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Artificial Intelligence in Pediatrics

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Artificial Intelligence in Medicine

Abstract

Pediatrics is a specialty with significant promise for the application of artificial intelligence (AI) technologies, in part due to the richness of its datasets, with relatively more complete longitudinal records and often less heterogeneous patterns of disease compared to adult medicine. Despite considerable overlap with adult medicine, pediatrics presents a distinct set of clinical problems to solve. It is tempting to assume that AI tools developed for adults will easily translate to the pediatric population, where in reality this is unlikely to be the case. The challenges involved in the development of AI tools for healthcare are unfortunately exacerbated in pediatrics, and the implementation gap between how these systems are developed and the setting in which they will be deployed is a real challenge for the next decade. Robust evaluation through high quality clinical study design and clear reporting standards will be essential. This chapter reviews recent work to develop artificial intelligence solutions in pediatrics, including developments across cardiology, respiratory, gastroenterology, neonatology, genetics, endocrinology, ophthalmology, radiology, pediatric intensive care, and radiology specialties. We conclude that AI presents an exciting opportunity to transform aspects of pediatrics at a global scale, democratizing access to subspecialist diagnostic skills, improving quality and efficiency of care, enabling global access to healthcare through sensor-rich Internet-connected mobile devices, and enhancing imaging acquisition to reduce radiation while improving speed and quality. The ultimate challenge will be for pediatricians to find ways to deploy these novel technologies into clinical practice in a way that is safe, effective, and equitable and that ultimately improves outcomes for children.

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References

  1. Sugiyama K, Hasegawa Y. Computer assisted medical diagnosis system for inborn errors of metabolism. Jpn J Med Electron Biol Eng. 1984;22:942–3.

    Google Scholar 

  2. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25:30–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.

    Article  CAS  PubMed  Google Scholar 

  4. Alqahtani FF, Messina F, Offiah AC. Are semi-automated software program designed for adults accurate for the identification of vertebral fractures in children? Eur Radiol. 2019;29:6780–9.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Hoodbhoy Z, Masroor Jeelani S, Aziz A, Habib MI, Iqbal B, Akmal W, Siddiqui K, Hasan B, Leeflang M, Das JK. Machine learning for child and adolescent health: a systematic review. Pediatrics. 2021. https://doi.org/10.1542/peds.2020-011833.

  6. Khazaei H, Mench-Bressan N, McGregor C, Pugh JE. Health informatics for neonatal intensive care units: an analytical modeling perspective. IEEE J Transl Eng Health Med. 2015;3:3000109.

    Article  PubMed  Google Scholar 

  7. Nations U, United Nations. World population prospects 2019: highlights. Statistical Papers – United Nations (Ser A), Population and Vital Statistics Report. 2019. https://doi.org/10.18356/13bf5476-en.

  8. Chang A. Artificial intelligence in pediatric cardiology and cardiac surgery: irrational hype or paradigm shift? Ann Pediatr Cardiol. 2019;12:191.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Gaffar S, Gearhart AS, Chang AC. The next frontier in pediatric cardiology: artificial intelligence. Pediatr Clin N Am. 2020;67:995–1009.

    Article  Google Scholar 

  10. Sacks LD, Axelrod DM. Virtual reality in pediatric cardiology. Curr Opin Cardiol. 2020;35:37–41.

    Article  PubMed  Google Scholar 

  11. Plasencia JD, Kamarianakis Y, Ryan JR, et al. Alternative methods for virtual heart transplant-size matching for pediatric heart transplantation with and without donor medical images available. Pediatr Transplant. 2018;22:e13290.

    Article  PubMed  Google Scholar 

  12. Petitjean C, Dacher J-N. A review of segmentation methods in short axis cardiac MR images. Med Image Anal. 2011;15:169–84.

    Article  PubMed  Google Scholar 

  13. Pace DF, Dalca AV, Geva T, Powell AJ, Moghari MH, Golland P. Interactive whole-heart segmentation in congenital heart disease. Med Image Comput Comput Assist Interv. 2015;9351:80–8.

    PubMed  PubMed Central  Google Scholar 

  14. Yu L, Yang X, Qin J, Heng P-A. 3D FractalNet: dense volumetric segmentation for cardiovascular MRI volumes. In: Reconstruction, segmentation, and analysis of medical images. Springer International Publishing; 2017. p. 103–10.

    Chapter  Google Scholar 

  15. Mukhopadhyay A. Total variation random forest: fully automatic MRI segmentation in congenital heart diseases. In: Reconstruction, segmentation, and analysis of medical images. Springer International Publishing; 2017. p. 165–71.

    Chapter  Google Scholar 

  16. Pace DF, Dalca AV, Brosch T, Geva T, Powell AJ, Weese J, Moghari MH, Golland P. Iterative segmentation from limited training data: applications to congenital heart disease. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support. 2018;11045:334–42.

    Article  Google Scholar 

  17. Rezaei M, Yang H, Meinel C. Whole heart and great vessel segmentation with context-aware of generative adversarial networks. In: Bildverarbeitung für die Medizin 2018. Berlin/Heidelberg: Springer Vieweg; 2018. p. 353–8.

    Chapter  Google Scholar 

  18. Bhatikar SR, DeGroff C, Mahajan RL. A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics. Artif Intell Med. 2005;33:251–60.

    Article  PubMed  Google Scholar 

  19. Latif S, Usman M, Rana R, Qadir J. Phonocardiographic sensing using deep learning for abnormal heartbeat detection. IEEE Sensors J. 2018;18:9393–400.

    Article  Google Scholar 

  20. Kucharski D, Grochala D, Kajor M, Kańtoch E. A deep learning approach for valve defect recognition in heart acoustic signal. information systems architecture and technology. In: Proceedings of 38th international conference on information systems architecture and technology – ISAT 2017, 3–14. 2018.

    Google Scholar 

  21. Liu C, Springer D, Li Q, et al. An open access database for the evaluation of heart sound algorithms. Physiol Meas. 2016;37:2181–213.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zühlke L, Myer L, Mayosi BM. The promise of computer-assisted auscultation in screening for structural heart disease and clinical teaching. Cardiovasc J Afr. 2012;23:405–8.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Baumgartner CF, Kamnitsas K, Matthew J, Fletcher TP, Smith S, Koch LM, Kainz B, Rueckert D. SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans Med Imaging. 2017;36:2204–15.

    Article  PubMed  Google Scholar 

  24. Dong J, Liu S, Liao Y, Wen H, Lei B, Li S, Wang T. A generic quality control framework for fetal ultrasound cardiac four-chamber planes. IEEE J Biomed Health Inform. 2020;24:931–42.

    Article  PubMed  Google Scholar 

  25. Baumgartner CF, Kamnitsas K, Matthew J, Smith S, Kainz B, Rueckert D. Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks. In: Medical image computing and computer-assisted intervention – MICCAI. Springer International Publishing; 2016. p. 203–11.

    Chapter  Google Scholar 

  26. Wang J, Liu X, Wang F, Zheng L, Gao F, Zhang H, Zhang X, Xie W, Wang B. Automated interpretation of congenital heart disease from multi-view echocardiograms. Med Image Anal. 2021;69:101942.

    Article  PubMed  Google Scholar 

  27. Le TK, Truong V, Nguyen-Vo T-H, et al. Application of machine learning in screening of congenital heart diseases using fetal echocardiography. J Am Coll Cardiol. 2020;75:648.

    Article  Google Scholar 

  28. Arnaout R, Curran L, Zhao Y, Levine JC, Chinn E, Moon-Grady AJ. Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning. medRxiv 2020.06.22.20137786. 2020.

    Google Scholar 

  29. Ruiz-Fernández D, Monsalve Torra A, Soriano-Payá A, Marín-Alonso O, Triana Palencia E. Aid decision algorithms to estimate the risk in congenital heart surgery. Comput Methods Prog Biomed. 2016;126:118–27.

    Article  Google Scholar 

  30. Ferrante G, Licari A, Marseglia GL, La Grutta S. Artificial intelligence as an emerging diagnostic approach in paediatric pulmonology. Respirology. 2020;25:1029–30.

    Article  PubMed  Google Scholar 

  31. Mahomed N, van Ginneken B, Philipsen RHHM, Melendez J, Moore DP, Moodley H, Sewchuran T, Mathew D, Madhi SA. Computer-aided diagnosis for World Health Organization-defined chest radiograph primary-endpoint pneumonia in children. Pediatr Radiol. 2020;50:482–91.

    Article  PubMed  Google Scholar 

  32. Naydenova E, Tsanas A, Casals-Pascual C, De Vos M. Smart diagnostic algorithms for automated detection of childhood pneumonia in resource-constrained settings. 2015 IEEE Global Humanitarian Technology Conference (GHTC). 2015. https://doi.org/10.1109/ghtc.2015.7344000.

  33. Correa M, Zimic M, Barrientos F, et al. Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition. PLoS One. 2018;13:e0206410.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Parker D, Picone J, Harati A, Lu S, Jenkyns MH, Polgreen PM. Detecting paroxysmal coughing from pertussis cases using voice recognition technology. PLoS One. 2013;8:e82971.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Sharan RV, Abeyratne UR, Swarnkar VR, Porter P. Automatic croup diagnosis using cough sound recognition. IEEE Trans Biomed Eng. 2019;66:485–95.

    Article  PubMed  Google Scholar 

  36. Boner AL, Piacentini GL, Peroni DG, Irving CS, Goldstein D, Gavriely N, Godfrey S. Children with nocturnal asthma wheeze intermittently during sleep. J Asthma. 2010;47:290–4.

    Article  PubMed  Google Scholar 

  37. Habukawa C, Ohgami N, Matsumoto N, Hashino K, Asai K, Sato T, Murakami K. A wheeze recognition algorithm for practical implementation in children. PLoS One. 2020;15:e0240048.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Prodhan P, Dela Rosa RS, Shubina M, Haver KE, Matthews BD, Buck S, Kacmarek RM, Noviski NN. Wheeze detection in the pediatric intensive care unit: comparison among physician, nurses, respiratory therapists, and a computerized respiratory sound monitor. Respir Care. 2008;53:1304–9.

    PubMed  Google Scholar 

  39. Huffaker MF, Carchia M, Harris BU, Kethman WC, Murphy TE, Sakarovitch CCD, Qin F, Cornfield DN. Passive nocturnal physiologic monitoring enables early detection of exacerbations in children with asthma. A proof-of-concept study. Am J Respir Crit Care Med. 2018;198:320–8.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Luo G, Stone BL, Fassl B, Maloney CG, Gesteland PH, Yerram SR, Nkoy FL. Predicting asthma control deterioration in children. BMC Med Inform Decis Mak. 2015;15:84.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Nathan RA, Sorkness CA, Kosinski M, Schatz M, Li JT, Marcus P, Murray JJ, Pendergraft TB. Development of the asthma control test☆A survey for assessing asthma control. J Allergy Clin Immunol. 2004;113:59–65.

    Article  PubMed  Google Scholar 

  42. Hirai K, Enseki M, Tabata H, Nukaga M, Matsuda S, Kato M, Furuya H, Mochizuki H. Objective measurement of frequency and pattern of nocturnal cough in children with asthma exacerbation. Ann Allergy Asthma Immunol. 2016;117:169–74.

    Article  PubMed  Google Scholar 

  43. Mathews TJ, Driscoll AK. Trends in Infant Mortality in the United States, 2005–2014. NCHS Data Brief. 2017;279:1–8.

    Google Scholar 

  44. Kyu HH, Stein CE, Boschi Pinto C, et al. Causes of death among children aged 5–14 years in the WHO European Region: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Child Adolesc Health. 2018;2:321–37.

    Article  PubMed  PubMed Central  Google Scholar 

  45. French CE, Delon I, Dolling H, et al. Whole genome sequencing reveals that genetic conditions are frequent in intensively ill children. Intensive Care Med. 2019;45:627–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wojcik MH, Schwartz TS, Thiele KE, et al. Infant mortality: the contribution of genetic disorders. J Perinatol. 2019;39:1611–9.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Gurovich Y, Hanani Y, Bar O, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med. 2019;25:60–4.

    Article  CAS  PubMed  Google Scholar 

  48. Clark MM, Hildreth A, Batalov S, et al. Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Sci Transl Med. 2019. https://doi.org/10.1126/scitranslmed.aat6177.

  49. Genome Sequencing. 2015. https://www.genomicsengland.co.uk/understanding-genomics/genome-sequencing/. Accessed 15 Mar 2021.

  50. National Human Genome Research Institute DNA Sequencing Costs: Data. https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data. Accessed 15 Mar 2021.

  51. Poplin R, Chang P-C, Alexander D, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36:983–7.

    Article  CAS  PubMed  Google Scholar 

  52. Sundaram L, Gao H, Padigepati SR, et al. Predicting the clinical impact of human mutation with deep neural networks. Nat Genet. 2018;50:1161–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Supernat A, Vidarsson OV, Steen VM, Stokowy T. Comparison of three variant callers for human whole genome sequencing. Sci Rep. 2018;8:17851.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Mastrototaro JJ. The MiniMed continuous glucose monitoring system. Diabetes Technol Ther. 2000;2(Suppl 1):S13–8.

    Article  PubMed  Google Scholar 

  55. Bode BW. Clinical utility of the continuous glucose monitoring system. Diabetes Technol Ther. 2000;2(Suppl 1):S35–41.

    Article  PubMed  Google Scholar 

  56. Tyler NS, Mosquera-Lopez CM, Wilson LM, et al. An artificial intelligence decision support system for the management of type 1 diabetes. Nat Metabolism. 2020;2:612–9.

    Article  Google Scholar 

  57. Nimri R, NextDREAM Consortium, Battelino T, Laffel LM, Slover RH, Schatz D, Weinzimer SA, Dovc K, Danne T, Phillip M. Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Nat Med. 2020;26:1380–4.

    Article  CAS  PubMed  Google Scholar 

  58. Elleri D, Dunger DB, Hovorka R. Closed-loop insulin delivery for treatment of type 1 diabetes. BMC Med. 2011;9:120.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Elleri D, Allen JM, Biagioni M, et al. Evaluation of a portable ambulatory prototype for automated overnight closed-loop insulin delivery in young people with type 1 diabetes. Pediatr Diabetes. 2012;13:449–53.

    Article  CAS  PubMed  Google Scholar 

  60. Phillip M, Battelino T, Atlas E, et al. Nocturnal glucose control with an artificial pancreas at a diabetes camp. N Engl J Med. 2013;368:824–33.

    Article  CAS  PubMed  Google Scholar 

  61. Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res. 2018;20:e10775.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Bekiari E, Kitsios K, Thabit H, Tauschmann M, Athanasiadou E, Karagiannis T, Haidich A-B, Hovorka R, Tsapas A. Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis. BMJ. 2018;361:k1310.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Breton MD, Beck RW, Wadwa RP, iDCL Trial Research Group. A randomized trial of closed-loop control in children with type 1 diabetes. Reply. N Engl J Med. 2020;383:2484.

    Article  PubMed  Google Scholar 

  64. Bothe MK, Dickens L, Reichel K, Tellmann A, Ellger B, Westphal M, Faisal AA. The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas. Expert Rev Med Devices. 2013;10:661–73.

    Article  CAS  PubMed  Google Scholar 

  65. de Canete JF, Gonzalez-Perez S, Ramos-Diaz JC. Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes. Comput Methods Prog Biomed. 2012;106:55–66.

    Article  Google Scholar 

  66. Edwards EM, Ehret DEY, Soll RF, Horbar JD. Vermont Oxford Network: a worldwide learning community. Transl Pediatr. 2019;8:182–92.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Modi N. Information technology infrastructure, quality improvement and research: the UK National Neonatal Research Database. Transl Pediatr. 2019;8:193–8.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Goel N, Shrestha S, Smith R, et al. Screening for early onset neonatal sepsis: NICE guidance-based practice versus projected application of the Kaiser Permanente sepsis risk calculator in the UK population. Arch Dis Child Fetal Neonatal Ed. 2020;105:118–22.

    Article  PubMed  Google Scholar 

  69. Kuzniewicz MW, Puopolo KM, Fischer A, Walsh EM, Li S, Newman TB, Kipnis P, Escobar GJ. A quantitative, risk-based approach to the management of neonatal early-onset sepsis. JAMA Pediatr. 2017;171:365–71.

    Article  PubMed  Google Scholar 

  70. Cailes B, Kortsalioudaki C, Buttery J, Pattnayak S, Greenough A, Matthes J, Bedford Russell A, Kennea N, Heath PT, neonIN network. Epidemiology of UK neonatal infections: the neonIN infection surveillance network. Arch Dis Child Fetal Neonatal Ed. 2018;103:F547–53.

    Article  PubMed  Google Scholar 

  71. Puopolo KM, Draper D, Wi S, Newman TB, Zupancic J, Lieberman E, Smith M, Escobar GJ. Estimating the probability of neonatal early-onset infection on the basis of maternal risk factors. Pediatrics. 2011;128:e1155–63.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Escobar GJ, Puopolo KM, Wi S, Turk BJ, Kuzniewicz MW, Walsh EM, Newman TB, Zupancic J, Lieberman E, Draper D. Stratification of risk of early-onset sepsis in newborns ≥ 34 weeks’ gestation. Pediatrics. 2014;133:30–6.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Pettinger KJ, Mayers K, McKechnie L, Phillips B. Sensitivity of the Kaiser Permanente early-onset sepsis calculator: a systematic review and meta-analysis. EClin Med. 2020;19:100227.

    Google Scholar 

  74. Taylor JA, Burgos AE, Flaherman V, Chung EK, Simpson EA, Goyal NK, Von Kohorn I, Dhepyasuwan N, Better Outcomes through Research for Newborns Network. Discrepancies between transcutaneous and serum bilirubin measurements. Pediatrics. 2015;135:224–31.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Maisels MJ, Bhutani VK, Bogen D, Newman TB, Stark AR, Watchko JF. Hyperbilirubinemia in the newborn infant >=35 weeks’ gestation: an update with clarifications. Pediatrics. 2009;124:1193–8.

    Article  PubMed  Google Scholar 

  76. Moyer VA, Ahn C, Sneed S. Accuracy of clinical judgment in neonatal jaundice. Arch Pediatr Adolesc Med. 2000;154:391–4.

    Article  CAS  PubMed  Google Scholar 

  77. NICE. Neonatal jaundice – clinical guideline. CG98. 2010. https://www.nice.org.uk/guidance/cg98/evidence/full-guideline-pdf-245411821

  78. Outlaw F, Nixon M, Odeyemi O, MacDonald LW, Meek J, Leung TS. Smartphone screening for neonatal jaundice via ambient-subtracted sclera chromaticity. PLoS One. 2020;15:e0216970.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Taylor JA, Stout JW, de Greef L, et al. Use of a smartphone app to assess neonatal jaundice. Pediatrics. 2017. https://doi.org/10.1542/peds.2017-0312.

  80. Rizvi MR, Alaskar FM, Albaradie RS, Rizvi NF, Al-Abdulwahab K. A novel non-invasive technique of measuring bilirubin levels using bilicapture. Oman Med J. 2019;34:26–33.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Munkholm SB, Krøgholt T, Ebbesen F, Szecsi PB, Kristensen SR. The smartphone camera as a potential method for transcutaneous bilirubin measurement. PLoS One. 2018;13:e0197938.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Bhutani VK, Zipursky A, Blencowe H, et al. Neonatal hyperbilirubinemia and Rhesus disease of the newborn: incidence and impairment estimates for 2010 at regional and global levels. Pediatr Res. 2013;74(Suppl 1):86–100.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–10.

    Article  PubMed  Google Scholar 

  84. Chiang MF, Jiang L, Gelman R, Du YE, Flynn JT. Interexpert agreement of plus disease diagnosis in retinopathy of prematurity. Arch Ophthalmol. 2007;125:875–80.

    Article  PubMed  Google Scholar 

  85. Fleck BW, BOOST II Retinal Image Digital Analysis (RIDA) Group, Williams C, et al. An international comparison of retinopathy of prematurity grading performance within the Benefits of Oxygen Saturation Targeting II trials. Eye. 2018;32:74–80.

    Article  CAS  PubMed  Google Scholar 

  86. Worrall DE, Wilson CM, Brostow GJ. Automated retinopathy of prematurity case detection with convolutional neural networks. In: Deep learning and data labeling for medical applications. Springer International Publishing; 2016. p. 68–76.

    Chapter  Google Scholar 

  87. Brown JM, Campbell JP, Beers A, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol. 2018;136:803–10.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Hu J, Chen Y, Zhong J, Ju R, Yi Z. Automated analysis for retinopathy of prematurity by deep neural networks. IEEE Trans Med Imaging. 2019;38:269–79.

    Article  PubMed  Google Scholar 

  89. Taylor S, Brown JM, Gupta K, et al. Monitoring disease progression with a quantitative severity scale for retinopathy of prematurity using deep learning. JAMA Ophthalmol. 2019. https://doi.org/10.1001/jamaophthalmol.2019.2433.

  90. Wang B, Xiao L, Liu Y, Wang J, Liu B, Li T, Ma X, Zhao Y. Application of a deep convolutional neural network in the diagnosis of neonatal ocular fundus hemorrhage. Biosci Rep. 2018. https://doi.org/10.1042/bsr20180497.

  91. Liu X, Jiang J, Zhang K, et al. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PLoS One. 2017;12:e0168606.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Chan J, Raju S, Nandakumar R, Bly R, Gollakota S. Detecting middle ear fluid using smartphones. Sci Transl Med. 2019;11:eaav1102. https://doi.org/10.1126/scitranslmed.aav1102.

    Article  PubMed  Google Scholar 

  93. Mannino RG, Myers DR, Tyburski EA, Caruso C, Boudreaux J, Leong T, Clifford GD, Lam WA. Smartphone app for non-invasive detection of anemia using only patient-sourced photos. Nat Commun. 2018;9:4924.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Muñoz-Organero M, Powell L, Heller B, Harpin V, Parker J. Using recurrent neural networks to compare movement patterns in ADHD and normally developing children based on acceleration signals from the wrist and ankle. Sensors. 2019;19:2935. https://doi.org/10.3390/s19132935.

    Article  PubMed Central  Google Scholar 

  95. Liang H, Tsui BY, Ni H, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med. 2019;25:433–8.

    Article  CAS  PubMed  Google Scholar 

  96. Rajpurkar P, Irvin J, Ball RL, et al. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018;15:e1002686.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Li Q, Zhong L, Huang H, et al. Auxiliary diagnosis of developmental dysplasia of the hip by automated detection of Sharp’s angle on standardized anteroposterior pelvic radiographs. Medicine. 2019;98:e18500.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Thian YL, Li Y, Jagmohan P, Sia D, Chan VEY, Tan RT. Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Radiol: Artif Intell. 2019;1:e180001.

    Google Scholar 

  99. England JR, Gross JS, White EA, Patel DB, England JT, Cheng PM. Detection of traumatic pediatric elbow joint effusion using a deep convolutional neural network. Am J Roentgenol. 2018;211:1361–8.

    Article  Google Scholar 

  100. Zheng Q, Furth SL, Tasian GE, Fan Y. Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features. J Pediatr Urol. 2019;15:75.e1–7.

    Article  Google Scholar 

  101. Tong C, Liang B, Li J, Zheng Z. A deep automated skeletal bone age assessment model with heterogeneous features learning. J Med Syst. 2018;42:249.

    Article  PubMed  Google Scholar 

  102. Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018;287:313–22.

    Article  PubMed  Google Scholar 

  103. Mutasa S, Chang PD, Ruzal-Shapiro C, Ayyala R. MABAL: a novel deep-learning architecture for machine-assisted bone age labeling. J Digit Imaging. 2018;31:513–9.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Spampinato C, Palazzo S, Giordano D, Aldinucci M, Leonardi R. Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal. 2017;36:41–51.

    Article  CAS  PubMed  Google Scholar 

  105. Kim JR, Shim WH, Yoon HM, Hong SH, Lee JS, Cho YA, Kim S. Computerized bone age estimation using deep learning based program: evaluation of the accuracy and efficiency. AJR Am J Roentgenol. 2017;209:1374–80.

    Article  PubMed  Google Scholar 

  106. Thodberg HH, Sävendahl L. Validation and reference values of automated bone age determination for four ethnicities. Acad Radiol. 2010;17:1425–32.

    Article  PubMed  Google Scholar 

  107. Berst MJ, Dolan L, Bogdanowicz MM, Stevens MA, Chow S, Brandser EA. Effect of knowledge of chronologic age on the variability of pediatric bone age determined using the Greulich and Pyle standards. Am J Roentgenol. 2001;176:507–10.

    Article  CAS  Google Scholar 

  108. Thodberg HH, Kreiborg S, Juul A, Pedersen KD. The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging. 2009;28:52–66.

    Article  PubMed  Google Scholar 

  109. Ceschin R, Zahner A, Reynolds W, Gaesser J, Zuccoli G, Lo CW, Gopalakrishnan V, Panigrahy A. A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D convolutional neural networks. NeuroImage. 2018;178:183–97.

    Article  PubMed  Google Scholar 

  110. Guo Y, Wu G, Commander LA, Szary S, Jewells V, Lin W, Shent D. Segmenting hippocampus from infant brains by sparse patch matching with deep-learned features. Med Image Comput Comput Assist Interv. 2014;17:308–15.

    PubMed  PubMed Central  Google Scholar 

  111. Dolz J, Desrosiers C, Wang L, Yuan J, Shen D, Ben Ayed I. Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation. Comput Med Imaging Graph. 2020;79:101660.

    Article  PubMed  Google Scholar 

  112. Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, Shen D. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage. 2015;108:214–24.

    Article  PubMed  Google Scholar 

  113. Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJNL, Isgum I. Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging. 2016;35:1252–61.

    Article  PubMed  Google Scholar 

  114. Nie D, Wang L, Gao Y, Shen D. Fully convolutional networks for multi-modality isointense infant brain image segmentation. Proc IEEE Int Symp Biomed Imaging. 2016;2016:1342–5.

    PubMed  PubMed Central  Google Scholar 

  115. Nguyen XV, Oztek MA, Nelakurti DD, Brunnquell CL, Mossa-Basha M, Haynor DR, Prevedello LM. Applying artificial intelligence to mitigate effects of patient motion or other complicating factors on image quality. Top Magn Reson Imaging. 2020;29:175–80.

    Article  PubMed  Google Scholar 

  116. Wang S, Su Z, Ying L, Peng X, Zhu S, Liang F, Feng D, Liang D. Accelerating magnetic resonance imaging via deep learning. Proc IEEE Int Symp Biomed Imaging. 2016;2016:514–7.

    PubMed  PubMed Central  Google Scholar 

  117. Chen H, Zhang Y, Kalra MK, Lin F, Chen Y, Liao P, Zhou J, Wang G. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging. 2017;36:2524–35.

    Article  PubMed  PubMed Central  Google Scholar 

  118. Gong E, Pauly JM, Wintermark M, Zaharchuk G. Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI. J Magn Reson Imaging. 2018;48:330–40.

    Article  PubMed  Google Scholar 

  119. Kamaleswaran R, Akbilgic O, Hallman MA, West AN, Davis RL, Shah SH. Applying artificial intelligence to identify physiomarkers predicting severe sepsis in the PICU. Pediatr Crit Care Med. 2018;19:e495–503.

    Article  PubMed  Google Scholar 

  120. Fernández IS, Sansevere AJ, Gaínza-Lein M, Kapur K, Loddenkemper T. Machine learning for outcome prediction in electroencephalograph (EEG)-monitored children in the intensive care unit. J Child Neurol. 2018;33:546–53.

    Article  Google Scholar 

  121. Zhai H, Brady P, Li Q, Lingren T, Ni Y, Wheeler DS, Solti I. Developing and evaluating a machine learning based algorithm to predict the need of pediatric intensive care unit transfer for newly hospitalized children. Resuscitation. 2014;85:1065–71.

    Article  PubMed  PubMed Central  Google Scholar 

  122. Kennedy CE, Aoki N, Mariscalco M, Turley JP. Using time series analysis to predict cardiac arrest in a PICU. Pediatr Crit Care Med. 2015;16:e332–9.

    Article  PubMed  PubMed Central  Google Scholar 

  123. Weiss SL, for the SPROUT Study Investigators and Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network, Fitzgerald JC, et al. Discordant identification of pediatric severe sepsis by research and clinical definitions in the SPROUT international point prevalence study. Crit Care. 2015;19:325. https://doi.org/10.1186/s13054-015-1055-x.

    Article  PubMed  PubMed Central  Google Scholar 

  124. Mossotto E, Ashton JJ, Coelho T, Beattie RM, MacArthur BD, Ennis S. Classification of paediatric inflammatory bowel disease using machine learning. Sci Rep. 2017;7:2427.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Goldberg JE, Rosenkrantz AB. Artificial intelligence and radiology: a social media perspective. Curr Probl Diagn Radiol. 2019;48:308–11.

    Article  PubMed  Google Scholar 

  126. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  127. Rivera SC, The SPIRIT-AI and CONSORT-AI Working Group, Liu X, Chan A-W, Denniston AK, Calvert MJ, SPIRIT-AI and CONSORT-AI Steering Group, SPIRIT-AI and CONSORT-AI Consensus Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020;26:1351–63.

    Article  CAS  Google Scholar 

  128. Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK, SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020;26:1364–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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  1. Google Health, London, UK

    Christopher J. Kelly, Alexander P. Y. Brown & James A. Taylor

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  1. Christopher J. Kelly
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  2. Alexander P. Y. Brown
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  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Faculty of Medicine, Imperial College London, London, UK

    Hutan Ashrafian

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Kelly, C.J., Brown, A.P.Y., Taylor, J.A. (2021). Artificial Intelligence in Pediatrics. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_316-1

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  • DOI: https://doi.org/10.1007/978-3-030-58080-3_316-1

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