دورية أكاديمية
Automated Identification and Segmentation of Ellipsoid Zone At-Risk Using Deep Learning on SD-OCT for Predicting Progression in Dry AMD
| العنوان: | Automated Identification and Segmentation of Ellipsoid Zone At-Risk Using Deep Learning on SD-OCT for Predicting Progression in Dry AMD |
|---|---|
| المؤلفون: | Gagan Kalra, Hasan Cetin, Jon Whitney, Sari Yordi, Yavuz Cakir, Conor McConville, Victoria Whitmore, Michelle Bonnay, Jamie L. Reese, Sunil K. Srivastava, Justis P. Ehlers |
| المصدر: | Diagnostics, Vol 13, Iss 6, p 1178 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Medicine (General) |
| مصطلحات موضوعية: | ellipsoid zone integrity, photoreceptor damage, age-related macular degeneration, automated feature segmentation, deep learning, quantitative optical coherence tomography, Medicine (General), R5-920 |
| الوصف: | Background: The development and testing of a deep learning (DL)-based approach for detection and measurement of regions of Ellipsoid Zone (EZ) At-Risk to study progression in nonexudative age-related macular degeneration (AMD). Methods: Used in DL model training and testing were 341 subjects with nonexudative AMD with or without geographic atrophy (GA). An independent dataset of 120 subjects were used for testing model performance for prediction of GA progression. Accuracy, specificity, sensitivity, and intraclass correlation coefficient (ICC) for DL-based EZ At-Risk percentage area measurement was calculated. Random forest-based feature ranking of EZ At-Risk was compared to previously validated quantitative OCT-based biomarkers. Results: The model achieved a detection accuracy of 99% (sensitivity = 99%; specificity = 100%) for EZ At-Risk. Automatic EZ At-Risk measurement achieved an accuracy of 90% (sensitivity = 90%; specificity = 84%) and the ICC compared to ground truth was high (0.83). In the independent dataset, higher baseline mean EZ At-Risk correlated with higher progression to GA at year 5 (p < 0.001). EZ At-Risk was a top ranked feature in the random forest assessment for GA prediction. Conclusions: This report describes a novel high performance DL-based model for the detection and measurement of EZ At-Risk. This biomarker showed promising results in predicting progression in nonexudative AMD patients. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 13061178 2075-4418 |
| Relation: | https://www.mdpi.com/2075-4418/13/6/1178; https://doaj.org/toc/2075-4418 |
| DOI: | 10.3390/diagnostics13061178 |
| URL الوصول: | https://doaj.org/article/9400c4ba42384787a20c3a1550b3fd1a |
| رقم الأكسشن: | edsdoj.9400c4ba42384787a20c3a1550b3fd1a |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 13061178 20754418 |
|---|---|
| DOI: | 10.3390/diagnostics13061178 |