Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review.
Since 2000, there have been more than 8000 publications on radiology artificial intelligence (AI). AI breakthroughs allow complex tasks to be automated and even performed beyond human capabilities. However, the lack of details on the methods and algorithm code undercuts its scientific value. Many sc...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 5; pp. 2306 - 2313 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2023
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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=171950875&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950875 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171950875 164736490 171950875 171950875 10.1007/s10278-023-00870-5 171950875 ppf: 2306 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review. aug: au: Moassefi, Mana Rouzrokh, Pouria Conte, Gian Marco Vahdati, Sanaz Fu, Tianyuan Tahmasebi, Aylin Younis, Mira Farahani, Keyvan Gentili, Amilcare Kline, Timothy Kitamura, Felipe C. Huo, Yuankai Kuanar, Shiba Younis, Khaled Erickson, Bradley J. Faghani, Shahriar affil: https://ror.org/03zzw1w08 Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, Rochester, MN, USA sug: subj: Reproducibility of Results Deep Learning Algorithms Diagnostic Imaging Evaluation Human Systematic Review World Wide Web Artificial Intelligence Descriptive Statistics ab: Since 2000, there have been more than 8000 publications on radiology artificial intelligence (AI). AI breakthroughs allow complex tasks to be automated and even performed beyond human capabilities. However, the lack of details on the methods and algorithm code undercuts its scientific value. Many science subfields have recently faced a reproducibility crisis, eroding trust in processes and results, and influencing the rise in retractions of scientific papers. For the same reasons, conducting research in deep learning (DL) also requires reproducibility. Although several valuable manuscript checklists for AI in medical imaging exist, they are not focused specifically on reproducibility. In this study, we conducted a systematic review of recently published papers in the field of DL to evaluate if the description of their methodology could allow the reproducibility of their findings. We focused on the Journal of Digital Imaging (JDI), a specialized journal that publishes papers on AI and medical imaging. We used the keyword "Deep Learning" and collected the articles published between January 2020 and January 2022. We screened all the articles and included the ones which reported the development of a DL tool in medical imaging. We extracted the reported details about the dataset, data handling steps, data splitting, model details, and performance metrics of each included article. We found 148 articles. Eighty were included after screening for articles that reported developing a DL model for medical image analysis. Five studies have made their code publicly available, and 35 studies have utilized publicly available datasets. We provided figures to show the ratio and absolute count of reported items from included studies. According to our cross-sectional study, in JDI publications on DL in medical imaging, authors infrequently report the key elements of their study to make it reproducible. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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