Multimodal Medical Image Fusion as a Novel Approach for Aortic Annulus Sizing

Hamzah, Nur'Aqilah; Omar, Zaid; Hanafi, Marsyita; Izhar, Lila Iznita

doi:10.1007/978-981-10-8405-8_4

Cited by 2 publications

(1 citation statement)

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“…Additional challenges are that such a combination of data can require more sophisticated models (that can be computationally expensive to train) and more complicated data normalization techniques (which includes correction of errors and variations embedded in data from multiple sources) ( 34 ). Such model complexity can come at the cost of model “explainability.” Another issue with data fusion that Hamzah et al point out is that it can be difficult to reconcile data that is acquired in different ways ( 35 ). For example, the quality of ECHO data is highly dependent on the expertise of the sonographer.…”

Section: Data Fusion Considerationsmentioning

confidence: 99%

Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care

Amal

Safarnejad

Omiye

et al. 2022

Front. Cardiovasc. Med.

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Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Sources of data for digital health tools include multiple modalities such as electronic medical records (EMR), radiology images, and genetic repositories, to name a few. While historically, these data were utilized in silos, new machine learning (ML) and deep learning (DL) technologies enable the integration of these data sources to produce multi-modal insights. Data fusion, which integrates data from multiple modalities using ML and DL techniques, has been of growing interest in its application to medicine. In this paper, we review the state-of-the-art research that focuses on how the latest techniques in data fusion are providing scientific and clinical insights specific to the field of cardiovascular medicine. With these new data fusion capabilities, clinicians and researchers alike will advance the diagnosis and treatment of cardiovascular diseases (CVD) to deliver more timely, accurate, and precise patient care.

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Section: Data Fusion Considerationsmentioning

confidence: 99%