Non-invasive Methods for Diagnosing Jaundice in Newborns: A Review

Abstract views: 32 / PDF downloads: 18


  • Banan K. Abdulkader University of Mousl
  • Mazin H. Aziz University of Mousl


Neonatal jaundice, Machine Learning, bilirubin, Total Serum Bilirubin, Transcutaneous Bilirubin


Neonatal jaundice, characterized by the manifestation of yellowing in the skin and eyes as a
result of augmented levels of bilirubin, presents a substantial peril to the well-being and duration of life in
neonates, conceivably influencing their comprehensive health and longevity. Its early onset, typically
within the initial days, demands prompt attention, especially when it arises physiologically on the second
or third day. Elevated bilirubin, stemming from red blood cell breakdown, presents a challenge for
newborns as they struggle to naturally eliminate this pigment. Left untreated, jaundice can lead to severe
outcomes like kernicterus, causing irreversible brain damage due to heightened bilirubin levels. This study
aims to comprehensively assess various non-invasive frameworks for identifying neonatal jaundice. The
review scrutinizes innovative, non-invasive approaches, comparing methods based on clinical data to
predict serum bilirubin levels. Challenges in using machine learning for jaundice detection are also
highlighted. Non-invasive methods have shown remarkable success across diagnostic, supportive, research,
and clinical domains in managing neonatal jaundice. This ongoing exploration sets the stage for improved
neonatal care, underscoring the importance of timely diagnosis and intervention to prevent enduring
neurological damage resulting from acute bilirubin encephalopathy. The conclusions drawn from this
research hold great importance, as they emphasize the possibility of non-invasive methods to revolutionize
neonatal healthcare, guaranteeing a safer and more efficient approach to monitoring and treating jaundice
in infants.


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Author Biographies

Banan K. Abdulkader, University of Mousl

Computer Engineer, IRAQ

Mazin H. Aziz, University of Mousl

Computer Engineer, IRAQ


A. Chakraborty, S. Goud, V. Shetty, and B. Bhattacharyya, “Neonatal Jaundice Detection System using CNN Algorithm and Image Processing,” International Journal of Electrical Engineering and Technology, vol. 11, no. 3, pp. 248–264, 2020.

A. Yaseen Abdulrazzak, S. Latif Mohammed, A. Al-Naji, and J. Chahl, “Computer-Aid System for Automated JaDetectionundice,” Journal of Techniques, vol. 5, no. 1, pp. 8–15, Mar. 2023, doi: 10.51173/jt.v5i1.1128.

P. Annisa, A. W. Astuti, and S. Sharma, “Neonatal Jaundice Causal Factors: A Literature Review,” Women, Midwives and Midwifery, vol. 3, no. 1, pp. 45–60, Feb. 2023, doi: 10.36749/wmm.3.1.45-60.2023.

A. Gourishankar and S. S. Akkinapally, “Urinary tract infection in infants with asymptomatic Jaundice: a meta-analysis UTI and Jaundice Review”, doi: 10.1101/2022.07.26.22278041.

T. W. R. Hansen, “The epidemiology of neonatal jaundice,” Pediatric Medicine, vol. 4. AME Publishing Company, May 01, 2021. doi: 10.21037/pm-21-4.

S. Nihila, T. Rajalakshmi, S. S. Panda, N. Lhazay, and G. D. Giri, “Non-invasive Technique for Detecting Neonatal Jaundice,” 2022. doi: 10.1007/978-981-16-2123-9_46.

S. Hamidreza, “Clinical and Physiological studies of jaundice in the newborn infants and novel design and diagnostic method for neonatal hyperbilirubinemia determination,” Archive of Biomedical Science and Engineering, vol. 8, no. 1, pp. 005–011, Aug. 2022, doi: 10.17352/abse.000028.

C. Dani, C. V. Hulzebos, and C. Tiribelli, “Transcutaneous bilirubin measurements: useful, but also reproducible?,” Pediatric Research, vol. 89, no. 4. Springer Nature, pp. 725–726, Mar. 01, 2021. doi: 10.1038/s41390-020-01242-3.

M. K. Shariati, N. T. Taleghani, N. Izadi, A. Miri, R. T. Tafti, and F. A. Gorji, “Which Is More Accurate: Transcutaneous Bilirubin Measurement on the Forehead or Sternum?,” Arch Iran Med, vol. 25, no. 8, pp. 552–556, Aug. 2022, doi: 10.34172/aim.2022.88.

M. Spandana and J. Vagha, “Review on Different Methods of Serum Bilirubin Estimation,” J Pharm Res Int, pp. 34–41, Jan. 2022, doi: 10.9734/jpri/2022/v34i3b35390.

R. Singla and S. Singh, “A Framework for Detection of Jaundice in New Born Babies using Homomorphic Filtering Based Image Processing.”

C. Bin Tsai, W. Y. Hung, and W. Y. Hsu, “A fast and effective system for analysis of optokinetic waveforms with a low-cost eye tracking device,” Healthcare (Switzerland), vol. 9, no. 1, Jan. 2021, doi: 10.3390/healthcare9010010.

M. Sharma, L. Khiangte, P. Beti, T. Kambiakdik, and C. S. Singh, “Pathological jaundice in late preterm neonates admitted in a tertiary hospital, Imphal: a prospective COHORT study,” Int J Contemp Pediatrics, vol. 9, no. 12, p. 1163, Nov. 2022, doi: 10.18203/2349-3291.ijcp20223062.

M. N. Mansor et al., “Jaundice in newborn monitoring using color detection method,” in Procedia Engineering, 2012, pp. 1631–1635. doi: 10.1016/j.proeng.2012.01.185.

K. Mreihil et al., “Uniform national guidelines do not prevent wide variations in the clinical application of phototherapy for neonatal jaundice,” Acta Paediatrica, International Journal of Paediatrics, vol. 107, no. 4, 2018, doi: 10.1111/apa.14142.

K. Mreihil et al., “Phototherapy is commonly used for neonatal jaundice but greater control is needed to avoid toxicity in the most vulnerable infants.,” Acta Paediatrica, International Journal of Paediatrics, vol. 107, no. 4, pp. 611–619, Apr. 2018, doi: 10.1111/apa.14141.

M. J. Maisels, J. F. Watchko, V. K. Bhutani, and D. K. Stevenson, “An approach to the management of hyperbilirubinemia in the preterm infant less than 35 weeks of gestation,” Journal of Perinatology, vol. 32, no. 9, pp. 660–664, Sep. 2012, doi: 10.1038/jp.2012.71.

W. Hashim, A. Al-Naji, I. A. Al-Rayahi, and M. Oudah, “Computer Vision for Jaundice Detection in Neonates Using Graphic User Interface,” IOP Conf Ser Mater Sci Eng, vol. 1105, no. 1, p. 012076, Jun. 2021, doi: 10.1088/1757-899x/1105/1/012076.

T. Burzykowski, A. J. Rousseau, M. Geubbelmans, and D. Valkenborg, “Introduction to machine learning,” American Journal of Orthodontics and Dentofacial Orthopedics, vol. 163, no. 5. Elsevier Inc., pp. 732–734, May 01, 2023. doi: 10.1016/j.ajodo.2023.02.005.

“Machine Learning,” in Artificial Intelligence Technology, Singapore: Springer Nature Singapore, 2023, pp. 43–86. doi: 10.1007/978-981-19-2879-6_2.

Shaveta, “A review on machine learning,” International Journal of Science and Research Archive, vol. 9, no. 1, pp. 281–285, May 2023, doi: 10.30574/ijsra.2023.9.1.0410.

A. Althnian, N. Almanea, and N. Aloboud, “Neonatal jaundice diagnosis using a smartphone camera based on eye, skin, and fused features with transfer learning,” Sensors, vol. 21, no. 21, Nov. 2021, doi: 10.3390/s21217038.

R. Karim, M. Zaman, and W. H. Yong, “A Non-invasive Methods for Neonatal Jaundice Detection and Monitoring to Assess Bilirubin Level: A Review,” Annals of Emerging Technologies in Computing, vol. 7, no. 1. International Association for Educators and Researchers (IAER), pp. 15–29, Jan. 01, 2023. doi: 10.33166/AETiC.2023.01.002.

A. Gupta, A. Kumar, and P. Khera, “Method and Model for Jaundice Prediction Through Non-Invasive Bilirubin Detection Technique.” [Online]. Available:

S. B. Munkholm, T. Krøgholt, F. Ebbesen, P. B. Szecsi, and S. R. Kristensen, “The smartphone camera as a potential method for transcutaneous bilirubin measurement,” PLoS One, vol. 13, no. 6, Jun. 2018, doi: 10.1371/journal.pone.0197938.

P. Padidar et al., “Detection of neonatal jaundice by using an android OS-based smartphone application,” Iran J Pediatr, vol. 29, no. 2, Apr. 2019, doi: 10.5812/ijp.84397.

S. Ali, Z. Beiji, and A. Ali, “An Algorithm for Diagnosis of the Three Kinds of Constitutional Jaundice,” 2010.

A. Kumar, P. Khera, and N. Saini, “Bilirubin Detection Technique for Jaundice Prediction Using Smartphones Article in,” 2016. [Online]. Available:

F. Outlaw, J. Meek, L. W. MacDonald, and T. S. Leung, “Screening for neonatal jaundice with a smartphone,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Jul. 2017, pp. 241–242. doi: 10.1145/3079452.3079488.

T. S. Leung et al., “Screening neonatal jaundice based on the sclera color of the eye using digital photography,” Biomed Opt Express, vol. 6, no. 11, p. 4529, Nov. 2015, doi: 10.1364/boe.6.004529.

M. R. Rizvi, F. M. Alaskar, R. S. Albaradie, N. F. Rizvi, and K. Al-Abdulwahab, “A novel non-invasive technique of measuring bilirubin levels using bilicapture,” Oman Med J, vol. 34, no. 1, pp. 26–33, Jan. 2019, doi: 10.5001/OMJ.2019.05.

F. Outlaw, M. Nixon, O. Odeyemi, L. W. MacDonald, J. Meek, and T. S. Leung, “Smartphone screening for neonatal jaundice via ambient-subtracted sclera chromaticity,” PLoS One, vol. 15, no. 3, 2020, doi: 10.1371/journal.pone.0216970.

M. Aydın, F. Hardalaç, B. Ural, and S. Karap, “Neonatal Jaundice Detection System,” J Med Syst, vol. 40, no. 7, Jul. 2016, doi: 10.1007/s10916-016-0523-4.

S. Swarna, S. Pasupathy, B. Chinnasami, N. M. D., and B. Ramraj, “The smart phone study: assessing the reliability and accuracy of neonatal jaundice measurement using smart phone application,” Int J Contemp Pediatrics, vol. 5, no. 2, p. 285, Feb. 2018, doi: 10.18203/2349-3291.ijcp20175928.

J. A. Taylor et al., “Use of a Smartphone App to Assess Neonatal Jaundice.”

J. Castro-Ramos, C. Toxqui-Quitl, F. Villa Manriquez, E. Orozco-Guillen, A. Padilla-Vivanco, and JJ. Sánchez-Escobar, “Detecting jaundice by using digital image processing,” in Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXI, SPIE, Mar. 2014, p. 89491U. doi: 10.1117/12.2041354.

W. Y. Hsu and H. C. Cheng, “A fast and effective system for detection of neonatal jaundice with a dynamic threshold white balance algorithm,” Healthcare (Switzerland), vol. 9, no. 8, Aug. 2021, doi: 10.3390/healthcare9081052.

M. N. Mansor, M. Hariharan, S. N. Basah, and S. Yaacob, “New newborn jaundice monitoring scheme based on combination of pre-processing and color detection method,” Neurocomputing, vol. 120, 2013, doi: 10.1016/j.neucom.2012.10.034.

L. De Greef et al., “BiliCam: Using mobile phones to monitor newborn jaundice,” in UbiComp 2014 - Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Association for Computing Machinery, Inc, 2014, pp. 331–342. doi: 10.1145/2632048.2632076.

A. Aune, G. Vartdal, H. Bergseng, L. L. Randeberg, and E. Darj, “Bilirubin estimates from smartphone images of newborn infants’ skin correlated highly to serum bilirubin levels,” Acta Paediatrica, International Journal of Paediatrics, vol. 109, no. 12, pp. 2532–2538, Dec. 2020, doi: 10.1111/apa.15287.

S. Lingaldinna, K. C. Konda, N. Bapanpally, M. Alimelu, H. Singh, and M. Ramaraju, “Validity of bilirubin measured by biliscan (Smartphone application) in neonatal jaundice – an observational study,” Journal of Nepal Paediatric Society, vol. 41, no. 1, pp. 93–98, 2021, doi: 10.3126/jnps.v40i3.29412.

F. Hardalaç et al., “Classification of neonatal jaundice in a mobile application with noninvasive image processing methods,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 29, no. 4, pp. 2116–2126, 2021, doi: 10.3906/ELK-2008-76.

E. P. Rahayu, M. N. Widyawati, and S. Suryono, “Euclidean distance digital image processing for jaundice detect,” IOP Conf Ser Mater Sci Eng, vol. 1108, no. 1, p. 012022, Mar. 2021, doi: 10.1088/1757-899x/1108/1/012022.

M. S. Jarjees, N. Abdulkadir, and K. Kandla, “Neonatal Jaundice Detection System Using Convolutional Neural Network Algorithm Design and Implementation of Medical Instrument View project,” 2023, doi: 10.14704/nq.2022.20.8.NQ44777.

M. Dwi Anggraeni, A. Fatoni, E. Rahmawati, and I. Nartiningsih, “Estimation of Neonatal Jaundice from the Chest Images Captured with a Smartphone,” 2022. [Online]. Available:

S. Dissaneevate et al., “A Mobile Computer-Aided Diagnosis of Neonatal Hyperbilirubinemia using Digital Image Processing and Machine Learning Techniques,” International Journal of Innovative Research and Scientific Studies, vol. 5, no. 1, pp. 10–17, 2022, doi: 10.53894/ijirss.v5i1.334.

A. A. Al-Naji, “NJN: A Dataset for the Normal and Jaundiced Newborns Coded wireless communication systems over Suzuki fading channels View project Urine Color Analysis for Diseases Detection Based on a Computer Vision System View project,” 2023, doi: 10.20944/preprints202303.0379.v1.

H. V. S. L. Inamanamelluri, V. R. Pulipati, N. C. Pradhan, P. Chintamaneni, M. Manur, and R. Vatambeti, “Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning,” Revue d’Intelligence Artificielle, vol. 37, no. 2, pp. 257–265, Apr. 2023, doi: 10.18280/ria.370202.

F. T. Z. Khanam, A. Al-Naji, A. G. Perera, D. Wang, and J. Chahl, “Non-invasive and non-contact automatic jaundice detection of infants based on random forest,” Comput Methods Biomech Biomed Eng Imaging Vis, 2023, doi: 10.1080/21681163.2023.2244601.

E. Zarehpour et al., BiliBin: An Intelligent Mobile Phone-based Platform to Monitor Newborn Jaundice. 2023. doi: 10.21203/

N. Chidozie Egejuru, A. Onyenonachi Asinobi, O. Adewunmi, T. Aderounmu, S. Ademola Adegoke, and P. Adebayo Idowu, “A Classification Model for Severity of Neonatal Jaundice Using Deep Learning,” American Journal of Pediatrics, vol. 5, no. 3, p. 159, 2019, doi: 10.11648/j.ajp.20190503.24.

P. Adebayo Idowu, N. Chidozie Egejuru, J. Ademola Balogun, and O. Ajibola Sarumi, “Comparative Analysis of Prognostic Model for Risk Classification of Neonatal Jaundice using Machine Learning Algorithms,” 2019. [Online]. Available:

I. Daunhawer et al., “Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning,” Pediatr Res, vol. 86, no. 1, pp. 122–127, Jul. 2019, doi: 10.1038/s41390-019-0384-x.

M. D. Anggraeni, A. Fatoni, and E. Rahmawati, “Prediction of Bilirubin Concentration using Neonatal Forehead Images,” in AIP Conference Proceedings, American Institute of Physics Inc., Nov. 2022. doi: 10.1063/5.0103722.




How to Cite

Abdulkader, B. K., & Aziz, M. H. (2024). Non-invasive Methods for Diagnosing Jaundice in Newborns: A Review. International Journal of Advanced Natural Sciences and Engineering Researches, 8(5), 204–218. Retrieved from

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