Assessing the FAIRness of Deep Learning Models in Cardiovascular Disease Using Computed Tomography Images: Data and Code Perspective

被引：1

作者：

Shiferaw, Kirubel Biruk ^{[1
]}

Zeleke, Atinkut ^{[1
]}

Waltemath, Dagmar ^{[1
]}

机构：

[1] Univ Med Greifswald, Inst Community Med, Med Informat Lab, Greifswald, Germany

来源：

CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023 | 2023年 / 302卷

关键词：

FAIR Principles; Deep learning; cardiovascular disease; computed tomography; RDA FAIR Data maturity model; ARTIFICIAL-INTELLIGENCE;

D O I：

10.3233/SHTI230065

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：63 / 67

页数：5

共 21 条

[1]

Alliance R.D, RDA FAIR Data maturity model

[2]

Bossuyt P.M., Towards complete and accurate reporting of studies of diagnostic accuracy: the 138

[3] FAIRshake: Toolkit to Evaluate the FAIRness of Research Digital Resources [J].

Clarke, Daniel J. B. ;

Wang, Lily ;

Jones, Alex ;

Wojciechowicz, Megan L. ;

Torre, Denis ;

Jagodnik, Kathleen M. ;

Jenkins, Sherry L. ;

McQuilton, Peter ;

Flamholz, Zachary ;

Silverstein, Moshe C. ;

Schilder, Brian M. ;

Robasky, Kimberly ;

Castillo, Claris ;

Idaszak, Ray ;

Ahalt, Stanley C. ;

Williams, Jason ;

Schurer, Stephan ;

Cooper, Daniel J. ;

Azevedo, Ricardo de Miranda ;

Klenk, Juergen A. ;

Haendel, Melissa A. ;

Nedzel, Jared ;

Avillach, Paul ;

Shimoyama, Mary E. ;

Harris, Rayna M. ;

Gamble, Meredith ;

Poten, Rudy ;

Charbonneau, Amanda L. ;

Larkin, Jennie ;

Brown, C. Titus ;

Bonazzi, Vivien R. ;

Dumontier, Michel J. ;

Sansone, Susanna-Assunta ;

Ma'ayan, Avi .

CELL SYSTEMS, 2019, 9 (05) :417-421

[4]

Collins GS, 2015, ANN INTERN MED, V162, P55, DOI [10.1038/bjc.2014.639, 10.7326/M14-0697, 10.1016/j.jclinepi.2014.11.010, 10.1186/s12916-014-0241-z, 10.7326/M14-0698, 10.1111/eci.12376, 10.1136/bmj.g7594, 10.1002/bjs.9736, 10.1016/j.eururo.2014.11.025]

[5]

FAIR Data Maturity Model Working Group, 2020, Specification and Guidelines (1.0), DOI [10.15497/rda00050, DOI 10.15497/RDA00050]

[6] A review of deep learning based methods for medical image multi-organ segmentation [J].

Fu, Yabo ;

Lei, Yang ;

Wang, Tonghe ;

Curran, Walter J. ;

Liu, Tian ;

Yang, Xiaofeng .

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2021, 85 :107-122

[7]

Gundersen OE, 2020, AI MAG, V41, P103

[8]

Gundersen OE, 2018, AAAI CONF ARTIF INTE, P1644

[9] dtoolAI: Reproducibility for Deep Learning [J].

Hartley, Matthew ;

Olsson, Tjelvar S. G. .

PATTERNS, 2020, 1 (05)

[10] Issues in the Reproducibility of Deep Learning Results [J].

Jean-Paul, S. ;

Elseify, T. ;

Obeid, I. ;

Picone, J. .

2019 IEEE SIGNAL PROCESSING IN MEDICINE AND BIOLOGY SYMPOSIUM (SPMB), 2019,

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