Multi-modality approaches for medical support systems: A systematic review of the last decade
Article
Article Title | Multi-modality approaches for medical support systems: A systematic review of the last decade |
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ERA Journal ID | 20983 |
Article Category | Article |
Authors | Salvi, Massimo, Loh, Hui Wen, Seoni, Silvia, Barua, Prabal Datta, García, Salvador, Molinari, Filippo and Acharya, U. Rajendra |
Journal Title | Information Fusion |
Journal Citation | 103 |
Article Number | 102134 |
Number of Pages | 20 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 1566-2535 |
1872-6305 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.inffus.2023.102134 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1566253523004505 |
Abstract | Healthcare traditionally relies on single-modality approaches, which limit the information available for medical decisions. However, advancements in technology and the availability of diverse data sources have made it feasible to integrate multiple modalities and gain a more comprehensive understanding of patients' conditions. Multi-modality approaches involve fusing and analyzing various data types, including medical images, biosignals, clinical records, and other relevant sources. This systematic review provides a comprehensive exploration of the multi-modality approaches in healthcare, with a specific focus on disease diagnosis and prognosis. The adoption of multi-modality approaches in healthcare is crucial for personalized medicine, as it enables a comprehensive profile of each patient, considering their genetic makeup, imaging characteristics, clinical history, and other relevant factors. The review also discusses the technical challenges associated with fusing heterogeneous multimodal data and highlights the emergence of deep learning approaches as a powerful paradigm for multimodal data integration. |
Keywords | Data fusion |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 420311. Health systems |
Byline Affiliations | Polytechnic University of Turin, Italy |
National University of Singapore | |
School of Business | |
University of Technology Sydney | |
Catedrático de Universidad, Spain | |
School of Mathematics, Physics and Computing | |
Centre for Health Research | |
Library Services |
https://research.usq.edu.au/item/z5vqq/multi-modality-approaches-for-medical-support-systems-a-systematic-review-of-the-last-decade
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