathan Page
- Marko Jakovljevic
- Yasaman Sharifi
- Prateek Bhatia
- Amirreza Manteghinejad
- Melisa S. Guelen
- Michael C. Veronesi
- Virginia Hill
- Tiffany Y. So
- Mark Krycia
- Bojan Petrović
- Fatima Memon
- Justin Cramer
- Elizabeth Schrickel
- Vilma Kosović
- Lorenna Vidal
- Gerard Thompson
- Ichiro Ikuta
- Basimah Albalooshy
- Ali Nabavizadeh
- Nourel Hoda Tahon
- Karuna Shekdar
- Aashim Bhatia
- Claudia Kirsch
- Gennaro D’Anna
- Philipp Lohmann
- Amal Saleh Nour
- Andriy Myronenko
- Adam Goldman-Yassen
- Janet R. Reid
- Sanjay Aneja
- Spyridon Bakas
- Mariam Aboian