Screening for Neonatal Jaundice with a Smartphone

Outlaw, Felix; Meek, Judith; MacDonald, Lindsay W.; Leung, Terence S.

doi:10.1145/3079452.3079488

Cited by 10 publications

(12 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is caused by excessive bilirubin in the blood, which results in yellow pigmentation in the skin, eye, or pale stools of the neonate [6]. A wide range of efforts exist in the literature that analyze images of neonates' eyes, skin, or pale stool to recognize jaundice [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Further, there exist a few efforts that do so using numerical data, such as [34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

“…Rizvi et al [20] used the Diazo method with dichloroaniline (DCA) to estimate bilirubin level using neonates' eyes images. The authors in [19,22] captured two versions for each image, namely with flash and no-flash images. In the latter work, the images are used to apply the ambient subtraction technique, which proved to achieve promising results.…”

Section: Related Workmentioning

confidence: 99%

“…For the former, researchers have used images of a neonate's face [7], forehead [8][9][10][11], sternum [9,10,12,13], abdomen [13,14], or multiple body parts such as sole, palm, forehead, and arm [15], and face, arms, feet, and middle body [16]. For the latter, researchers have used an eye's sclera for diagnosis [17][18][19][20][21][22]. Further, the study in [23] has focused on stool samples.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning

Althnian

Almanea

Aloboud

2021

Sensors

View full text Add to dashboard Cite

Neonatal jaundice is a common condition worldwide. Failure of timely diagnosis and treatment can lead to death or brain injury. Current diagnostic approaches include a painful and time-consuming invasive blood test and non-invasive tests using costly transcutaneous bilirubinometers. Since periodic monitoring is crucial, multiple efforts have been made to develop non-invasive diagnostic tools using a smartphone camera. However, existing works rely either on skin or eye images using statistical or traditional machine learning methods. In this paper, we adopt a deep transfer learning approach based on eye, skin, and fused images. We also trained well-known traditional machine learning models, including multi-layer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF), and compared their performance with that of the transfer learning model. We collected our dataset using a smartphone camera. Moreover, unlike most of the existing contributions, we report accuracy, precision, recall, f-score, and area under the curve (AUC) for all the experiments and analyzed their significance statistically. Our results indicate that the transfer learning model performed the best with skin images, while traditional models achieved the best performance with eyes and fused features. Further, we found that the transfer learning model with skin features performed comparably to the MLP model with eye features.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning

Althnian

Almanea

Aloboud

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Research interest in smartphone-based jaundice screening is growing, and recent work is promising [15][16][17][18][19][20][21][22]. The most extensively validated system is the BiliCam app that estimates total serum bilirubin (TSB) based on the skin colour of the sternum [15].…”

Section: Plos Onementioning

confidence: 99%

Smartphone screening for neonatal jaundice via ambient-subtracted sclera chromaticity

et al. 2020

Self Cite

View full text Add to dashboard Cite

Jaundice is a major cause of mortality and morbidity in the newborn. Globally, early identification and home monitoring are significant challenges in reducing the incidence of jaundicerelated neurological damage. Smartphone cameras are promising as colour-based screening tools as they are low-cost, objective and ubiquitous. We propose a novel smartphone method to screen for neonatal jaundice by imaging the sclera. It does not rely on colour calibration cards or accessories, which may facilitate its adoption at scale and in less economically developed regions. Our approach is to explicitly address three confounding factors in relating colour to jaundice: (1) skin pigmentation, (2) ambient light, and (3) camera spectral response. (1) The variation in skin pigmentation is avoided by imaging the sclera. (2) With the smartphone screen acting as an illuminating flash, a flash/ no-flash image pair is captured using the front-facing camera. The contribution of ambient light is subtracted. (3) In principle, this permits a device-and ambient-independent measure of sclera chromaticity following a one-time calibration. We introduce the concept of Scleral-Conjunctival Bilirubin (SCB), in analogy with Transcutaneous Bilirubin (TcB). The scleral chromaticity is mapped to an SCB value. A pilot study was conducted in the UCL Hospital Neonatal Care Unit (n = 37). Neonates were imaged using a specially developed app concurrently with having a blood test for total serum bilirubin (TSB). The better of two models for SCB based on ambient-subtracted sclera chromaticity achieved r = 0.75 (p<0.01) correlation with TSB. Ambient subtraction improved chromaticity estimates in proof-of-principle laboratory tests and screening performance within our study sample. Using an SCB decision threshold of 190μmol/L, the sensitivity was 100% (specificity 61%) in identifying newborns with TSB>250μmol/L (area under receiver operating characteristic curve, AUROC, 0.86), and 92% (specificity 67%) in identifying newborns with TSB>205μmol/L (AUROC 0.85). These results are comparable to modern transcutaneous bilirubinometers.

show abstract

“…On the other hand, Outlaw et. al., used images of sclera capture through a smartphone application, neoSCB, without the use of a colour calibration card[36].…”

mentioning

confidence: 99%

Development of Jaundice Detection Approaches in Neonates

Bhagat¹,

M²,

Singh³

et al. 2020

JUSST

View full text Add to dashboard Cite

Jaundice is one of the most common diseases that have a significant impact in the first few days of newborn life. Detection and regular monitoring of bilirubin, which is responsible for Jaundice, is an essential phase during the hyperbilirubinemia. In the literature, various clinical assessment methods of Jaundice are available. It motivates us to present a review of these clinical assessment methods in practice, along with their advantages and limitations. In this paper, we have discussed three widely used methods, such as visual assessment, total serum bilirubin and transcutaneous bilirubinometer. From the comparative analysis of these methods, it is concluded that the visual assessment is very subjective in nature, whereas, the total serum bilirubin method is still a gold standard method. The detailed analysis of the methods depicts that the correlation between this two total serum bilirubin and transcutaneous bilirubinometer has enormous potential for improvement resulting in the enhancement in precision and accuracy of bilirubin measurement.

show abstract

Screening for Neonatal Jaundice with a Smartphone

Cited by 10 publications

References 5 publications

Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning

Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning

Smartphone screening for neonatal jaundice via ambient-subtracted sclera chromaticity

Development of Jaundice Detection Approaches in Neonates

Contact Info

Product

Resources

About