Observational studyPloS one2021
Prediction of anemia and estimation of hemoglobin concentration using a smartphone camera.
Observational study in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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The trial behind it
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Who cites it
21 citing papers in PubMed, 60 citations in OpenAlex.
- Development of a Deep Learning Model to Estimate Anemia from Palpebral Conjunctiva Taken with a Portable Slit-Lamp Microscope.Bioengineering (Basel, Switzerland) · 2026Article
- Towards noninvasive blood count using a deep learning pipeline from bulbar conjunctiva videos.NPJ digital medicine · 2026Article
- Physician Gestalt for Anemia Detection in the Emergency Department: A Prospective Study.The western journal of emergency medicine · 2026Observational
- Smartphone-Based Anemia ScreeningCurrent medical imaging · 2025Article
- Estimation of Total Hemoglobin (SpHb) from Facial Videos Using 3D Convolutional Neural Network-Based Regression.Biosensors · 2025Article
- Article
- Clinical Bedside Benchmarking Test for Measuring the Total Hemoglobin Concentration.Healthcare (Basel, Switzerland) · 2025Article
- Article
- Radiomic identification of anemia features in monochromatic conjunctiva photographs in school-age children.Biophotonics discovery · 2025Article
- Non-invasive quantification of total hemoglobin concentrations by visible-light spectroscopic optical coherence tomography: a validation study on healthy volunteers.Biomedical optics express · 2025Article
- Article
- Dataset of human skin and fingernails images for non-invasive haemoglobin level assessment.Scientific data · 2024Article
- Diffuse reflectance spectroscopy and RGB-imaging: a comparative study of non-invasive haemoglobin assessment.Scientific reports · 2024Article
- Article
- "Notification! You May Have Cancer." Could Smartphones and Wearables Help Detect Cancer Early?JMIR cancer · 2024Article
- Prediction of anemia in real-time using a smartphone camera processing conjunctival images.PloS one · 2024Article
- Feasibility of smartphone colorimetry of the face as an anaemia screening tool for infants and young children in Ghana.PloS one · 2023Article
- Anemia detection through non-invasive analysis of lip mucosa images.Frontiers in big data · 2023Article
- Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks.Frontiers in big data · 2023Article
- Deep learning-based fundus image analysis for cardiovascular disease: a review.Therapeutic advances in chronic disease · 2023Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anemia, defined as a low hemoglobin concentration, has a large impact on the health of the world's population. We describe the use of a ubiquitous device, the smartphone, to predict hemoglobin concentration and screen for anemia. This was a prospective convenience sample study conducted in Emergency Department (ED) patients of an academic teaching hospital. In an algorithm derivation phase, images of both conjunctiva were obtained from 142 patients in Phase 1 using a smartphone. A region of interest targeting the palpebral conjunctiva was selected from each image. Image-based parameters were extracted and used in stepwise regression analyses to develop a prediction model of estimated hemoglobin (HBc). In Phase 2, a validation model was constructed using data from 202 new ED patients. The final model based on all 344 patients was tested for accuracy in anemia and transfusion thresholds. Hemoglobin concentration ranged from 4.7 to 19.6 g/dL (mean 12.5). In Phase 1, there was a significant association between HBc and laboratory-predicted hemoglobin (HBl) slope = 1.07 (CI = 0.98-1.15), p<0.001. Accuracy, sensitivity, and specificity of HBc for predicting anemia was 82.9 [79.3, 86.4], 90.7 [87.0, 94.4], and 73.3 [67.1, 79.5], respectively. In Phase 2, accuracy, sensitivity and specificity decreased to 72.6 [71.4, 73.8], 72.8 [71, 74.6], and 72.5 [70.8, 74.1]. Accuracy for low (<7 g/dL) and high (<9 g/dL) transfusion thresholds was 94.4 [93.7, 95] and 86 [85, 86.9] respectively. Error trended with increasing HBl values (slope 0.27 [0.19, 0.36] and intercept -3.14 [-4.21, -2.07] (p<0.001) such that HBc tended to underestimate hemoglobin in higher ranges and overestimate in lower ranges. Higher quality images had a smaller bias trend than lower quality images. When separated by skin tone results were unaffected. A smartphone can be used in screening for anemia and transfusion thresholds. Improvements in image quality and computational corrections can further enhance estimates of hemoglobin.
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