{"title":"Rapid reagent-free anaemia screening using plant-derived “HemoQR” paper-strips and smartphone: A study on 200 human subjects","authors":"Suman Chakraborty , Sarbartha Chakraborty , Akash Bajaj , Hitesh Gupta , Mahendra Dashora , Sambit Ghosh , Sonal V. Chaukade , Rajesh Kumar Sagar , Sohom Banerjee","doi":"10.1016/j.indcrop.2024.119914","DOIUrl":null,"url":null,"abstract":"<div><div>Detecting anaemia in resource-limited settings is challenging due to limited access to diagnostic tools, healthcare infrastructure, and trained personnel, often resulting in underdiagnosis and delayed treatment. This study aims to evaluate HemoQR, a novel point-of-care (POC) diagnostic method for haemoglobin (Hb) level detection using a smartphone-based system integrated with a paper strip without needing any reagent or additional auxiliary device. The goal is to assess the efficacy of this portable, user-friendly, rapid and accurate alternative for Hb measurement in large-scale intervention programmes such as Anaemia-mukt Bharat (AMB), which is crucial for diagnosing anaemia and monitoring various health conditions. The method involves the transfer of one drop of the blood sample to a paper strip that is made indigenously using plant based cellulosic sources for optimal functionality. The colour signal around the test spot is quantitatively analysed using a smartphone camera equipped with a custom application. The application processes the images through a machine learning algorithm to determine the Hb concentration. The system was evaluated in a clinical trial involving 200 participants, comparing its performance with the gold-standard laboratory Hb assay using automated haematology analyser. The smartphone-based method demonstrated high sensitivity (87.09 %) and specificity (96.11 %) for anaemia screening, as compared with laboratory results. The average detection time was significantly reduced to under q minute, and the system showed excellent user-friendliness and portability with no auxiliary device needed beyond the smartphone. Clinical trial results indicated that the device was accurate and reliable across different Hb levels. Summarily, HemoQR evidenced to be a promising tool for POC diagnostics. Its high accuracy, rapid results, and ease of use make it suitable for both clinical settings and remote areas with limited access to laboratory facilities. Future research will prioritize further validation and integration of the anaemia screening outcome with electronic health records from a large population cohort. This will help identify early indicators and signals of numerous emerging disease burdens that could escalate into secondary and tertiary health crises if not addressed in time.</div></div>","PeriodicalId":13581,"journal":{"name":"Industrial Crops and Products","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industrial Crops and Products","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926669024018910","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Detecting anaemia in resource-limited settings is challenging due to limited access to diagnostic tools, healthcare infrastructure, and trained personnel, often resulting in underdiagnosis and delayed treatment. This study aims to evaluate HemoQR, a novel point-of-care (POC) diagnostic method for haemoglobin (Hb) level detection using a smartphone-based system integrated with a paper strip without needing any reagent or additional auxiliary device. The goal is to assess the efficacy of this portable, user-friendly, rapid and accurate alternative for Hb measurement in large-scale intervention programmes such as Anaemia-mukt Bharat (AMB), which is crucial for diagnosing anaemia and monitoring various health conditions. The method involves the transfer of one drop of the blood sample to a paper strip that is made indigenously using plant based cellulosic sources for optimal functionality. The colour signal around the test spot is quantitatively analysed using a smartphone camera equipped with a custom application. The application processes the images through a machine learning algorithm to determine the Hb concentration. The system was evaluated in a clinical trial involving 200 participants, comparing its performance with the gold-standard laboratory Hb assay using automated haematology analyser. The smartphone-based method demonstrated high sensitivity (87.09 %) and specificity (96.11 %) for anaemia screening, as compared with laboratory results. The average detection time was significantly reduced to under q minute, and the system showed excellent user-friendliness and portability with no auxiliary device needed beyond the smartphone. Clinical trial results indicated that the device was accurate and reliable across different Hb levels. Summarily, HemoQR evidenced to be a promising tool for POC diagnostics. Its high accuracy, rapid results, and ease of use make it suitable for both clinical settings and remote areas with limited access to laboratory facilities. Future research will prioritize further validation and integration of the anaemia screening outcome with electronic health records from a large population cohort. This will help identify early indicators and signals of numerous emerging disease burdens that could escalate into secondary and tertiary health crises if not addressed in time.
期刊介绍:
Industrial Crops and Products is an International Journal publishing academic and industrial research on industrial (defined as non-food/non-feed) crops and products. Papers concern both crop-oriented and bio-based materials from crops-oriented research, and should be of interest to an international audience, hypothesis driven, and where comparisons are made statistics performed.