Early Prediction of Neonatal Jaundice using Artificial Intelligence Techniques

Kumar, Yogesh; Patel, Niravkumar D.; Koul, Apeksha; Gupta, Anish

doi:10.1109/iciptm54933.2022.9753884

Cited by 16 publications

(2 citation statements)

References 26 publications

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“…There is evidence that the disease investigated by Kumar et al [23] can cause brain injury and the resulting condition of kernicterus, which is marked by involuntary, repetitive movements, an abnormal fixation on the ceiling, and diminished hearing. Therefore, early diagnosis and treatment can reduce or eliminate consequences.…”

Section: Related Workmentioning

confidence: 99%

Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning

Inamanamelluri,

Pulipati,

Pradhan

et al. 2023

RIA

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A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive.

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Section: Related Workmentioning

confidence: 99%

Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning

Inamanamelluri,

Pulipati,

Pradhan

et al. 2023

RIA

View full text Add to dashboard Cite

show abstract

“…This could be achieved by establishing a correlation between skin color and bilirubin concentration by examining the skin's visual properties with a camera and image processing tools [29]. The authors of paper [30] created artificial intelligence-based algorithms for detecting jaundice in babies. In addition to ending their evaluation of several AI approaches, they released a report summarizing their achievements and the challenges they have faced in this industry.…”

Section: An Overview Of the Various Non-invasive Techniques Used For ...mentioning

confidence: 99%

A Non-invasive Methods for Neonatal Jaundice Detection and Monitoring to Assess Bilirubin Level: A Review

Karim¹,

Zaman²,

Yong³

2023

AETiC

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Neonatal jaundice is a frequent cause of substantial illness and mortality in newborns. The newborn infant’s skin, eyes, and other tissues turn yellow because bilirubin contains a pigment or coloring. Jaundice that manifests in the first few days is highly dangerous and typically needs to be treated right away. It is typically “physiologic” when jaundice emerges on the second or third day. Hyperbilirubinemia refers to an abnormally high bilirubin level in the blood. During the decomposition of red blood cells, bilirubin is formed. Bilirubin can build up in the blood, bodily fluids, and other tissues of newborn babies because they are not naturally able to expel it. Kernicterus or irreversible brain damage can result from untreated jaundice if the abnormally high levels of bilirubin are not controlled. In cases of neonatal jaundice, there is currently a variety of estimating methods for measuring bilirubin levels. The goal of this research is to provide a thorough evaluation of various non-invasive frameworks for the identification of newborn jaundice. For this review article, a critical analysis has done by using 51 articles from 2009 to 2022 where all articles are based on the detection of neonatal jaundice. This literary work on non-invasive methods and neonatal jaundice results appear to be an understanding of the avant-garde procedures created and used in this domain. The review also compares and contrasts different non-invasive strategies for predicting an infant’s state of serum bilirubin based on different data such as social media data, and clinical data. At last, the open issues and future challenges of using a non-invasive method to better understand as well as diagnose the neonatal jaundice state of any individual were discussed. From the literature study, usually apparent that the utilization of non-invasive methods in neonatal jaundice has yielded noteworthy fulfillment within the regions of diagnosis, support, research, and clinical governance.

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A Review of Deep Transfer Learning Approaches for Class-Wise Prediction of Alzheimer’s Disease Using MRI Images

Sisodia

Ameta

Kumar

et al. 2023

Arch Computat Methods Eng

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Early Prediction of Neonatal Jaundice using Artificial Intelligence Techniques

Cited by 16 publications

References 26 publications

Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning

Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning

A Non-invasive Methods for Neonatal Jaundice Detection and Monitoring to Assess Bilirubin Level: A Review

A Review of Deep Transfer Learning Approaches for Class-Wise Prediction of Alzheimer’s Disease Using MRI Images

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