Maleki F, Moy L, Forghani R, Ghosh T, Ovens K, Langer S, Rouzrokh P, Khosravi B, Ganjizadeh A, Warren D, Daneshjou R, Moassefi M, Avval AH, Sotardi S, Tenenholtz N, Kitamura F, and Kline T
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., Competing Interests: Declarations Ethics Approval This research does not involve human participants, their data or biological material. Consent to Participate Not applicable as this research does not involve human subjects. Consent for Publication The manuscript does not contain any individual person’s data in any form (including individual details, images, or videos). All visualization content is anonymized and derived from public resources, ensuring that all data used is publicly available. Competing Interests FM, LM, PR, BK, AG, DW, RD, MM, AHA, SS, NT, and TK are members of the Machine Learning Education Subcommittee of Society for Imaging Informatics in Medicine (SIIM). LM is the Editor of Radiology with salary support from RSNA and serves on the Editorial Board of JMRI. LM has received grant support from the Siemens Research Grant, the Gordon and Betty Moore Foundation, the Mary Kay Foundation, Google, and NCI/NIH. LM has received personal fees from Lunit Insight, ICAD, Guerbet, and Medscape and is on the Advisory Board for ICAD, Lunit, and Guerbet. LM holds stock options in Lunit and has been reimbursed for meeting and travel expenses by the British Society of Breast Radiology, the European Society of Breast Imaging, and the Korean Society of Radiology. LM is also a member of the ISMRM Board of Trustees and serves on the ACR Data Safety Monitoring Board. RF has had a research collaboration/grant and has acted as a consultant and/or speaker for Nuance Communications Inc., Canon Medical Systems Inc., and GE Healthcare. RF is also a co-investigator on a National Institutes of Health STTR grant subaward and a co-principal investigator on a National Science Foundation grant. FK is the Vice-chair of the SIIM Machine Learning Committee, a member of the RIC at RSNA, a member of the AI committee at RSNA, an Early Career Consultant to the Editor of Radiology, and an Associate Editor for Radiology: Artificial Intelligence. FK is also a consultant for MD.ai, a consultant for GE Healthcare, and a speaker for Sharing Progress in Cancer Care. The rest of the authors declare no competing interests., (© 2024. The Author(s).)