Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI.
Purpose: The present scoping review aims to assess the non-inferiority of distributed learning over centrally and locally trained machine learning (ML) models in medical applications. Methods: We performed a literature search using the term "distributed learning" OR "federated learning" in the PubMe...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 48; no. 12; pp. 3791 - 3805 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2021
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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=152744558&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152744558 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Nov2021 vid: 48 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152744558 149770784 10.1007/s00259-021-05339-7 152744558 ppf: 3791 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI. aug: au: Kirienko, Margarita Sollini, Martina Ninatti, Gaia Loiacono, Daniele Giacomello, Edoardo Gozzi, Noemi Amigoni, Francesco Mainardi, Luca Lanzi, Pier Luca Chiti, Arturo affil: Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy sug: ab: Purpose: The present scoping review aims to assess the non-inferiority of distributed learning over centrally and locally trained machine learning (ML) models in medical applications. Methods: We performed a literature search using the term "distributed learning" OR "federated learning" in the PubMed/MEDLINE and EMBASE databases. No start date limit was used, and the search was extended until July 21, 2020. We excluded articles outside the field of interest; guidelines or expert opinion, review articles and meta-analyses, editorials, letters or commentaries, and conference abstracts; articles not in the English language; and studies not using medical data. Selected studies were classified and analysed according to their aim(s). Results: We included 26 papers aimed at predicting one or more outcomes: namely risk, diagnosis, prognosis, and treatment side effect/adverse drug reaction. Distributed learning was compared to centralized or localized training in 21/26 and 14/26 selected papers, respectively. Regardless of the aim, the type of input, the method, and the classifier, distributed learning performed close to centralized training, but two experiments focused on diagnosis. In all but 2 cases, distributed learning outperformed locally trained models. Conclusion: Distributed learning resulted in a reliable strategy for model development; indeed, it performed equally to models trained on centralized datasets. Sensitive data can get preserved since they are not shared for model development. Distributed learning constitutes a promising solution for ML-based research and practice since large, diverse datasets are crucial for success. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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