NON-BLOOD ANEMIA DETECTION: A SYSTEMATIC REVIEW

Background: Anemia is a serious global health problem that may result in hemorrhage, premature birth, Low Birth Weight, and fetal development disorder. To this day, detection for anemia in midwifery care is still invasive, despite the fact that it has some drawbacks, including the need for blood sampling, expensive cost, the need for skillful health care personnel, and the need for laboratory facility. All these drawbacks make people less interested in undergoing examination. The WHO recommends hemoglobin (Hb) screening using non-invasive methods. Method: This systematic review is based on the PRISMA protocol with searches from the database of Google Scholar, Pubmed, and Science Direct for publications published from 2010 to 2019. The keywords used were: “Early Detection for Anemia”, “Screening for Anemia”, and “Non-Invasive Anemia Detection” with inclusion criteria of publications written in English and Bahasa Indonesia, and those published between 2011 and 2019, which resulted in 16 selected publications.Results: Searches for publications landed 302 published publications, 16 of which meet the criteria. These 16 selected publications consist of 10 publications detecting anemia using fingers, and the remaining six, detect anemia using conjunctiva.  Conclusion: Non-invasive methods are deemed effective in detecting anemia because they are easy to implement, do not require blood sampling, affordable, and do not need skillful health care personnel to administer them, as well as having less possibility for infection. These advantages make non-invasive methods applicable in midwifery care

Semantic segmentation of conjunctiva region for non-invasive anemia detection applications

Technology is changing the future of healthcare, technology-supported non-invasive medical procedures are more preferable in the medical diagnosis. Anemia is one of the widespread diseases affecting the wellbeing of individuals around the world especially childbearing age women and children and addressing this issue with the advanced technology will reduce the prevalence in large numbers. The objective of this work is to perform segmentation of the conjunctiva region for non-invasive anemia detection applications using deep learning. The proposed U-Net Based Conjunctiva Segmentation Model (UNBCSM) uses fine-tuned U-Net architecture for effective semantic segmentation of conjunctiva from the digital eye images captured by consumer-grade cameras in an uncontrolled environment. The ground truth for this supervised learning was given as Pascal masks obtained by manual selection of conjunctiva pixels. Image augmentation and pre-processing was performed to increase the data size and the performance of the model. UNBCSM showed good segmentation results and exhibited a comparable value of Intersection over Union (IoU) score between the ground truth and the segmented mask of 96% and 85.7% for training and validation, respectively

CONVOLUTIONAL NEURAL NETWORK FOR ANEMIA DETECTION BASED ON CONJUNCTIVA PALPEBRAL IMAGES

Anemia is a condition in which the level of hemoglobin in a person's blood is below normal. Hemoglobin concentration is one of the parameters commonly used to determine a person's physical condition. Anemia can attack anyone, especially pregnant women. Currently, many non-invasive anemia detection methods have been developed. One of non-invasive methods in detecting anemia can be seen through its physiological characteristics, namely palpebral conjunctiva images. In this study, conjunctival image-based anemia detection was carried out using one of the deep learning methods, namely Convolutional Neural Netwok (CNN). This CNN method is used with the aim of obtaining more specific characteristics in distinguishing normal and anemic conditions based on the image of the palpebral conjunctiva. The Convolutional Neural Network proposed model in this study consists of five hidden layers, each of which uses a filter size of 3x3, 5x5, 7x7, 9x9, and 11x11 and output channels 16, 32, 64, 128 respectively. Fully connected layer and sigmoid activation function are used to classify normal and anemic conditions. The study was conducted using 2000 images of the palpebral conjunctiva which contained anemia and normal conditions. Furthermore, the dataset is divided into 1,440 images for training, 160 images for validation and 400 images for model testing. The study obtained the best accuracy of 94%, with the average value of precision, recall and f-1 score respectively 0.935; 0.94; 0.935. The results of the study indicate that the system is able to classify normal and anemic conditions with minimal errors. Furthermore, the system that has been designed can be implemented in an Android-based application so that the detection of anemia based on this palpebral conjunctival image can be carried out in real-tim

A non-invasive machine learning mechanism for early disease recognition on Twitter: The case of anemia

Social media sites, such as Twitter, provide the means for users to share their stories, feelings, and health conditions during the disease course. Anemia, the most common type of blood disorder, is recognized as a major public health problem all over the world. Yet very few studies have explored the potential of recognizing anemia from online posts. This study proposed a novel mechanism for recognizing anemia based on the associations between disease symptoms and patients' emotions posted on the Twitter platform. We used k-means and Latent Dirichlet Allocation (LDA) algorithms to group similar tweets and to identify hidden disease topics. Both disease emotions and symptoms were mapped using the Apriori algorithm. The proposed approach was evaluated using a number of classifiers. A higher prediction accuracy of 98.96 % was achieved using Sequential Minimal Optimization (SMO). The results revealed that fear and sadness emotions are dominant among anemic patients. The proposed mechanism is the first of its kind to diagnose anemia using textual information posted on social media sites. It can advance the development of intelligent health monitoring systems and clinical decision-support systems

Smartphone-based photo analysis for the evaluation of anemia, jaundice and COVID-19

Anemia and jaundice are common health conditions that affect millions of children, adults, and the elderly worldwide. Recently, the pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), the virus that leads to COVID-19, has generated an extreme worldwide concern and a huge impact on public health, education, and economy, reaching all spheres of society. The development of techniques for non-invasive diagnosis and the use of mobile health (mHealth) is reaching more and more space. The analysis of a simple photograph by smartphone can allow an assessment of a person's health status. Image analysis techniques have advanced a lot in a short time. Analyses that were previously done manually, can now be done automatically by methods involving artificial intelligence. The use of smartphones, combined with machine learning techniques for image analysis (preprocessing, extraction of characteristics, classification, or regression), capable of providing predictions with high sensitivity and specificity, seems to be a trend. We presented in this review some highlights of the evaluation of anemia, jaundice, and COVID-19 by photo analysis, emphasizing the importance of using the smartphone, machine learning algorithms, and applications that are emerging rapidly. Soon, this will certainly be a reality. Also, these innovative methods will encourage the incorporation of mHealth technologies in telemedicine and the expansion of people's access to health services and early diagnosis

A new, feasible, and convenient method based on semantic segmentation and deep learning for hemoglobin monitoring

ObjectiveNon-invasive methods for hemoglobin (Hb) monitoring can provide additional and relatively precise information between invasive measurements of Hb to help doctors' decision-making. We aimed to develop a new method for Hb monitoring based on mask R-CNN and MobileNetV3 with eye images as input.MethodsSurgical patients from our center were enrolled. After image acquisition and pre-processing, the eye images, the manually selected palpebral conjunctiva, and features extracted, respectively, from the two kinds of images were used as inputs. A combination of feature engineering and regression, solely MobileNetV3, and a combination of mask R-CNN and MobileNetV3 were applied for model development. The model's performance was evaluated using metrics such as R2, explained variance score (EVS), and mean absolute error (MAE).ResultsA total of 1,065 original images were analyzed. The model's performance based on the combination of mask R-CNN and MobileNetV3 using the eye images achieved an R2, EVS, and MAE of 0.503 (95% CI, 0.499–0.507), 0.518 (95% CI, 0.515–0.522) and 1.6 g/dL (95% CI, 1.6–1.6 g/dL), which was similar to that based on MobileNetV3 using the manually selected palpebral conjunctiva images (R2: 0.509, EVS:0.516, MAE:1.6 g/dL).ConclusionWe developed a new and automatic method for Hb monitoring to help medical staffs' decision-making with high efficiency, especially in cases of disaster rescue, casualty transport, and so on

Anemia detection through non-invasive analysis of lip mucosa images

This paper aims to detect anemia using images of the lip mucosa, where the skin tissue is thin, and to confirm the feasibility of detecting anemia noninvasively and in the home environment using machine learning (ML). Data were collected from 138 patients, including 100 women and 38 men. Six ML algorithms: artificial neural network (ANN), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM) which are widely used in medical applications, were used to classify the collected data. Two different data types were obtained from participants' images (RGB red color values and HSV saturation values) as features, with age, sex, and hemoglobin levels utilized to perform classification. The ML algorithm was used to analyze and classify images of the lip mucosa quickly and accurately, potentially increasing the efficiency of anemia screening programs. The accuracy, precision, recall, and F-measure were evaluated to assess how well ML models performed in predicting anemia. The results showed that NB reported the highest accuracy (96%) among the other ML models used. DT, KNN and ANN reported an accuracies of (93%), while LR and SVM had an accuracy of (79%) and (75%) receptively. This research suggests that employing ML approaches to identify anemia will help classify the diagnosis, which will then help to create efficient preventive measures. Compared to blood tests, this noninvasive procedure is more practical and accessible to patients. Furthermore, ML algorithms may be created and trained to assess lip mucosa photos at a minimal cost, making it an affordable screening method in regions with a shortage of healthcare resources

Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks

Anemia is defined as a drop in the number of erythrocytes or hemoglobin concentration below normal levels in healthy people. The increase in paleness of the skin might vary based on the color of the skin, although there is currently no quantifiable measurement. The pallor of the skin is best visible in locations where the cuticle is thin, such as the interior of the mouth, lips, or conjunctiva. This work focuses on anemia-related pallors and their relationship to blood count values and artificial intelligence. In this study, a deep learning approach using transfer learning and Convolutional Neural Networks (CNN) was implemented in which VGG16, Xception, MobileNet, and ResNet50 architectures, were pre-trained to predict anemia using lip mucous images. A total of 138 volunteers (100 women and 38 men) participated in the work to develop the dataset that contains two image classes: healthy and anemic. Image processing was first performed on a single frame with only the mouth area visible, data argumentation was preformed, and then CNN models were applied to classify the dataset lip images. Statistical metrics were employed to discriminate the performance of the models in terms of Accuracy, Precision, Recal, and F1 Score. Among the CNN algorithms used, Xception was found to categorize the lip images with 99.28% accuracy, providing the best results. The other CNN architectures had accuracies of 96.38% for MobileNet, 95.65% for ResNet %, and 92.39% for VGG16. Our findings show that anemia may be diagnosed using deep learning approaches from a single lip image. This data set will be enhanced in the future to allow for real-time classification