1. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions
- Author
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Yang Nan, Javier Del Ser, Simon Walsh, Carola Schönlieb, Michael Roberts, Ian Selby, Kit Howard, John Owen, Jon Neville, Julien Guiot, Benoit Ernst, Ana Pastor, Angel Alberich-Bayarri, Marion I. Menzel, Sean Walsh, Wim Vos, Nina Flerin, Jean-Paul Charbonnier, Eva van Rikxoort, Avishek Chatterjee, Henry Woodruff, Philippe Lambin, Leonor Cerdá-Alberich, Luis Martí-Bonmatí, Francisco Herrera, Guang Yang, European Commission, British Heart Foundation, Commission of the European Communities, European Research Council Horizon 2020, Innovative Medicines Initiative, Boehringer Ingelheim Ltd, Medical Research Council (MRC), Roberts, Michael [0000-0002-3484-5031], Selby, Ian Andrew [0000-0003-4244-8893], and Apollo - University of Cambridge Repository
- Subjects
FOS: Computer and information sciences ,COLOR NORMALIZATION ,Computer Science - Artificial Intelligence ,domain adaptation ,Computer Vision and Pattern Recognition (cs.CV) ,IMAGES ,SEGMENTATION ,Computer Science - Computer Vision and Pattern Recognition ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,All institutes and research themes of the Radboud University Medical Center ,0302 clinical medicine ,0801 Artificial Intelligence and Image Processing ,information fusion ,Artificial Intelligence & Image Processing ,reproducibility ,RADIOMIC FEATURES ,cs.CV ,GENE-EXPRESSION ,DIFFUSION MRI DATA ,cs.AI ,UNWANTED VARIATION ,3. Good health ,SCANNER ,Artificial Intelligence (cs.AI) ,data standardisation ,Hardware and Architecture ,Signal Processing ,Inflammatory diseases Radboud Institute for Health Sciences [Radboudumc 5] ,COEFFICIENT ,Information fusion ,data harmonisation ,030217 neurology & neurosurgery ,Software ,Information Systems - Abstract
This study was supported in part by the European Research Council Innovative Medicines Initiative (DRAGON#, H2020-JTI-IMI2 101005122), the AI for Health Imaging Award (CHAIMELEON##, H2020-SC1-FA-DTS-2019-1 952172), the UK Research and Innovation Future Leaders Fellowship (MR/V023799/1), the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), the SABRE project supported by Boehringer Ingelheim Ltd, the European Union's Horizon 2020 research and innovation programme (ICOVID, 101016131), the Euskampus Foundation (COVID19 Resilience, Ref. COnfVID19), and the Basque Government (consolidated research group MATHMODE, Ref. IT1294-19, and 3KIA project from the ELKARTEK funding program, Ref. KK-2020/00049)., Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research., European Research Council Innovative Medicines Initiative H2020-JTI-IMI2 101005122, AI for Health Imaging Award H2020-SC1-FA-DTS-2019-1 952172, UK Research & Innovation (UKRI) MR/V023799/1, British Heart Foundation TG/18/5/34111 PG/16/78/32402, Boehringer Ingelheim, European Commission 101016131, Euskampus Foundation COnfVID19, Basque Government IT1294-19, Basque Government (3KIA project from the ELKARTEK funding program) KK-2020/00049
- Published
- 2022