Big Data in multiscale modelling: from medical image processing to personalized models

The healthcare industry is different from other industries–patient data are sensitive, their storage needs to be handled with care and in compliance with regulative, while prediction accuracy needs to be high. This fast expansion in medical image modalities and data collection leads to generation of...

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Vydáno v:Journal of big data Ročník 10; číslo 1; s. 72 - 22
Hlavní autoři: Geroski, Tijana, Jakovljević, Djordje, Filipović, Nenad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.12.2023
Springer Nature B.V
SpringerOpen
Témata:
3D reconstruction
Artificial neural networks
Big Data
Carotid arteries
Communications Engineering
Computational Science and Engineering
Computer Science
Customization
Data analysis
Data collection
Data Mining and Knowledge Discovery
Data processing
Database Management
Drug testing
Drugs
Geometry
Health care
Health care industry
Health services
Image processing
Image reconstruction
Incentives
Information Storage and Retrieval
Literature reviews
Mathematical Applications in Computer Science
Mathematical models
Medical image processing
Medical imaging
Multiscale modelling
Networks
Neural networks
R&D
Research & development
Storage
Tests
Three dimensional models
Ultrasonic imaging
Workflow
Medical image processing
Big Data
3D reconstruction
Multiscale modelling
ISSN:2196-1115, 2196-1115
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Popis
Shrnutí:The healthcare industry is different from other industries–patient data are sensitive, their storage needs to be handled with care and in compliance with regulative, while prediction accuracy needs to be high. This fast expansion in medical image modalities and data collection leads to generation of so called “Big Data” which is time-consuming to be analyzed by medical experts. This paper provides an insight into the Big Data from the aspect of its role in multiscale modelling. Special attention is paid to the workflow, starting from medical image processing all the way to creation of personalized models and their analysis. A review of literature regarding Big Data in healthcare is provided and two proposed solutions are described–carotid artery ultrasound image processing and 3D reconstruction, and drug testing on personalized heart models. Related to the carotid artery ultrasound image processing, the starting point is ultrasound images, which are segmented using convolutional neural network U-net, while segmented masks were further used in 3D reconstruction of geometry. Related to the drug testing on personalized heart model, similar approach was proposed, images were used in creation of personalized 3D geometrical model that is used in computational modelling to determine pressure in the left ventricle before and after drug testing. All the aforementioned methodologies are complex, include Big Data analysis and should be performed using servers or high-performance computing. Future development of Big Data applications in healthcare domains offers a lot of potential due to new data standards, rapid development of research and technology, as well as strong government incentives.
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ISSN:2196-1115
2196-1115
DOI:10.1186/s40537-023-00763-y