Riazi Esfahani Parsa, Reddy Akshay J, Nawathey Neel, Ghauri Muhammad S, Min Mildred, Wagh Himanshu, Tak Nathaniel, Patel Rakesh
Medicine, California University of Science and Medicine, Colton, USA.
Ophthalmology, California Northstate University, Rancho Cordova, USA.
Cureus. 2023 Jul 9;15(7):e41615. doi: 10.7759/cureus.41615. eCollection 2023 Jul.
Background Age-related macular degeneration (AMD), diabetic retinopathy (DR), drusen, choroidal neovascularization (CNV), and diabetic macular edema (DME) are significant causes of visual impairment globally. Optical coherence tomography (OCT) imaging has emerged as a valuable diagnostic tool for these ocular conditions. However, subjective interpretation and inter-observer variability highlight the need for standardized diagnostic approaches. Methods This study aimed to develop a robust deep learning model using artificial intelligence (AI) techniques for the automated detection of drusen, CNV, and DME in OCT images. A diverse dataset of 1,528 OCT images from Kaggle.com was used for model training. The performance metrics, including precision, recall, sensitivity, specificity, F1 score, and overall accuracy, were assessed to evaluate the model's effectiveness. Results The developed model achieved high precision (0.99), recall (0.962), sensitivity (0.985), specificity (0.987), F1 score (0.971), and overall accuracy (0.987) in classifying diseased and healthy OCT images. These results demonstrate the efficacy and efficiency of the model in distinguishing between retinal pathologies. Conclusion The study concludes that the developed deep learning model using AI techniques is highly effective in the automated detection of drusen, CNV, and DME in OCT images. Further validation studies and research efforts are necessary to evaluate the generalizability and integration of the model into clinical practice. Collaboration between clinicians, policymakers, and researchers is essential for advancing diagnostic tools and management strategies for AMD and DR. Integrating this technology into clinical workflows can positively impact patient care, particularly in settings with limited access to ophthalmologists. Future research should focus on collecting independent datasets, addressing potential biases, and assessing real-world effectiveness. Overall, the use of machine learning algorithms in conjunction with OCT imaging holds great potential for improving the detection and management of drusen, CNV, and DME, leading to enhanced patient outcomes and vision preservation.
年龄相关性黄斑变性（AMD）、糖尿病视网膜病变（DR）、玻璃膜疣、脉络膜新生血管（CNV）和糖尿病性黄斑水肿（DME）是全球视力损害的重要原因。光学相干断层扫描（OCT）成像已成为诊断这些眼部疾病的重要工具。然而，主观解读和观察者间的差异凸显了标准化诊断方法的必要性。方法：本研究旨在利用人工智能（AI）技术开发一种强大的深度学习模型，用于自动检测OCT图像中的玻璃膜疣、CNV和DME。使用来自Kaggle.com的1528张OCT图像的多样化数据集进行模型训练。评估包括精度、召回率、灵敏度、特异性、F1分数和总体准确率在内的性能指标，以评估模型的有效性。结果：所开发的模型在对患病和健康的OCT图像进行分类时，达到了高精度（0.99）、召回率（0.962）、灵敏度（0.985）、特异性（0.987）、F1分数（0.971）和总体准确率（0.987）。这些结果证明了该模型在区分视网膜病变方面的有效性和效率。结论：该研究得出结论，所开发的使用AI技术的深度学习模型在自动检测OCT图像中的玻璃膜疣、CNV和DME方面非常有效。需要进一步的验证研究和研究工作来评估该模型的通用性以及将其整合到临床实践中的情况。临床医生、政策制定者和研究人员之间的合作对于推进AMD和DR的诊断工具和管理策略至关重要。将这项技术整合到临床工作流程中可以对患者护理产生积极影响，特别是在眼科医生资源有限的环境中。未来的研究应侧重于收集独立数据集、解决潜在偏差以及评估实际效果。总体而言，将机器学习算法与OCT成像结合使用在改善玻璃膜疣、CNV和DME的检测和管理方面具有巨大潜力，从而提高患者的治疗效果并保护视力。
