Non-Invasive Bilirubin Level Quantification and Jaundice Detection by Sclera Image Processing

Miah, Md. Messal Monem; Tazim, Rafat Jamal; Johora, Fatema Tuj; Imran, Md. Ibrahim Al; Surma, Sanzida Sayedul; Islam, Fariba; Shabab, Rashid; Shahnaz, Celia; Subhana, Arik

doi:10.1109/ghtc46095.2019.9033059

Cited by 12 publications

(9 citation statements)

References 14 publications

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“…With the help of that concept, both patients' time and then physicians are protected. Yet, medical doctors execute additionally assisting among fortune state of affairs as much as possible, explaining in [ 19 ]. The proposed solution to the problem is to assign good yet efficient medical capabilities to patients by connecting and gathering data records via health reputation monitors such as patient's morale rate, blood pressure, and ECG or to send a fortune warning in accordance with the patient's medical doctor's current reputation or complete scientific ID in [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant’s Jaundice Symptoms

Samanta

Karthikeyan²,

Karuppiah

et al. 2021

Journal of Healthcare Engineering

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One of the most important and difficult research fields is newborn jaundice grading. The mitotic count is an important component in determining the severity of newborn jaundice. The use of principal component analysis (PCA) feature selection and an optimal tree strategy classifier to produce automatic mitotic detection in histopathology images and grading is given. This study makes use of real-time and benchmark datasets, as well as specific approaches for detecting jaundice in newborn newborns. According to research, the quality of the feature may have a negative impact on categorization performance. Additionally, compressing the classification method for exclusive main properties can result in a classification performance bottleneck. As a result, identifying appropriate characteristics for training the classifier is required. By combining a feature selection method with a classification model, this is possible. The major outcomes of this study revealed that image processing techniques are critical for predicting neonatal hyperbilirubinemia. Image processing is a method of translating analogue images to digital formats and manipulating them. The primary goal of medical image processing is to collect information useful for disease detection, diagnosis, monitoring, and therapy. Image datasets can be used to validate the performance of newborn jaundice detection. When compared to conventional approaches, it offers results that are accurate, quick, and time efficient. Accuracy, sensitivity, and specificity, which are common performance indicators, were also predictive.

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Section: Related Workmentioning

confidence: 99%

Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant’s Jaundice Symptoms

Samanta

Karthikeyan²,

Karuppiah

et al. 2021

Journal of Healthcare Engineering

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“…During a physical examination, observation of the sclera may reveal primary signs of jaundice, and the evaluation is subject to mistakes by the evaluating clinician. 18 To create a prototype to be used in remote locations with few resources, a non-invasive method has been developed to determine serum bilirubin levels from pictures of the sclera. The more intense the yellowish color of the sclera, the higher the bilirubin level.…”

Section: Jaundicementioning

confidence: 99%

“…The results were attractive; sensitivity ¼ 90% and specificity ¼ 82%, revealing that this method can be a non-invasive alternative to determine bilirubin levels and to allow the development of smartphone applications using this technique. 18 Smartphone apps are being developed to evaluate skin and sclera coloration, aiming to make accurate jaundice predictions. One of these smartphone applications was developed by Padidar et al 19 to process pictures of the skin taken from the forehead of 113 newborns to analyze the RGB components of the images and a calibration card.…”

Section: Jaundicementioning

confidence: 99%

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

Mazzu-Nascimento

Evangelista

Abubakar

et al. 2021

International Journal of Nutrology

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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.

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“…The authors of [30] presented a technique for jaundice detection by processing images of the sclera. A webcam camera and a controllable LED flashlight mounted on the webcam were used to capture the images.…”

Section: Introductionmentioning

confidence: 99%

“…The authors also used RGB, HSV histogram, and a random forest regression algorithm to determine the level of bilirubin. The technique proposed in [30] was able to assess the intensity of jaundice and provide results in a matter of minutes. Furthermore, it did not require exceptionally trained personnel to operate; however, it was only suitable for adults.…”

Section: Introductionmentioning

confidence: 99%

Neonatal Jaundice Detection Using a Computer Vision System

Hashim

Al-Naji

Al-Rayahi

et al. 2021

Designs

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Jaundice or Hyperbilirubinemia is a very common condition that affects newborns in their first few weeks of life. The main cause of jaundice is the high level of the bilirubin substance in the blood. As bilirubin is toxic to brain cells, acute bilirubin encephalopathy can occur in cases of extreme jaundice. This condition can result in brain trauma and lead to kernicterus, which causes repetitive and uncontrolled movements, a permanent upward look, and hearing loss. Thus, a timely diagnosis and treatment can help in preventing long-term damage. In this paper, a developed system based on a digital camera was proposed to diagnose and treat jaundice in newborns. The system detects jaundice and determines if the neonate needs treatment based on the analysis obtained from the real-time captured images. The treatment was achieved by using an Arduino Uno microcontroller to drive phototherapy lighting, which has proven to be an efficient treatment method for jaundice. In addition, the proposed system has the ability to send the diagnostic results to the mobile phone of the care provider. The obtained results from 20 infants inside the intensive care unit showed that the proposed system was accurate in terms of detecting jaundice, easy to implement, and affordable.

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Non-Invasive Bilirubin Level Quantification and Jaundice Detection by Sclera Image Processing

Cited by 12 publications

References 14 publications

Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant’s Jaundice Symptoms

Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant’s Jaundice Symptoms

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

Neonatal Jaundice Detection Using a Computer Vision System

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