Document Type
Article
Publication Date
8-1-2024
Abstract
The application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproducibility and refine techniques. Our study addresses the unique challenges presented by DL in medical imaging by developing a comprehensive checklist using the Delphi method to enhance reproducibility and reliability in this dynamic field. We compiled a preliminary checklist based on a comprehensive review of existing checklists and relevant literature. A panel of 11 experts in medical imaging and DL assessed these items using Likert scales, with two survey rounds to refine responses and gauge consensus. We also employed the content validity ratio with a cutoff of 0.59 to determine item face and content validity. Round 1 included a 27-item questionnaire, with 12 items demonstrating high consensus for face and content validity that were then left out of round 2. Round 2 involved refining the checklist, resulting in an additional 17 items. In the last round, 3 items were deemed non-essential or infeasible, while 2 newly suggested items received unanimous agreement for inclusion, resulting in a final 26-item DL model reporting checklist derived from the Delphi process. The 26-item checklist facilitates the reproducible reporting of DL tools and enables scientists to replicate the study's results.
Recommended Citation
Moassefi, Mana; Singh, Yashbir; Conte, Gian Marco; Khosravi, Bardia; Rouzrokh, Pouria; Vahdati, Sanaz; Safdar, Nabile; Moy, Linda; Kitamura, Felipe; Gentili, Amilcare; Lakhani, Paras; Kottler, Nina; Halabi, Safwan; Yacoub, Joseph; Hou, Yuankai; Younis, Khaled; Erickson, Bradley; Krupinski, Elizabeth; and Faghani, Shahriar, "Checklist for Reproducibility of Deep Learning in Medical Imaging" (2024). Department of Radiology Faculty Papers. Paper 173.
https://jdc.jefferson.edu/radiologyfp/173
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
38483694
Language
English
Included in
Artificial Intelligence and Robotics Commons, Diagnosis Commons, Investigative Techniques Commons, Radiology Commons
Comments
This article is the author's final published version in Journal of Imaging Informatics in Medicine, Volume 37, Issue 4, August 2024, Pages 1664-1673.
The published version is available at https://doi.org/10.1007/s10278-024-01295-4. Copyright © The Authors 2024.
Erratum issued October 2024 republishing the article open access: https://doi.org/10.1007/s10278-024-01295-4.