背景技术在印度,贫血是严重的公共卫生问题,影响超过一半的人口。为了减轻人民的负担,印度政府在贫血MuktBharat计划下采取了贫血诊断和治疗的监测策略。现场护理测试(POCT)设备在无法获得复杂实验室仪器的社区水平上测试血红蛋白中起着关键作用。大多数当前可用的POCT设备本质上是侵入性的,其具有其自身的局限性。一种非侵入性的血红蛋白估计方法将解决侵入性POCT仪器的许多局限性,这将进一步提高人们对血红蛋白检测的可接受性。非侵入性贫血检测应用程序(NiADA)(MonereAIPrivateLimited,加尔各答,西孟加拉邦,印度),非侵入性POCT应用程序,使用人工智能(AI)从下眼睑图像中预测血红蛋白水平。这种实时,即时护理,低成本解决方案使用自定义计算机视觉深度学习算法来确定血液血红蛋白值。方法该研究验证了基于AI的智能手机应用程序NiADA与实验室血红蛋白估算以及广泛使用的即时血红蛋白估算仪器(HemoCueHb301;HemoCueAB,Angelholm,SkaneCounty,瑞典)。这项研究是在印度东部的一家三级医院进行的,共招募了556名参与者。其中包括58名儿科患者,51名孕妇,214名成年女性,和224名成年男性。使用Python进行统计分析(Python软件基金会,威尔明顿,特拉华州,美国)。P值<0.05被认为是显著的。结果成年女性和男性的NiADA与实验室估计的血红蛋白值之间的平均差异分别为-0.29g/dL和-0.89g/dL。孕妇为0.61g/dL,儿科人群为-0.69g/dL。NiADA的一致性限制在成年女性的2.77至-2.18g/dL之间,3.76至-1.96g/dL的成年男性,孕妇1.89至-3.29g/dL,儿科人群为3.28至-2.08g/dL。NiADA应用对实验室估计方法的敏感性和特异性分别为成年女性的75.8%和53.8%,成年男性占70.0%和48.3%,怀孕女性占23.8%和90%,儿科人群的比例分别为75%和57%。结论作为一种非侵入性应用,NiADA的性能令人满意,可与HemoCueHb301和其他POCT设备等微创工具相媲美。
Background Anemia is a severe public health problem in India affecting more than half of the population. To reduce its burden on the population, the Government of India under the Anemia Mukt Bharat program has adopted a monitoring strategy for the diagnosis and treatment of anemia. Point-of-care testing (POCT) devices play a pivotal role in testing hemoglobin at a community level where sophisticated laboratory instruments are not available. The majority of the currently available POCT devices are invasive in nature which have their own limitations. A non-invasive method of hemoglobin estimation will address many of the limitations of an invasive POCT instrument, which will further improve people\'s acceptability for hemoglobin testing. The Non-Invasive Anemia Detection App (NiADA) (Monere AI Private Limited, Kolkata, West Bengal, India), a non-invasive POCT application, uses artificial intelligence (AI) to predict the hemoglobin level from lower eyelid images. This real-time, point-of-care, low-cost solution uses a custom computer vision deep-learning algorithm to determine blood hemoglobin value. Method The study validates an AI-based smartphone application NiADA against laboratory hemoglobin estimation and a widely used point-of-care hemoglobin estimation instrument (HemoCue Hb 301; HemoCue AB, Angelholm, Skane County, Sweden). The study was conducted in a tertiary care hospital in Eastern India and recruited a total of 556 participants. These included 58 pediatric patients, 51 pregnant women, 214 adult females, and 224 adult males. Statistical analysis was performed using Python (Python Software Foundation, Wilmington, Delaware, United States). A p-value of < 0.05 was taken to be significant. Result The mean difference observed between NiADA and laboratory-estimated hemoglobin values came out to be -0.29 g/dL and -0.89 g/dL for adult females and males respectively, and 0.61 g/dL for pregnant women and -0.69g/dL for the pediatric population. The limits of agreement for NiADA were narrow at 2.77 to -2.18 g/dL for adult females, 3.76 to -1.96 g/dL for adult males, 1.89 to -3.29 g/dL for pregnant women, and 3.28 to -2.08 g/dL for the pediatric population. The sensitivity and specificity of the NiADA application against the laboratory estimation method were 75.8% and 53.8% for adult females, 70.0% and 48.3% for adult males, 23.8% and 90% for pregnant females, and 75% and 57% for the pediatric population. Conclusion As a non-invasive application, NiADA\'s performance is satisfactory and comparable with minimally invasive tools like HemoCue Hb 301 and other POCT devices.