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Review
.2023 Mar 23;13(7):1212.
doi: 10.3390/diagnostics13071212.

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

Affiliations
Review

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

Suman Bhakar et al. Diagnostics (Basel)..

Abstract

Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson's Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer's disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches.

Keywords: Alzheimer’s disease; CNN; MSC; Parkinson’s disease; deep learning; diabetic retinopathy; disease severity; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Strategy for inclusion of sources for this study.
Figure 2
Figure 2
Process of optimizing a classification model for disease identification.
Figure 3
Figure 3
Various inputs to Parkinson’s Disease diagnosis.
Figure 4
Figure 4
Coarse–Fine Diabetic Retinopathy Network [64].
Figure 5
Figure 5
Block diagram of GDT-DBN classification for TB infection level identification [4].
Figure 6
Figure 6
Different categories to measure the sound using polysomnography [88].
See this image and copyright information in PMC

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References

    1. Wu D., Gong K., Arru C.D., Homayounieh F., Bizzo B., Buch V., Ren H., Kim K., Neumark N., Xu P., et al. Severity and consolidation quantification of COVID-19 from CT images using deep learning based on hybrid weak labels. IEEE J. Biomed. Health Inform. 2020;24:3529–3538. doi: 10.1109/JBHI.2020.3030224. - DOI - PMC - PubMed
    1. Nguyen H.H., Saarakkala S., Blaschko M.B., Tiulpin A. Semixup: In-and out-of-manifold regularization for deep semi-supervised knee osteoarthritis severity grading from plain radiographs. IEEE Trans. Med. Imaging. 2020;39:4346–4356. doi: 10.1109/TMI.2020.3017007. - DOI - PubMed
    1. Tadesse G.A., Javed H., Thanh N.L.N., Thi H.D.H., Thwaites L., Clifton D.A., Zhu T. Multi-modal diagnosis of infectious diseases in the developing world. IEEE J. Biomed. Health Inform. 2020;24:2131–2141. doi: 10.1109/JBHI.2019.2959839. - DOI - PubMed
    1. Mithra K., Emmanuel W.S. Gaussian model based hybrid technique for infection level identification in TB diagnosis. J. King Saud Univ.-Comput. Inf. Sci. 2021;33:988–998. doi: 10.1016/j.jksuci.2018.07.008. - DOI
    1. Aşuroğlu T., Oğul H. A deep learning approach for sepsis monitoring via severity score estimation. Comput. Methods Programs Biomed. 2021;198:105816. doi: 10.1016/j.cmpb.2020.105816. - DOI - PubMed

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