Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review
Article
Jahmunah, V., Koh, Joel En Wei, Sudarshan, Vidya K., Raghavendra, U., Gudigar, Anjan, Oh, Shu Lih, Loh, Hui Wen, Faust, Oliver, Barua, Prabal Datta, Ciaccio, Edward J. and Acharya, U. Rajendra. 2023. "Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review." Biocybernetics and Biomedical Engineering. 43 (1), pp. 82-108. https://doi.org/10.1016/j.bbe.2022.12.002
| Article Title | Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review |
|---|---|
| ERA Journal ID | 211872 |
| Article Category | Article |
| Authors | Jahmunah, V., Koh, Joel En Wei, Sudarshan, Vidya K., Raghavendra, U., Gudigar, Anjan, Oh, Shu Lih, Loh, Hui Wen, Faust, Oliver, Barua, Prabal Datta, Ciaccio, Edward J. and Acharya, U. Rajendra |
| Journal Title | Biocybernetics and Biomedical Engineering |
| Journal Citation | 43 (1), pp. 82-108 |
| Number of Pages | 82-108 |
| Year | 2023 |
| Publisher | Elsevier BV |
| Place of Publication | Netherlands |
| ISSN | 0208-5216 |
| 2391-467X | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bbe.2022.12.002 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0208521622001139 |
| Abstract | Celiac Disease (CD) is a common ailment that affects approximately 1% of the world population. Automated CD detection can help experts during the diagnosis of this condition at an early stage and bring significant benefits to both patients and healthcare providers. For this purpose, scientists have created automatic and semi-automatic CD diagnostic support systems. In this study, we performed information extraction methods that were found useful for efforts to differentiate CD versus non-CD. To focus the review process, only methods for endoscopy, video capsule endoscopy (VCE) and biopsy image analyses were considered. As described herein, we have learned that statistical and non-linear methods are most important for information extraction. These information extraction tools might benefit clinical workflows by reducing intra- and inter-observer variability. However, bias, introduced by resolving design choices during the creation of diagnostic support systems, may limit the general validity of the performance results, impacting the transferability of study outcomes. Therefore, having am overview of information extraction tools. Together with their general and specific limitations, might be assistive in improving the information extraction process. We hope our review results will provide a foundation for the design of next-generation statistical and nonlinear methods that can be used in CD detection systems. We have also compared various review articles and discussed recommendations to improve CD diagnosis. From this review, it is evident that CD diagnosis is slowly moving away from conventional techniques towards advanced deep learning techniques. |
| Keywords | Celiac disease; Nonlinear features; Entropies; Texture; Statistical analysis; Video capsule endoscopy |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Ngee Ann Polytechnic, Singapore |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Manipal Academy of Higher Education, India | |
| Anglia Ruskin University, United Kingdom | |
| School of Management and Enterprise | |
| University of Technology Sydney | |
| Cogninet Australia, Australia | |
| Asia University, Taiwan | |
| Institute for Life Sciences and the Environment | |
| Kumamoto University, Japan |
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