Artificial intelligence extension of the OSCAR-IB criteria
Petzold, A., Albrecht, P., Balcer, L. , Bekkers, E., Brandt, A. U., Calabresi, P. A., Deborah, O. G., Graves, J. S., Green, A., Keane, P. A., Nij Bijvank, J. A., Sander, J. W., Paul, F., Saidha, S., Villoslada, P., Wagner, S. K., Yeh, E. A., Aktas, O., Antel, J., Asgari, N., Audo, I., Avasarala, J., Avril, D., Bagnato, F. R., Banwell, B., Bar-Or, A., Behbehani, R., Manterola, A. B., Bennett, J., Benson, L., Bernard, J., Bremond-Gignac, D., Britze, J., Burton, J., Calkwood, J., Carroll, W., Chandratheva, A., Cohen, J., Comi, G., Cordano, C., Costa, S., Costello, F., Courtney, A., Cruz-Herranz, A., Cutter, G., Crabb, D. P. 
ORCID: 0000-0001-8754-3902, Delott, L., De Seze, J., Diem, R., Dollfuss, H., El Ayoubi, N. K., Fasser, C., Finke, C., Fischer, D., Fitzgerald, K., Fonseca, P., Frederiksen, J. L., Frohman, E., Frohman, T., Fujihara, K., Cuellar, I. G., Galetta, S., Garcia-Martin, E., Giovannoni, G., Glebauskiene, B., Suárez, I. G., Jensen, G. P., Hamann, S., Hartung, H. P., Havla, J., Hemmer, B., Huang, S. C., Imitola, J., Jasinskas, V., Jiang, H., Kafieh, R., Kappos, L., Kardon, R., Keegan, D., Kildebeck, E., Kim, U. S., Klistorner, S., Knier, B., Kolbe, S., Korn, T., Krupp, L., Lagrèze, W., Leocani, L., Levin, N., Liskova, P., Preiningerova, J. L., Lorenz, B., May, E., Miller, D., Mikolajczak, J., Saïd, S. M., Montalban, X., Morrow, M., Mowry, E. & Murta, J. (2021).
Artificial intelligence extension of the OSCAR-IB criteria.
Annals of Clinical and Translational Neurology, 8(7),
pp. 1528-1542.
doi: 10.1002/acn3.51320
Abstract
Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.
| Publication Type: | Article |
|---|---|
| Additional Information: | © 2021 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association. DOI: https://doi.org/10.1002/acn3.51320 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
| Subjects: | R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
| Departments: | School of Health & Psychological Sciences > Optometry & Visual Sciences |
| SWORD Depositor: |
Available under License Creative Commons Attribution Non-commercial No Derivatives.
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