Neonatal Jaundice Detection System

Smartphone-based screening of neonatal jaundice in three populations in low and middle-income countries: a cross-sectional study

BACKGROUND

Neonatal hyperbilirubinemia (NHB) is a significant cause of morbidity and mortality, particularly in low and middle-income countries (LMICs). Transcutaneous bilirubinometers offer a non-invasive method for assessing NHB but have limited availability due to cost and maintenance requirements. Visual assessment of jaundice is shown to be inaccurate. Smartphone-based technologies have the potential to provide innovative and accessible healthcare solutions. This study aimed to evaluate the Picterus system, a smartphone-based tool for screening of NHB, in three non-Caucasian populations in LMICs.

METHODS

Between 2018 and 2022, cross-sectional studies were conducted in three countries: Mexico, Nepal and the Philippines. Newborns meeting the inclusion criteria were recruited, and data on demographic characteristics, skin type and visual assessment of jaundice were collected. Bilirubin levels were measured using both the Picterus system and total serum bilirubin (TSB) analysis. Correlation analyses, Bland-Altman plots and receiver operating characteristic (ROC) curves were used to evaluate the Picterus system.

RESULTS

A total of 416 infants were included in the analysis. The Picterus smartphone system demonstrated a significant positive correlation with TSB levels across all sites (r=0.76). The correlation coefficient was significantly higher in Mexico compared with Nepal and the Philippines. Bland-Altman plots showed limits of agreement ±89.2 µmol/L. Picterus values were underestimated in Mexico, whereas they were overestimated in Nepal and the Philippines. ROC analysis for detection of infants with TSB >225 µmol/L indicated that the Picterus system had higher sensitivity and specificity compared with visual assessment using the Kramer scale.

DISCUSSION

This study shows that the Picterus system can potentially be used in screening for neonatal jaundice in populations with moderate dark skin types. Further studies are needed before the system can be used in clinical practice.

Artificial Intelligence non-invasive methods for neonatal jaundice detection: A review

Neonatal jaundice is a common and potentially fatal health condition in neonates, especially in low and middle income countries, where it contributes considerably to neonatal morbidity and death. Traditional diagnostic approaches, such as Total Serum Bilirubin (TSB) testing, are invasive and could lead to discomfort, infection risk, and diagnostic delays. As a result, there is a rising interest in non-invasive approaches for detecting jaundice early and accurately. An in-depth analysis of non-invasive techniques for detecting neonatal jaundice is presented by this review, exploring several AI-driven techniques, such as Machine Learning (ML) and Deep Learning (DL), which have demonstrated the ability to enhance diagnostic accuracy by evaluating complex patterns in neonatal skin color and other relevant features. It is identified that AI models incorporating variants of neural networks achieve an accuracy rate of over 90% in detecting jaundice when compared to traditional methods. Furthermore, satisfactory outcomes in field settings have been demonstrated by mobile-based applications that use smartphone cameras to estimate bilirubin levels, providing a practical alternative for resource-constrained areas. The potential impact of AI-based solutions on reducing neonatal morbidity and mortality is evaluated by this review, with a focus on real-world clinical challenges, highlighting the effectiveness and practicality of AI-based strategies as an assistive tool in revolutionizing neonatal care through early jaundice diagnosis, while also addressing the ethical and practical implications of integrating these technologies in clinical practice. Future research areas, such as the development of new imaging technologies and the incorporation of wearable sensors for real-time bilirubin monitoring, are recommended by the paper.

Feasibility study of texture-based machine learning approach for early detection of neonatal jaundice

Untreated neonatal jaundice can have severe consequences. Effective screening for neonatal jaundice can prevent long-term complications in infants. Non-invasive approaches may be beneficial in settings with limited resources. This feasibility study explores a texture-based machine learning approach for early detection of neonatal jaundice. Clinical data and skin images of 200 infants were captured from four body locations using the Neonatal Jaundice Screening and Assessment Plate. Data were split into training/validating (n = 160) and blind testing (n = 40) datasets. Ninety-two features (three clinical, 89 texture-based) were extracted after image processing. Eight machine learning models were compared for bilirubin level prediction. The best performing model, Support Vector Machine (SVM), was implemented in a web-based application (AmberSNAP) and tested using blind testing dataset. SVM paired with RRelief-F feature selection achieved optimal performance for head and sternum measurements, while SVM with Univariate Regression performed best for abdomen and lower leg measurements. Blind testing demonstrated good performance in bilirubin level prediction (mean absolute error: 1.675 mg/dL; root mean square error: 2.192 mg/dL), with moderate correlation between predicted and measured values (r = 0.644, p < 0.001). These findings suggest that texture-based machine learning is a feasible approach for neonatal jaundice screening in low-resource settings.

Visualization of Unconjugated Bilirubin In Vivo with a Novel Approach Using Chemical Exchange Saturation Transfer Magnetic Resonance Imaging in a Rat Model

Unconjugated bilirubin (UCB) visualization is valuable for early bilirubin encephalopathy (BE) diagnosis and management. UCB neurotoxicity is a challenge, necessitating improved imaging modalities for precise localization and characterization. This study developed a noninvasive method for UCB imaging in the brain using chemical exchange saturation transfer (CEST) magnetic resonance imaging, which visualizes UCB distribution through amide-bulk water proton exchange, a process termed bilirubin CEST (Bil-CEST) imaging. Bil-CEST imaging parameters were initially optimized; the exchange rate of the amide protons of UCB was calculated. Bil-CEST imaging characteristics and specificity were assessed using in vitro images of UCB solutions under different conditions and images of other brain metabolites. Bil-CEST maps of the rat brain were collected at the baseline and dynamically, postinjection of the UCB solution or vehicle into lateral ventricles of Sprague-Dawley rats. The model was validated using a water maze and pathological staining. In vitro, the Bil-CEST effect was observed at approximately 5.5 ppm downfield from bulk water. This effect was proportional to the UCB concentration and B1 amplitude. In vivo, Bil-CEST imaging revealed a progressive enhancement following a lateral ventricular UCB injection. Conversely, no significant imaging changes were observed in the vehicle group. Compared with the vehicle group, the UCB group had more hippocampal neuronal apoptosis and worse cognitive function. These findings highlight the utility of Bil-CEST in direct UCB imaging, indicating its potential as a clinically valuable biomarker for BE diagnosis and management.

Development and Validation of a Smartphone Application for Neonatal Jaundice Screening

Importance

This diagnostic study describes the merger of domain knowledge (Kramer principle of dermal advancement of icterus) with current machine learning (ML) techniques to create a novel tool for screening of neonatal jaundice (NNJ), which affects 60% of term and 80% of preterm infants.

Objective

This study aimed to develop and validate a smartphone-based ML app to predict bilirubin (SpB) levels in multiethnic neonates using skin color analysis.

Design, Setting, and Participants

This diagnostic study was conducted between June 2022 and June 2024 at a tertiary hospital and 4 primary-care clinics in Singapore with a consecutive sample of neonates born at 35 or more weeks' gestation and within 21 days of birth.

Exposure

The smartphone-based ML app captured skin images via the central aperture of a standardized color calibration sticker card from multiple regions of interest arranged in a cephalocaudal fashion, following the Kramer principle of dermal advancement of icterus. The ML model underwent iterative development and k-folds cross-validation, with performance assessed based on root mean squared error, Pearson correlation, and agreement with total serum bilirubin (TSB). The final ML model underwent temporal validation.

Main Outcomes and Measures

Linear correlation and statistical agreement between paired SpB and TSB; sensitivity and specificity for detection of TSB equal to or greater than 17mg/dL with SpB equal to or greater than 13 mg/dL were assessed.

Results

The smartphone-based ML app was validated on 546 neonates (median [IQR] gestational age, 38.0 [35.0-41.0] weeks; 286 [52.4%] male; 315 [57.7%] Chinese, 35 [6.4%] Indian, 169 [31.0%] Malay, and 27 [4.9%] other ethnicities). Iterative development and cross-validation was performed on 352 neonates. The final ML model (ensembled gradient boosted trees) incorporated yellowness indicators from the forehead, sternum, and abdomen. Temporal validation on 194 neonates yielded a Pearson r of 0.84 (95% CI, 0.79-0.88; P < .001), 82% of data pairs within clinically acceptable limits of 3 mg/dL, sensitivity of 100%, specificity of 70%, positive predictive value of 10%, negative predictive value of 100%, positive likelihood ratio of 3.3, negative likelihood ratio of 0, and area under the receiver operating characteristic curve of 0.89 (95% CI, 0.82-0.96).

Conclusions and Relevance

In this diagnostic study of a new smartphone-based ML app, there was good correlation and statistical agreement with TSB with sensitivity of 100%. The screening tool has the potential to be an NNJ screening tool, with treatment decisions based on TSB (reference standard). Further prospective studies are needed to establish the generalizability and cost-effectiveness of the screening tool in the clinical setting.

Comparative Analysis of Classification of Neonatal Bilirubin by Using Various Machine Learning Approaches

Background Neonatal jaundice poses significant risks to newborn health, necessitating early detection and management. Machine learning (ML) offers promising avenues for improving classification and monitoring, potentially revolutionizing neonatal care. Materials and methods A comparative analysis was conducted using various ML algorithms to classify neonatal bilirubin levels. Data were collected from neonatal images, and algorithms were trained and tested using standard methodologies. Performance metrics, including accuracy, precision, and recall, were evaluated to assess algorithm effectiveness. Results The Nu-Support Vector Classification (NuSVC) model emerged as the most effective, achieving a testing accuracy of 62.50%, with precision and recall rates of 61.90% and 56.52%, respectively. While variability existed among algorithms, these results highlight NuSVC's potential for clinical application in neonatal jaundice screening. Conclusion ML holds promise for improving neonatal jaundice detection and management. The findings suggest that the NuSVC algorithm can enhance screening accuracy, potentially mitigating risks associated with untreated neonatal jaundice. Future research should focus on refining models for broader clinical applicability and integrating ML into decision support systems to improve neonatal care globally.

Artificial intelligence in paediatrics: Current events and challenges

This article examines the use of artificial intelligence (AI) in the field of paediatric care within the framework of the 7P medicine model (Predictive, Preventive, Personalized, Precise, Participatory, Peripheral and Polyprofessional). It highlights various applications of AI in the diagnosis, treatment and management of paediatric diseases as well as the role of AI in prevention and in the efficient management of health care resources and the resulting impact on the sustainability of public health systems. Successful cases of the application of AI in the paediatric care setting are presented, placing emphasis on the need to move towards a 7P health care model. Artificial intelligence is revolutionizing society at large and has a great potential for significantly improving paediatric care.

The future of neonatal lung ultrasound: Validation of an artificial intelligence model for interpreting lung scans. A multicentre prospective diagnostic study

BACKGROUND

Artificial intelligence (AI) is a promising field in the neonatal field. We focused on lung ultrasound (LU), a useful tool for the neonatologist. Our aim was to train a neural network to create a model able to interpret LU.

METHODS

Our multicentric, prospective study included newborns with gestational age (GA) ≥ 33 + 0 weeks with early tachypnea/dyspnea/oxygen requirements. For each baby, three LU were performed: within 3 h of life (T0), at 4-6 h of life (T1), and in the absence of respiratory support (T2). Each scan was processed to extract the region of interest used to train a neural network to classify it according to the LU score (LUS). We assessed sensitivity, specificity, positive and negative predictive value of the AI model's scores in predicting the need for respiratory assistance with nasal continuous positive airway pressure and for surfactant, compared to an already studied and established LUS.

RESULTS

We enrolled 62 newborns (GA = 36 ± 2 weeks). In the prediction of the need for CPAP, we found a cutoff of 6 (at T0) and 5 (at T1) for both the neonatal lung ultrasound score (nLUS) and AI score (AUROC 0.88 for T0 AI model, 0.80 for T1 AI model). For the outcome "need for surfactant therapy", results in terms of area under receiver operator characteristic (AUROC) are 0.84 for T0 AI model and 0.89 for T1 AI model. In the prediction of surfactant therapy, we found a cutoff of 9 for both scores at T0, at T1 the nLUS cutoff was 6, while the AI's one was 5. Classification accuracy was good both at the image and class levels.

CONCLUSIONS

This is, to our knowledge, the first attempt to use an AI model to interpret early neonatal LUS and can be extremely useful for neonatologists in the clinical setting.

Performance of smartphone application to accurately quantify hyperbilirubinemia in neonates: a systematic review with meta-analysis

Neonatal jaundice is a common clinical condition that can progress to severe hyperbilirubinemia if identification and intervention are delayed. In this study, we aimed to analyze the current evidence on the accurate performance of smartphone applications to quantify bilirubin levels. PubMed, Embase, Emcare, MEDLINE, the Cochrane Library, and Google Scholar were searched from inception until July 2022. Grey literature was searched on "OpenGrey" and "MedNar" databases. We included prospective and retrospective cohort studies that recruited infants with a gestation of ≥ 35 weeks and reported paired total serum bilirubin (TSB) and smartphone app-based bilirubin (ABB) levels. We conducted the review using the guidelines of the Cochrane Collaboration Diagnostic Test Accuracy Working Group and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy (PRISMA-DTA) statement. The data were pooled using the random effects model. The outcome of interest was agreement between ABB and TSB measurements, provided as correlation coefficient, mean difference, and standard deviation. Certainty of evidence (COE) was assessed based on GRADE guidelines. Fourteen studies were included in the meta-analysis. The number of infants in individual studies ranged between 35 and 530. The pooled correlation coefficient (r) between ABB and TSB was 0.77 (95% CI 0.69 to 0.83; p < 0.01). Reported sensitivities for predicting a TSB of 250 µmol/L in individual studies ranged between 75 and 100% and specificities ranged from 61 to 100%. Similarly, a sensitivity of 83 to 100% and a specificity of 19.5 to 76% were reported for predicting a TSB of 205 μmol/L. Overall COE was considered moderate.    Conclusion: Smartphone app-based bilirubin estimation showed a reasonable correlation to TSB levels. Well-designed studies are required to determine its utility as a screening tool for various TSB cut-off levels. What is Known: • Neonatal jaundice is a common clinical condition. Timely screening and intervention are necessary to prevent neurological morbidities • Transcutaneous bilirubinometer is a widely used non-invasive screening device but is mostly available in hospital settings and has cost limitations. Researchers have recently explored the utility of smartphone applications to estimate bilirubin levels in neonates. What is New: • This is the first systematic review and meta-analysis conducted to assess the performance of smartphone applications to detect neonatal hyperbilirubinemia. • Bilirubin estimates of newborn infants obtained through smartphone applications had a reasonable correlation with serum bilirubin levels.

Validation of a smartphone-based screening tool (Biliscan) for neonatal jaundice in a multi-ethnic neonatal population

AIM

Neonatal jaundice is an important and prevalent condition that can cause kernicterus and mortality. This study validated a smartphone-based screening application (Biliscan) in detecting neonatal jaundice.

METHODS

A cross-sectional prospective study was conducted at the neonatal unit in a tertiary teaching hospital between August 2020 and October 2021. All babies born at the gestation of 35 weeks and above with clinical jaundice or are recommended for screening of jaundice within 21 days of post-natal age were recruited. Using Biliscan, images of the babies' skin over the sternum were taken against a standard colour card. The application uses feature extraction and machine learning regression to estimate the bilirubin level. Independent Biliscan bilirubin estimates (BsB) were made and compared with total serum bilirubin (TSB) and transcutaneous bilirubin (TcB) levels. Bland Altman plots were used to establish the agreement between BsB and TSB, as well as TcB, using the clinically acceptable limits of agreement of ±35 μmol/L, which were defined a priori. Pearson correlation coefficient was assessed to establish the strength of the relationship between BsB versus TSB and TcB. Diagnostic accuracy was assessed through receiver operating characteristic curve analysis.

RESULTS

Sixty-one paired TSB-BsB and 85 paired TcB-BsB measurements were obtained. Bland Altman plot for the entire group showed that 54% (33/61) of the pairs of TSB and BsB readings and 66% (56/85) of the pairs of TcB and BsB readings were within the maximum clinically acceptable difference of 35 μmol/L. Pearson r for BsB versus TSB and TcB was 0.54 (P < 0.001) and 0.66 (P < 0.001) respectively. Compared with TSB, the recommended gold standard measure for jaundice, Biliscan has a sensitivity of 76.92% and specificity of 70.83% for jaundice requiring phototherapy. The positive and negative predictive values in term infants were 93.3% and 36.9%, respectively.

CONCLUSION

Our results suggest that there is moderate correlation and mediocre agreement between BsB and TSB, as well as TcB. Improvement to the application algorithm and further studies that include a larger population, and a wider range of bilirubin values are necessary before the tool may be considered for use in screening of jaundice in newborns.

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

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.

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

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. IMPACT: Key message: Screening methods for neonatal hyperbilirubinemia facilitate early recognition and timely treatment of severe neonatal hyperbilirubinemia (SNH). Any bilirubin screening result obtained must be confirmed by a diagnostic method. What does this article add to the existing literature? Data on optical bilirubin estimation are summarized. Niche research strategies for prevention of SNH are presented. Impact: Transcutaneous screening for neonatal hyperbilirubinemia contributes to the prevention of SNH. A smartphone application with optical bilirubin estimation seems a promising low-cost screening method, especially in low-resource settings or at home.

Neonatal wearable device for colorimetry-based real-time detection of jaundice with simultaneous sensing of vitals

Neonatal jaundice occurs in >80% of newborns in the first week of life owing to physiological hyperbilirubinemia. Severe hyperbilirubinemia could cause brain damage owing to its neurotoxicity, a state commonly known as kernicterus. Therefore, periodic bilirubin monitoring is essential to identify infants at-risk and to initiate treatment including phototherapy. However, devices for continuous measurements of bilirubin have not been developed yet. Here, we established a wearable transcutaneous bilirubinometer that also has oxygen saturation (SpO2) and heart rate (HR) sensing functionalities. Clinical experiments with neonates demonstrated the possibility of simultaneous detection of bilirubin, SpO2, and HR. Moreover, our device could consistently measure bilirubin during phototherapy. These results demonstrate the potential for development of a combined treatment approach with an automatic link via the wearable bilirubinometer and phototherapy device for optimization of the treatment of neonatal jaundice.

R-JaunLab: Automatic Multi-Class Recognition of Jaundice on Photos of Subjects with Region Annotation Networks

Jaundice occurs as a symptom of various diseases, such as hepatitis, the liver cancer, gallbladder or pancreas. Therefore, clinical measurement with special equipment is a common method that is used to identify the total serum bilirubin level in patients. Fully automated multi-class recognition of jaundice combines two key issues: (1) the critical difficulties in multi-class recognition of jaundice approaches contrasting with the binary class and (2) the subtle difficulties in multi-class recognition of jaundice represent extensive individuals variability of high-resolution photos of subjects, huge coherency between healthy controls and occult jaundice, as well as broadly inhomogeneous color distribution. We introduce a novel approach for multi-class recognition of jaundice to detect occult jaundice, obvious jaundice and healthy controls. First, region annotation network is developed and trained to propose eye candidates. Subsequently, an efficient jaundice recognizer is proposed to learn similarities, context, localization features and globalization characteristics on photos of subjects. Finally, both networks are unified by using shared convolutional layer. Evaluation of the structured model in a comparative study resulted in a significant performance boost (categorical accuracy for mean 91.38%) over the independent human observer. Our work was exceeded against the state-of-the-art convolutional neural network (96.85% and 90.06% for training and validation subset, respectively) and showed a remarkable categorical result for mean 95.33% on testing subset. The proposed network makes a performance better than physicians. This work demonstrates the strength of our proposal to help bringing an efficient tool for multi-class recognition of jaundice into clinical practice.

Noninvasive methods for bilirubin measurements in newborns: A report

New technologies have become available for noninvasive assessments of neonatal hyperbilirubinemia. Our objective is to review the noninvasive methods for measuring bilirubin in the newborn. We searched relevant literature from 1966 to January 1, 2020, which included cross-sectional studies to define the accuracy of any noninvasive methods for measuring or estimating total serum/plasma bilirubin (TB) levels in newborns. We identified and included 83 relevant studies of direct visual assessment, icterometry, mobile phone applications, and transcutaneous bilirubinometry (TcB). Compared with laboratory TB measurements, visual assessment was the least accurate and least reliable (r: 0.37 to 074), while TcB was the most accurate, but not always near-equivalent (r: 0.45 to 0.99). The sensitivity and specificity of TcB cut-off values to detect significant hyperbilirubinemia (TB>95th percentile for age in hours) ranged from 74% to 100% and 18% to 89%, respectively.

Bilirubin estimates from smartphone images of newborn infants' skin correlated highly to serum bilirubin levels

AIM

Neonatal jaundice is an important cause of morbidity and mortality, and identifying the condition remains a challenge. This study evaluated a novel method of estimating bilirubin levels from colour-calibrated smartphone images.

METHODS

A cross-sectional prospective study was undertaken at two hospitals in Norway from February 2017 to March 2019, with standardised illumination at one hospital and non-standardised illumination at the other hospital. Healthy term-born infants with a normal birthweight were recruited up to 15 days of age. The main outcome measures were bilirubin estimates from digital images, plus total bilirubin in serum (TSB) and transcutaneous bilirubin (TcB).

RESULTS

Bilirubin estimates were performed for 302 newborn infants, and 76 had severe jaundice. The correlation between the smartphone estimates and TSB was measured by Pearson's r and was .84 for the whole sample. The correlation between the image estimates and TcB was 0.81. There were no significant differences between the hospitals. Sensitivity was 100%, and specificity was 69% for identifying severe jaundice of more than 250 µmol/L.

CONCLUSION

A smartphone-based tool that estimated bilirubin levels from digital images identified severe jaundice with high sensitivity and could provide a screening tool for neonatal jaundice.

Smartphone screening for neonatal jaundice via ambient-subtracted sclera chromaticity

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 jaundice-related 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.

Acylation derivatization based LC-MS analysis of 25-hydroxyvitamin D from finger-prick blood

Vitamin D metabolite analysis possessed significant clinical value for the pediatric department. However, invasive venipuncture sampling and high blood consumption inflicted much suffering on patients. For alleviation, we carried out a LC-MS method for 25-hydroxyvitamin D quantification in only 3 μl of plasma from the considerably less invasive finger-prick blood samples. To improve sensitivity, acylation on C3-hydroxyl (by isonicotinoyl chloride) rather than Diels-Alder adduction on s-cis-diene structure was for the very first time introduced into vitamin D metabolite derivatization. Compared with the existing derivatization approaches, this novel strategy not only prevented isomer interference, but also exhibited higher reacting throughput. For certification, the methodology was systematically validated and showed satisfying consistency with SRM927a. During clinical application, we found a convincing correlation between 25-hydroxyvitamin D and indirect/total bilirubin in jaundiced newborns. Such an observation indicated that vitamin D supplementation may help to achieve optimal outcomes in neonatal jaundice.