Development of a Semaphore of Anemia: Screening Method Based on Photographic Images of the Ungueal Bed Using a Digital Camera

Hermoza, Luis Alejandro Viteri; Cruz, Javier de la; Fernández, Elsa; Castañeda, Benjamín

doi:10.1109/embc44109.2020.9176017

Cited by 2 publications

(1 citation statement)

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“…The research performed three tasks to examine the model's ability to detect patients with varying anemia degrees. Two comparisons were made to evaluate the prediction model: the first comparison was between our model and Collings's model ( 23 ) and Hermoza's model ( 32 ). Collings's prediction was based on the conjunctiva and showed 76% accuracy in predicting anemia.…”

Section: Discussionmentioning

confidence: 99%

Prediction of anemia using facial images and deep learning technology in the emergency department

Zhang

Lou²,

Pan³

et al. 2022

Front. Public Health

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BackgroundAccording 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.MethodWe 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.ResultsThree 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%.ConclusionThe 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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Section: Discussionmentioning

confidence: 99%