Eyes-defy-anemia

Anemia is a condition in which the oxygen-carrying capacity of red blood cells is insufficient to meet the body's physiological needs and affects billions of people worldwide. An early diagnosis of this disease could prevent the advancement of other disorders. Currently, traditional methods used to detect anemia consist of venipuncture, which requires a patient to frequently visit laboratories. Therefore, anemia diagnosis using noninvasive and cost effective methods is an open challenge. The pallor of the fingertips, palms, nail beds, and eye conjunctiva can be observed to establish whether a patient suffers from anemia. In response to this challenge, we studied several algorithm devoted to anemia detection. We also collected a new dataset that contain eye conjunctiva photos of Indian and Italian patients, made available to the Scientific Community on IEEE Dataport.Photos were captured by a smartphone equipped with a suitable device, whose details were previously presented in the literature by our team. Using photos of the eye, the palpebral or whole (palpebral and forniceal) conjunctiva were manually segmented. The dataset Eyes-defy-anemia contains 218 images of eyes, in particular conjunctivas, which can be used for research on the diagnosis/estimation of anemia based on the pallor of conjunctiva.The same images can be effectively used to study segmentation algorithms of conjunctivas or exposed parts of the sclera and iris. All images of the dataset are accompanied by segmented elements (palpebral, forniceal and palpebral + forniceal conjunctivas) useful both to directly correlate the pallor with the value of Hb, to assess the performance of segmentation algorithms. Each image is accompanied by essential information such as the value of Hb measured in the laboratory, age, and sex of the patient, listed in xlsx files.

贫血是指红细胞携氧能力不足以满足机体生理需求的病症，全球数十亿人群受其影响。早期诊断该疾病可有效阻止其他并发症的进展。当前用于贫血检测的传统方法为静脉穿刺，该方法要求患者频繁前往实验室开展检测。因此，采用非侵入性且低成本的方式进行贫血诊断，是一项亟待解决的开放性挑战。可通过观察指尖、手掌、甲床及眼结膜的苍白程度，判断患者是否罹患贫血。针对这一挑战，我们研究了多款用于贫血检测的算法，并收集了全新的数据集——该数据集包含印度与意大利患者的眼结膜照片，已在IEEE Dataport平台向全球科研社区公开。照片由搭载专用辅助设备的智能手机拍摄，该设备的相关细节已由我们团队在过往学术文献中发表。利用采集到的眼部照片，我们手动分割了睑结膜、穹窿结膜或全结膜（睑结膜与穹窿结膜）。本数据集Eyes-defy-anemia包含218张眼部（尤其结膜区域）图像，可用于基于结膜苍白程度的贫血诊断与评估相关研究。这些图像同样可有效应用于结膜、巩膜暴露区域及虹膜的分割算法研究。数据集内所有图像均附带分割标注元素（睑结膜、穹窿结膜以及睑结膜+穹窿结膜），既可用于直接建立苍白程度与血红蛋白（Hemoglobin，Hb）检测值的关联，也可用于评估分割算法的性能。每张图像均附带患者的实验室血红蛋白检测值、年龄及性别等关键信息，相关数据整理于xlsx格式文件中。

搜集汇总

背景与挑战

背景概述

Eyes-defy-anemia数据集包含218张眼部结膜照片，用于基于结膜苍白度的贫血诊断研究。每张图像都附有手动分割的结膜部分和患者的关键临床信息，适用于贫血诊断算法和结膜分割算法的研究。