Validation of transcutaneous bilirubinometry during phototherapy for detection and monitoring of neonatal jaundice in a low-income setting

Johnson, S.D.; Vasu, V.; Marseille, C; Cj, Hill; Janvier, L; Toussaint, P.; Battersby, Cheryl

doi:10.1080/20469047.2019.1598126

Cited by 15 publications

(12 citation statements)

References 14 publications

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“…Previous studies have reported that transcutaneous bilirubin has a significant correlation with serum total bilirubin in term and preterm infants, even under and after phototherapy [ 9 , 15 , 16 , 17 , 18 ]. However, a discrepancy between transcutaneous and serum bilirubin measurements was still noted with extremely higher or lower bilirubin levels [ 19 ]. The factors potentially associated with inaccurate TCB results are preterm status, skin maturation condition, skin pigmentation and color, coverage of skin sites with light protection, and body sites for measurement [ 3 , 4 , 5 , 8 , 12 , 20 ].…”

Section: Discussionmentioning

confidence: 99%

The Impact of Phototherapy on the Accuracy of Transcutaneous Bilirubin Measurements in Neonates: Optimal Measurement Site and Timing

Lin

Chen

2021

Diagnostics

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Transcutaneous bilirubinometer devices are widely applied to assess neonatal hyperbilirubinemia. However, the optimal skin site and timing of transcutaneous bilirubin (TCB) measurements for the strongest correlation with total serum bilirubin (TSB) levels after phototherapy are still unclear. We conducted a retrospective observational study evaluating the correlation of TCB and TSB levels in neonates postphototherapy. The TCB measurements on the forehead and mid-sternum at 0 and 30 min postphototherapy were assessed by using a JM-103 bilirubinometer. Paired TCB and TSB measurements were assessed by Pearson correlation and Bland–Altman plots. We analyzed 40 neonates with 96 TSB and 384 TCB measurements. The TSB level correlated moderately with the forehead TCB level at 30 min postphototherapy (r = 0.65) and less strongly with the midsternum TCB level at 0 min postphototherapy (r = 0.52). The forehead at 30 min after cessation of phototherapy was the best time point and location of TCB measurement for the assessment of neonatal jaundice status. The reliability of TCB measurements varied across skin sites and durations after phototherapy. The effectiveness of TCB measurement to assess neonatal hyperbilirubinemia is much better on covered skin areas (foreheads) 30 min postphototherapy. The appropriate application of transcutaneous bilirubinometers could aid in clinical practice and avoid unnecessary management.

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Section: Discussionmentioning

confidence: 99%

The Impact of Phototherapy on the Accuracy of Transcutaneous Bilirubin Measurements in Neonates: Optimal Measurement Site and Timing

Lin

Chen

2021

Diagnostics

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“…Moreover, these instruments may overestimate TSB at low values and underestimate TSB at high values. 31 Aranda and colleagues showed that in various groups of term and preterm multi-racial sick neonates, TcB readings above 205 µmol/L should be confirmed with standard laboratory methods. 32 Nevertheless, although TcB may underestimate TSB when concentrations are higher than 205 or 257 μmol/L, other authors report no increase of underestimation at a specific TSB level.…”

Section: Transcutaneous Bilirubinometrymentioning

confidence: 99%

Screening methods for neonatal hyperbilirubinemia: benefits, limitations, requirements, and novel developments

et al. 2021

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Severe neonatal hyperbilirubinemia (SNH) is a serious condition that occurs worldwide. Timely recognition with bilirubin determination is key in the management of SNH. Visual assessment of jaundice is unreliable. Fortunately, transcutaneous bilirubin measurement for screening newborn infants is routinely available in many hospitals and outpatient settings. Despite a few limitations, the use of transcutaneous devices facilitates early recognition and appropriate management of neonatal jaundice. Unfortunately, however, advanced and often costly screening modalities are not accessible to everyone, while there is an urgent need for inexpensive yet accurate instruments to assess total serum bilirubin (TSB). In the near future, novel icterometers, and in particular optical bilirubin estimates obtained with a smartphone camera and processed with a smartphone application (app), seem promising methods for screening for SNH. If proven reliable, these methods may empower outpatient health workers as well as parents at home to detect jaundice using a simple portable device. Successful implementation of ubiquitous bilirubin screening may contribute substantially to the reduction of the worldwide burden of SNH. The benefits of non-invasive bilirubin screening notwithstanding, any bilirubin determination obtained through non-invasive screening must be confirmed by a diagnostic method before treatment.

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“…As previously mentioned, there are some existing medical devices that measure transcutaneous bilirubin from the skin, such as BiliCheck [24][25][26], Minolta JM-102 [27], and also some efforts to develop smartphone applications based on skin images, such as BiliCam [9,12] and BiliScan [13], and eyes images, such as Biliscreen [18], BiliCapture [20], and neoSCB [22]. A considerable amount of literature has been published on how the performance of these non-invasive bilirubin detection tools compares [24,30,[40][41][42]. For example, the work in [30] presented a comparison between JM103, BiliCheck, and BiliCam.…”

Section: Related Workmentioning

confidence: 99%

“…For forehead segmentation, we focused on the area twenty pixels above the points in the range [18,25] to avoid the eyebrows, and we counted 120 pixels above to be the height of the area [45]. To determine the sclera of left eye, we focused on points in the range [42,47], while for the right eye, we used the area in the range [36,41].…”

Section: Roi Detection and Segmentationmentioning

confidence: 99%

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

Althnian

Almanea

Aloboud

2021

Sensors

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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.

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Validation of transcutaneous bilirubinometry during phototherapy for detection and monitoring of neonatal jaundice in a low-income setting

Cited by 15 publications

References 14 publications

The Impact of Phototherapy on the Accuracy of Transcutaneous Bilirubin Measurements in Neonates: Optimal Measurement Site and Timing

The Impact of Phototherapy on the Accuracy of Transcutaneous Bilirubin Measurements in Neonates: Optimal Measurement Site and Timing

Screening methods for neonatal hyperbilirubinemia: benefits, limitations, requirements, and novel developments

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

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