Detection of Disease Features on Retinal OCT Scans Using RETFound
Eye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. Optical coherence tomography (OCT) imaging provides clinicians with in vivo, cross-sectional views of the re...
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MDPI AG
2024-11-01
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| author | Katherine Du Atharv Ramesh Nair Stavan Shah Adarsh Gadari Sharat Chandra Vupparaboina Sandeep Chandra Bollepalli Shan Sutharahan José-Alain Sahel Soumya Jana Jay Chhablani Kiran Kumar Vupparaboina |
| author_facet | Katherine Du Atharv Ramesh Nair Stavan Shah Adarsh Gadari Sharat Chandra Vupparaboina Sandeep Chandra Bollepalli Shan Sutharahan José-Alain Sahel Soumya Jana Jay Chhablani Kiran Kumar Vupparaboina |
| author_sort | Katherine Du |
| collection | DOAJ |
| description | Eye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. Optical coherence tomography (OCT) imaging provides clinicians with in vivo, cross-sectional views of the retina, enabling the identification of key pathological features. However, manual interpretation of OCT scans is labor-intensive and prone to variability, often leading to diagnostic inconsistencies. To address this, we leveraged the RETFound model, a foundation model pretrained on 1.6 million unlabeled retinal OCT images, to automate the classification of key disease signatures on OCT. We finetuned RETFound and compared its performance with the widely used ResNet-50 model, using single-task and multitask modes. The dataset included 1770 labeled B-scans with various disease features, including subretinal fluid (SRF), intraretinal fluid (IRF), drusen, and pigment epithelial detachment (PED). The performance was evaluated using accuracy and AUC-ROC values, which ranged across models from 0.75 to 0.77 and 0.75 to 0.80, respectively. RETFound models display comparable specificity and sensitivity to ResNet-50 models overall, making it also a promising tool for retinal disease diagnosis. These findings suggest that RETFound may offer improved diagnostic accuracy and interpretability for specific tasks, potentially aiding clinicians in more efficient and reliable OCT image analysis. |
| format | Article |
| id | doaj-art-a61af60c897d49eaa82d4fe85315afff |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-a61af60c897d49eaa82d4fe85315afff2024-12-27T14:11:25ZengMDPI AGBioengineering2306-53542024-11-011112118610.3390/bioengineering11121186Detection of Disease Features on Retinal OCT Scans Using RETFoundKatherine Du0Atharv Ramesh Nair1Stavan Shah2Adarsh Gadari3Sharat Chandra Vupparaboina4Sandeep Chandra Bollepalli5Shan Sutharahan6José-Alain Sahel7Soumya Jana8Jay Chhablani9Kiran Kumar Vupparaboina10Department of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USADepartment of Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad 502284, IndiaDepartment of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USADepartment of Computer Science, University of North Carolina at Greensboro, Greensboro, NC 27412, USADepartment of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USADepartment of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USADepartment of Computer Science, University of North Carolina at Greensboro, Greensboro, NC 27412, USADepartment of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USADepartment of Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad 502284, IndiaDepartment of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USADepartment of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USAEye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. Optical coherence tomography (OCT) imaging provides clinicians with in vivo, cross-sectional views of the retina, enabling the identification of key pathological features. However, manual interpretation of OCT scans is labor-intensive and prone to variability, often leading to diagnostic inconsistencies. To address this, we leveraged the RETFound model, a foundation model pretrained on 1.6 million unlabeled retinal OCT images, to automate the classification of key disease signatures on OCT. We finetuned RETFound and compared its performance with the widely used ResNet-50 model, using single-task and multitask modes. The dataset included 1770 labeled B-scans with various disease features, including subretinal fluid (SRF), intraretinal fluid (IRF), drusen, and pigment epithelial detachment (PED). The performance was evaluated using accuracy and AUC-ROC values, which ranged across models from 0.75 to 0.77 and 0.75 to 0.80, respectively. RETFound models display comparable specificity and sensitivity to ResNet-50 models overall, making it also a promising tool for retinal disease diagnosis. These findings suggest that RETFound may offer improved diagnostic accuracy and interpretability for specific tasks, potentially aiding clinicians in more efficient and reliable OCT image analysis.https://www.mdpi.com/2306-5354/11/12/1186retinal imagingoptical coherence tomographymachine learningage-related macular degenerationfoundational modelautomated report generation |
| spellingShingle | Katherine Du Atharv Ramesh Nair Stavan Shah Adarsh Gadari Sharat Chandra Vupparaboina Sandeep Chandra Bollepalli Shan Sutharahan José-Alain Sahel Soumya Jana Jay Chhablani Kiran Kumar Vupparaboina Detection of Disease Features on Retinal OCT Scans Using RETFound Bioengineering retinal imaging optical coherence tomography machine learning age-related macular degeneration foundational model automated report generation |
| title | Detection of Disease Features on Retinal OCT Scans Using RETFound |
| title_full | Detection of Disease Features on Retinal OCT Scans Using RETFound |
| title_fullStr | Detection of Disease Features on Retinal OCT Scans Using RETFound |
| title_full_unstemmed | Detection of Disease Features on Retinal OCT Scans Using RETFound |
| title_short | Detection of Disease Features on Retinal OCT Scans Using RETFound |
| title_sort | detection of disease features on retinal oct scans using retfound |
| topic | retinal imaging optical coherence tomography machine learning age-related macular degeneration foundational model automated report generation |
| url | https://www.mdpi.com/2306-5354/11/12/1186 |
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