Article ; Online: Machine Learning Techniques in Clinical Vision Sciences.
2017 Volume 42, Issue 1, Page(s) 1–15
Abstract: This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and ... ...
| Abstract | This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment. |
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| MeSH term(s) | Algorithms ; Diagnostic Techniques, Ophthalmological ; Eye Diseases/diagnosis ; Eye Diseases/physiopathology ; Humans ; Machine Learning ; Monitoring, Physiologic ; Multimodal Imaging ; Sensitivity and Specificity ; Vision, Ocular/physiology |
| Language | English |
| Publishing date | 2017-01 |
| Publishing country | England |
| Document type | Journal Article ; Review |
| ZDB-ID | 82079-9 |
| ISSN | 1460-2202 ; 0271-3683 |
| ISSN (online) | 1460-2202 |
| ISSN | 0271-3683 |
| DOI | 10.1080/02713683.2016.1175019 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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