Assessing the FAIRness of Deep Learning Models in Cardiovascular Disease Using Computed Tomography Images: Data and Code Perspective...33rd Medical Informatics Europe Conference (MIE2023), May 22-25, 2023, Gothenburg, Sweden.
The interest in the application of AI in medicine has intensely increased over the past decade with most of the changes in the past five years. Most recently, the application of deep learning algorithms in prediction and classification of cardiovascular diseases (CVD) using computed tomography (CT)...
| Publicado en: | Studies in Health Technology & Informatics Vol. 302; pp. 63 - 68 |
|---|---|
| Autores principales: | , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2023
|
| 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=163842092&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163842092 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2023 vid: 302 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 163842092 163842092 163842092 10.3233/SHTI230065 163842092 ppf: 63 ppct: 5 formats: tig: atl: Assessing the FAIRness of Deep Learning Models in Cardiovascular Disease Using Computed Tomography Images: Data and Code Perspective...33rd Medical Informatics Europe Conference (MIE2023), May 22-25, 2023, Gothenburg, Sweden. aug: au: SHIFERAW, Kirubel Biruk ZELEKE, Atinkut WALTEMATH, Dagmar affil: Medical Informatics Laboratory, Institute for Community Medicine, University Medicine Greifswald, Germany sug: subj: Sensitivity and Specificity Evaluation Deep Learning Cardiovascular Diseases Radiography Cardiovascular Risk Factors Risk Assessment Tomography, X-Ray Computed Image Interpretation, Computer Assisted Human Congresses and Conferences Sweden Sweden Artificial Intelligence Algorithms Data Management Cardiovascular Diseases Classification Prediction Models Descriptive Statistics ab: The interest in the application of AI in medicine has intensely increased over the past decade with most of the changes in the past five years. Most recently, the application of deep learning algorithms in prediction and classification of cardiovascular diseases (CVD) using computed tomography (CT) images showed promising results. The notable and exciting advancement in this area of study is, however, associated with different challenges related to the findability (F), accessibility(A), interoperability(I), reusability(R) of both data and source code. The aim of this work is to identify reoccurring missing FAIR-related features and to assess the level of FAIRness of data and models used to predict/diagnose cardiovascular diseases from CT images. We evaluated the FAIRness of data and models in published studies using the RDA (Research Data Alliance) FAIR Data maturity model and FAIRshake toolkit. The finding showed that although AI is anticipated to bring ground breaking solutions for complex medical problems, the findability, accessibility, interoperability and reusability of data/metadata/code is still a prominent challenge. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|