On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images
Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/4/407 |
| _version_ | 1797606365077700608 |
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| author | Prakash Kumar Karn Waleed H. Abdulla |
| author_facet | Prakash Kumar Karn Waleed H. Abdulla |
| author_sort | Prakash Kumar Karn |
| collection | DOAJ |
| description | Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming and heavily dependent on the personal experience of the analyst. This paper focuses on using machine learning to analyse OCT images in the clinical interpretation of retinal diseases. The complexity of understanding the biomarkers present in OCT images has been a challenge for many researchers, particularly those from nonclinical disciplines. This paper aims to provide an overview of the current state-of-the-art OCT image processing techniques, including image denoising and layer segmentation. It also highlights the potential of machine learning algorithms to automate the analysis of OCT images, reducing time consumption and improving diagnostic accuracy. Using machine learning in OCT image analysis can mitigate the limitations of manual analysis methods and provide a more reliable and objective approach to diagnosing retinal diseases. This paper will be of interest to ophthalmologists, researchers, and data scientists working in the field of retinal disease diagnosis and machine learning. By presenting the latest advancements in OCT image analysis using machine learning, this paper will contribute to the ongoing efforts to improve the diagnostic accuracy of retinal diseases. |
| first_indexed | 2024-03-11T05:14:10Z |
| format | Article |
| id | doaj.art-8eca4b55e4a44661bccb3e65e2415189 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-11T05:14:10Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-8eca4b55e4a44661bccb3e65e24151892023-11-17T18:21:40ZengMDPI AGBioengineering2306-53542023-03-0110440710.3390/bioengineering10040407On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT ImagesPrakash Kumar Karn0Waleed H. Abdulla1Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland 1010, New ZealandDepartment of Electrical, Computer and Software Engineering, University of Auckland, Auckland 1010, New ZealandOptical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming and heavily dependent on the personal experience of the analyst. This paper focuses on using machine learning to analyse OCT images in the clinical interpretation of retinal diseases. The complexity of understanding the biomarkers present in OCT images has been a challenge for many researchers, particularly those from nonclinical disciplines. This paper aims to provide an overview of the current state-of-the-art OCT image processing techniques, including image denoising and layer segmentation. It also highlights the potential of machine learning algorithms to automate the analysis of OCT images, reducing time consumption and improving diagnostic accuracy. Using machine learning in OCT image analysis can mitigate the limitations of manual analysis methods and provide a more reliable and objective approach to diagnosing retinal diseases. This paper will be of interest to ophthalmologists, researchers, and data scientists working in the field of retinal disease diagnosis and machine learning. By presenting the latest advancements in OCT image analysis using machine learning, this paper will contribute to the ongoing efforts to improve the diagnostic accuracy of retinal diseases.https://www.mdpi.com/2306-5354/10/4/407OCTfundusmachine learningdeep learning |
| spellingShingle | Prakash Kumar Karn Waleed H. Abdulla On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images Bioengineering OCT fundus machine learning deep learning |
| title | On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images |
| title_full | On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images |
| title_fullStr | On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images |
| title_full_unstemmed | On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images |
| title_short | On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images |
| title_sort | on machine learning in clinical interpretation of retinal diseases using oct images |
| topic | OCT fundus machine learning deep learning |
| url | https://www.mdpi.com/2306-5354/10/4/407 |
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