Checklist for Reproducibility of Deep Learning in Medical Imaging.
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...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1664 - 1674 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179554151&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554151 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2024 vid: 37 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179554151 179554151 179554151 10.1007/s10278-024-01065-2 179554151 ppf: 1664 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Checklist for Reproducibility of Deep Learning in Medical Imaging. aug: au: 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 S. Yacoub, Joseph H. Hou, Yuankai Younis, Khaled Erickson, Bradley J. Krupinski, Elizabeth Faghani, Shahriar affil: https://ror.org/02qp3tb03 Mayo Clinic Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA sug: subj: Diagnostic Imaging Image Interpretation, Computer Assisted Deep Learning Reproducibility of Results Evaluation Checklists Evaluation Human Documentation Delphi Technique Reliability Item Analysis Summated Rating Scaling Content Validity Face Validity Questionnaires ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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