ArticleFrontiers in public health2022
Prediction of anemia using facial images and deep learning technology in the emergency department.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 36 citations in OpenAlex.
- Chest-X-ray based deep feature-driven framework for anemia detection insights from a large hospital cohort.BioData mining · 2026Article
- Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology.Scientific reports · 2026Article
- AI-assisted haematology: machine learning-based prediction of iron-deficiency anaemia from reticulocyte maturation indices.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Estimation of Total Hemoglobin (SpHb) from Facial Videos Using 3D Convolutional Neural Network-Based Regression.Biosensors · 2025Article
- Deep Learning-Based Model for Non-invasive Hemoglobin Estimation via Body Parts Images: A Retrospective Analysis and a Prospective Emergency Department Study.Journal of imaging informatics in medicine · 2025Article
- AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.Patterns (New York, N.Y.) · 2025Review
- Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes.Scientific reports · 2025Article
- Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context.BMC public health · 2024Article
- Multidisciplinary approaches to study anaemia with special mention on aplastic anaemia (Review).International journal of molecular medicine · 2024Review
- Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms.Visual computing for industry, biomedicine, and art · 2024Article
- Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging.BMC medical informatics and decision making · 2024Article
- Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions.Archives of academic emergency medicine · 2024Review
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: According to the WHO, anemia is a highly prevalent disease, especially for patients in the emergency department. The pathophysiological mechanism by which anemia can affect facial characteristics, such as membrane pallor, has been proven to detect anemia with the help of deep learning technology. The quick prediction method for the patient in the emergency department is important to screen the anemic state and judge the necessity of blood transfusion treatment. Method: We trained a deep learning system to predict anemia using videos of 316 patients. All the videos were taken with the same portable pad in the ambient environment of the emergency department. The video extraction and face recognition methods were used to highlight the facial area for analysis. Accuracy and area under the curve were used to assess the performance of the machine learning system at the image level and the patient level. Results: Three tasks were applied for performance evaluation. The objective of Task 1 was to predict patients' anemic states [hemoglobin (Hb) <13 g/dl in men and Hb <12 g/dl in women]. The accuracy of the image level was 82.37%, the area under the curve (AUC) of the image level was 0.84, the accuracy of the patient level was 84.02%, the sensitivity of the patient level was 92.59%, and the specificity of the patient level was 69.23%. The objective of Task 2 was to predict mild anemia (Hb <9 g/dl). The accuracy of the image level was 68.37%, the AUC of the image level was 0.69, the accuracy of the patient level was 70.58%, the sensitivity was 73.52%, and the specificity was 67.64%. The aim of task 3 was to predict severe anemia (Hb <7 g/dl). The accuracy of the image level was 74.01%, the AUC of the image level was 0.82, the accuracy of the patient level was 68.42%, the sensitivity was 61.53%, and the specificity was 83.33%. Conclusion: The machine learning system could quickly and accurately predict the anemia of patients in the emergency department and aid in the treatment decision for urgent blood transfusion. It offers great clinical value and practical significance in expediting diagnosis, improving medical resource allocation, and providing appropriate treatment in the future.
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