RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models
Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases. As such, deep learning approaches could provide automated solutions for such applications. However, the potential of these techniques is often undermined by challenges in reproducibility and generalizability, which are key barriers to their clinical adoption. This paper introduces the RIDGE checklist, a comprehensive framework designed to assess the Reproducibility, Integrity, Dependability, Generalizability, and Efficiency of deep learning-based medical image segmentation models. The RIDGE checklist is not just a tool for evaluation but also a guideline for researchers striving to improve the quality and transparency of their work. By adhering to the principles outlined in the RIDGE checklist, researchers can ensure that their developed segmentation models are robust, scientifically valid, and applicable in a clinical setting.
Authors
- Atlas Haddadi Avval (ORCID: https://orcid.org/0000-0002-3896-7810)
- Roxana Daneshjou (ORCID: https://orcid.org/0000-0001-7988-9356)
- Timothy L. Kline (ORCID: https://orcid.org/0000-0002-7917-9853)
- Tapotosh Ghosh (ORCID: https://orcid.org/0000-0001-5228-666X)
- Farhad Maleki (ORCID: https://orcid.org/0000-0002-5673-8210)
- Mana Moassefi
- Daniel Warren (ORCID: https://orcid.org/0000-0002-6973-6900)
- Bardia Khosravi (ORCID: https://orcid.org/0000-0002-8024-339X)
- Pouria Rouzrokh (ORCID: https://orcid.org/0000-0003-4664-0751)
- Linda Moy (ORCID: https://orcid.org/0000-0001-9564-9360)
- Neil Tenenholtz (ORCID: https://orcid.org/0000-0003-1250-3716)
- Katie Ovens (ORCID: https://orcid.org/0000-0001-5956-7739)
- Susan Sotardi (ORCID: https://orcid.org/0000-0001-8389-2419)
- Reza Forghani (ORCID: https://orcid.org/0000-0002-8572-1864)
- Felipe Kitamura (ORCID: https://orcid.org/0000-0002-9992-5630)
- Ali Ganjizadeh (ORCID: https://orcid.org/0000-0001-6968-092X)
- Steve G. Langer
Institutions
- Microsoft (United States) (US)
- Children's Hospital of Philadelphia (US)
- University of Illinois Urbana-Champaign (US)
- Mashhad University of Medical Sciences (IR)
- University of Calgary (CA)
- University of Florida (US)
- NYU Langone Health (US)
- Mayo Clinic in Arizona (US)
- Stanford Medicine (US)
- DASA (Brazil)
- McGill University (CA)
Publication Details
- Journal
- Journal of Imaging Informatics in Medicine
- Published
- 2024-11-18
- DOI
- https://doi.org/10.1007/s10278-024-01282-9
- Citations
- 6
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.72
Funders
- National Science Foundation
- Gordon and Betty Moore Foundation
- Radiological Society of North America
- Lunit
- National Institutes of Health
- National Cancer Institute