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OCT OMNIBUS
- Citation Author(s):
- Submitted by:
- Apurba Nandi
- Last updated:
- Tue, 02/11/2025 - 08:00
- DOI:
- 10.21227/5mrz-7q54
- License:
39 Views- Categories:
- Keywords:
Abstract
The database compiled for this study is a comprehensive and meticulously curated repository designed to evaluate the efficacy of anti-VEGF therapy in patients with Diabetic Macular Edema (DME). It includes clinical and imaging data from 193 diabetic patients, aged 18-70 years, who participated in a single-center, randomized, parallelgroup, double-masked clinical trial. The database encompasses detailed demographic and clinical information, such as age, gender, medical history, duration of diabetes, and baseline measurements like blood pressure and intraocular pressure. Pre-treatment data acquisition includes high-resolution Optical Coherence Tomography (OCT) images obtained using the Heidelberg Spectralis OCT system, providing detailed retinal morphology and macular thickness measurements. Key parameters such as Central Macular Thickness (CMT), baseline Best Corrected Visual Acuity (BCVA), plasma VEGFR-2 concentrations, and the duration of disease were recorded to establish baseline disease severity. Treatment data documents intervention details, including three consecutive monthly doses of Ranibizumab followed by laser photocoagulation, and control group data involving sham injections and laser therapy. Post-treatment data highlights visual acuity improvements, reduction in CMT, responder classification based on BCVA and CMT thresholds, and adverse event monitoring. The OCT imaging protocol followed a 20°×20° volume scan with 6 µm axial resolution over a 6×6 mm macular region, with derived numerical measurements for analysis. Data was processed and used to train a hybrid AI model using LSTM networks for clinical parameters, to predict treatment outcomes. The database adheres to strict anonymization and encryption standards to ensure data security and patient privacy. Its applications include research and development of predictive AI models, clinical decision support for personalized DME therapy, and education in AI-based ophthalmic analysis. This database serves as a robust foundation for advancing precision medicine and improving patient outcomes in retinal disease management.
The database compiled for this study is a comprehensive and meticulously curated repository designed to evaluate the efficacy of anti-VEGF therapy in patients with Diabetic Macular Edema (DME). It includes clinical and imaging data from 193 diabetic patients, aged 18-70 years, who participated in a single-center, randomized, parallelgroup, double-masked clinical trial. The database encompasses detailed demographic and clinical information, such as age, gender, medical history, duration of diabetes, and baseline measurements like blood pressure and intraocular pressure. Pre-treatment data acquisition includes high-resolution Optical Coherence Tomography (OCT) images obtained using the Heidelberg Spectralis OCT system, providing detailed retinal morphology and macular thickness measurements. Key parameters such as Central Macular Thickness (CMT), baseline Best Corrected Visual Acuity (BCVA), plasma VEGFR-2 concentrations, and the duration of disease were recorded to establish baseline disease severity. Treatment data documents intervention details, including three consecutive monthly doses of Ranibizumab followed by laser photocoagulation, and control group data involving sham injections and laser therapy. Post-treatment data highlights visual acuity improvements, reduction in CMT, responder classification based on BCVA and CMT thresholds, and adverse event monitoring. The OCT imaging protocol followed a 20°×20° volume scan with 6 µm axial resolution over a 6×6 mm macular region, with derived numerical measurements for analysis. Data was processed and used to train a hybrid AI model using LSTM networks for clinical parameters, to predict treatment outcomes. The database adheres to strict anonymization and encryption standards to ensure data security and patient privacy. Its applications include research and development of predictive AI models, clinical decision support for personalized DME therapy, and education in AI-based ophthalmic analysis. This database serves as a robust foundation for advancing precision medicine and improving patient outcomes in retinal disease management.
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