Treffer: An Educational Graphical User Interface to Construct Convolutional Neural Networks for Teaching Artificial Intelligence in Radiology.

Title:
An Educational Graphical User Interface to Construct Convolutional Neural Networks for Teaching Artificial Intelligence in Radiology.
Authors:
Jin H; Division of Engineering Science, University of Toronto, Toronto, ON, Canada., Wagner MW; Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.; Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada.; Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada., Ertl-Wagner B; Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.; Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada.; Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada., Khalvati F; Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.; Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada.; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.; Department of Computer Science, University of Toronto, Toronto, ON, Canada.
Source:
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes [Can Assoc Radiol J] 2023 Aug; Vol. 74 (3), pp. 526-533. Date of Electronic Publication: 2022 Dec 07.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: SAGE Publications Country of Publication: United States NLM ID: 8812910 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1488-2361 (Electronic) Linking ISSN: 08465371 NLM ISO Abbreviation: Can Assoc Radiol J Subsets: MEDLINE
Imprint Name(s):
Publication: 2020- : Thousand Oaks, CA : SAGE Publications
Original Publication: [Montreal, Quebec : Medicöpea International Inc., 1986-
MeSH Terms:
Artificial Intelligence* , Radiology*/methods, Humans ; Neural Networks, Computer ; Radiography ; Machine Learning
Contributed Indexing:
Keywords: convolutional neural network; education; graphical user interface; machine learning; radiology
Entry Date(s):
Date Created: 20221208 Date Completed: 20230724 Latest Revision: 20230724
Update Code:
20250114
DOI:
10.1177/08465371221144264
PMID:
36475925
Database:
MEDLINE

Weitere Informationen

Deep learning techniques using convolutional neural networks (CNNs) have been successfully developed for various medical image analysis tasks. However, the skills to understand and develop deep learning models are not usually taught during radiology training, which constitutes a barrier for radiologists looking to integrate machine learning (ML) into their research or clinical practice. In this work, we developed and evaluated an educational graphical user interface (GUI) to construct CNNs for teaching deep learning concepts to radiology trainees. The GUI was developed in Python using the PyQt and PyTorch frameworks. The functionality of the GUI was demonstrated through a binary classification task on a dataset of MR images of the brain. The usability of the GUI was assessed through 45-min user testing sessions with 5 neuroradiologists and neuroradiology fellows, assessing mean task completion times, the System Usability Scale (SUS), and a qualitative questionnaire as metrics. Task completion times were compared against a ML expert who performed the same tasks. After a 20-min introduction to CNNs and a walkthrough of the GUI, users were able to perform all assigned tasks successfully. There was no significant difference in task completion time compared to a ML expert. The educational GUI achieved a score of 82.5 on the SUS, suggesting that the system is highly usable. Users indicated that the GUI seems useful as an educational tool to teach ML topics to radiology trainees. An educational GUI allows interactive teaching in ML that can be incorporated into radiology training.