Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Translational Vision Science & Technology Pub Date : 2025-01-02 DOI:10.1167/tvst.14.1.20

Rehana Khan, Vinod Maseedupally, Kaveri A Thakoor, Rajiv Raman, Maitreyee Roy

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引用次数: 0

Abstract

Purpose: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.

Methods: The dataset included 2265 participants aged 40 years and above from a population-based study in South India. The dataset included ocular and systemic clinical parameters, dilated retinal fundus images, and hematological data such as complete blood counts and Hb concentration levels. Eighty percent of the dataset was used for algorithm development and 20% for validation. A deep-convolutional neural network, utilizing VGG16, ResNet50, and InceptionV3 architectures, was trained to predict anemia and estimate Hb levels. Sensitivity, specificity, and accuracy were calculated, and receiver operating characteristic (ROC) curves were generated for comparison with clinical anemia data. GradCAM saliency maps highlighted regions linked to anemia and image processing techniques to quantify anemia-related features.

Results: For predicting anemia, the InceptionV3 model demonstrated the best performance, achieving 98% accuracy, 99% sensitivity, 97% specificity, and an area under the curve (AUC) of 0.98 (95% confidence interval [CI] = 0.97-0.99). For estimating Hb levels, the mean absolute error for the InceptionV3 model was 0.58 g/dL (95% CI = 0.57-0.59 g/dL). The model focused on the area around the optic disc and the neighboring retinal vessels, revealing that anemic subjects exhibited significantly increased vessel tortuosity and reduced vessel density (P < 0.001), with variable effects on vessel thickness.

Conclusions: The InceptionV3 model accurately predicted anemia and Hb levels, highlighting the potential of deep learning and vessel analysis for noninvasive anemia detection.

Translational relevance: The proposed method offers the possibility to quantitatively predict hematological parameters in a noninvasive manner.

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基于深度学习的视网膜图像无创贫血检测和血红蛋白估计：一种资源有限的可扩展解决方案。

目的：本研究的目的是开发和验证一种深度学习模型，用于无创贫血检测、血红蛋白（Hb）水平估计以及利用眼底图像识别与贫血相关的视网膜特征。方法：数据集包括2265名年龄在40岁及以上的参与者，他们来自印度南部的一项基于人口的研究。该数据集包括眼部和全身临床参数、扩大的视网膜眼底图像和血液学数据，如全血细胞计数和Hb浓度水平。80%的数据集用于算法开发，20%用于验证。利用VGG16、ResNet50和InceptionV3架构的深度卷积神经网络被训练来预测贫血和估计Hb水平。计算敏感性、特异性和准确性，并生成受试者工作特征（ROC）曲线，与临床贫血资料进行比较。GradCAM显著性地图突出了与贫血相关的区域，并使用图像处理技术量化贫血相关特征。结果：在预测贫血方面，InceptionV3模型表现最佳，准确率为98%，灵敏度为99%，特异性为97%，曲线下面积（AUC）为0.98（95%置信区间[CI] = 0.97-0.99）。对于估计Hb水平，InceptionV3模型的平均绝对误差为0.58 g/dL （95% CI = 0.57-0.59 g/dL）。该模型聚焦于视盘周围区域和邻近的视网膜血管，结果显示，贫血受试者的血管弯曲度显著增加，血管密度显著降低（P < 0.001），血管厚度也有不同的影响。结论：InceptionV3模型准确预测贫血和Hb水平，突出了深度学习和血管分析在无创贫血检测中的潜力。翻译相关性：提出的方法提供了以无创方式定量预测血液学参数的可能性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational Vision Science & Technology Engineering-Biomedical Engineering

CiteScore

5.70

自引率

3.30%

发文量

346

审稿时长

25 weeks

期刊介绍： Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO. The journal covers a broad spectrum of work, including but not limited to: Applications of stem cell technology for regenerative medicine, Development of new animal models of human diseases, Tissue bioengineering, Chemical engineering to improve virus-based gene delivery, Nanotechnology for drug delivery, Design and synthesis of artificial extracellular matrices, Development of a true microsurgical operating environment, Refining data analysis algorithms to improve in vivo imaging technology, Results of Phase 1 clinical trials, Reverse translational ("bedside to bench") research. TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.