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. A...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2524 - 2537 |
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| Main Authors: | , , , , , , , , , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278947&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278947 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278947 187278947 187278947 10.1007/s10278-024-01282-9 187278947 ppf: 2524 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models. aug: au: Maleki, Farhad Moy, Linda Forghani, Reza Ghosh, Tapotosh Ovens, Katie Langer, Steve Rouzrokh, Pouria Khosravi, Bardia Ganjizadeh, Ali Warren, Daniel Daneshjou, Roxana Moassefi, Mana Avval, Atlas Haddadi Sotardi, Susan Tenenholtz, Neil Kitamura, Felipe Kline, Timothy affil: https://ror.org/03yjb2x39 Department of Computer Science, University of Calgary, Calgary, AB, Canada sug: subj: Image Processing, Computer Assisted Medical Informatics Reproducibility of Results External Validity Productivity Prediction Models Evaluation Deep Learning Quality Assurance Funding Source Checklists Human Prospective Studies Retrospective Design Sample Size Data Curation Standards Sensitivity and Specificity Evaluation Validation Studies Sociodemographic Factors Comparative Studies Pilot Studies Equipment Failure ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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