Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns

Abstract

Necrotising enterocolitis (NEC) is one of the most common diseases in neonates and predominantly affects premature or very-low-birth-weight infants. Diagnosis is difficult and needed in hours since the first symptom onset for the best therapeutic effects. Artificial intelligence (AI) may play a significant role in NEC diagnosis. A literature search on the use of AI in the diagnosis of NEC was performed. Four databases (PubMed, Embase, arXiv, and IEEE Xplore) were searched with the appropriate MeSH terms. The search yielded 118 publications that were reduced to 8 after screening and checking for eligibility. Of the eight, five used classic machine learning (ML), and three were on the topic of deep ML. Most publications showed promising results. However, no publications with evident clinical benefits were found. Datasets used for training and testing AI systems were small and typically came from a single institution. The potential of AI to improve the diagnosis of NEC is evident. The body of literature on this topic is scarce, and more research in this area is needed, especially with a focus on clinical utility. Cross-institutional data for the training and testing of AI algorithms are required to make progress in this area.

Impact

  • Only a few publications on the use of AI in NEC diagnosis are available although they offer some evidence that AI may be helpful in NEC diagnosis.

  • AI requires large, multicentre, and multimodal datasets of high quality for model training and testing. Published results in the literature are based on data from single institutions and, as such, have limited generalisability.

  • Large multicentre studies evaluating broad datasets are needed to evaluate the true potential of AI in diagnosing NEC in a clinical setting.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Clinical and radiographic features of necrotising enterocolitis.
Fig. 2: Flow diagram of the study selection process.

References

  1. Kuzma-O’Reilly, B. et al. Evaluation, development, and implementation of potentially better practices in neonatal intensive care nutrition. Pediatrics 111, e461–e470 (2003).

    Article  Google Scholar 

  2. Malin, S. W., Bhutani, V. K., Ritchie, W. W., Hall, M. L. & Paul, D. Echogenic intravascular and hepatic microbubbles associated with necrotizing enterocolitis. J. Pediatr. 103, 637–640 (1983).

    Article  CAS  Google Scholar 

  3. Neu, J. & Walker, W. A. Necrotizing enterocolitis. N. Engl. J. Med. 364, 255–264 (2011).

    Article  CAS  Google Scholar 

  4. Epelman, M. et al. Necrotizing enterocolitis: review of state-of-the-art imaging findings with pathologic correlation. RadioGraphics 27, 285–305 (2007).

    Article  Google Scholar 

  5. Walsh, M. C. & Kliegman, R. M. Necrotizing enterocolitis: treatment based on staging criteria. Pediatr. Clin. North Am. 33, 179–201 (1986).

    Article  CAS  Google Scholar 

  6. Merritt, C., Goldsmith, J. & Sharp, M. Sonographic detection of portal venous gas in infants with necrotizing enterocolitis. Am. J. Roentgenol. 143, 1059–1062 (1984).

    Article  CAS  Google Scholar 

  7. Leonidas, J. C., Hall, R. T. & Amoury, R. A. Critical evaluation of the roentgen signs of neonatal necrotizing enterocolitis. Ann. Radio. (Paris) 19, 123–132 (1976).

    CAS  Google Scholar 

  8. Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).

    Article  CAS  Google Scholar 

  9. Sitek, A. et al. Artificial intelligence in PET. PET Clin. 16, 483–492 (2021).

    Article  Google Scholar 

  10. Soofi, A. A. & Awan, A. Classification techniques in machine learning: applications and issues. J. Basic Appl. Sci. 13, 459–465 (2017).

    Article  Google Scholar 

  11. Wiering, M. & van Otterlo, M. (eds) Reinforcement Learning (Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, Accessed 28 March 2022). http://link.springer.com/10.1007/978-3-642-27645-3.

  12. Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (eds Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q.) (Curran Associates, Inc., 2012). https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf.

  13. Vaswani, A. et al. Attention is all you need. In: Advances in Neural Information Processing Systems (eds Guyon, I. et al.) (Curran Associates, Inc., 2017). https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.

  14. Mueller, M., Taylor, S. N., Wagner, C. L. & Almeida, J. S. Using an artificial neural network to predict necrotizing enterocolitis in premature infants. In: 2009 International Joint Conference on Neural Networks 2172–2175 (IEEE, Atlanta, GA, USA, 2009, Accessed 28 March 2022). http://ieeexplore.ieee.org/document/5178635/.

  15. Ntonfo, G. M. K., Frize, M. & Bariciak, E. Detection of necrotizing enterocolitis in newborns using abdominal thermal signature analysis. In: 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings 36–39 (IEEE, Torino, Italy, 2015, Accessed 28 March 2022). http://ieeexplore.ieee.org/document/7145168/.

  16. Irles, C. et al. Estimation of neonatal intestinal perforation associated with necrotizing enterocolitis by machine learning reveals new key factors. Int J. Environ. Res. Public Health 15, 2509 (2018).

    Article  CAS  Google Scholar 

  17. Lure, A. C. et al. Using machine learning analysis to assist in differentiating between necrotizing enterocolitis and spontaneous intestinal perforation: a novel predictive analytic tool. J. Pediatr. Surg. 56, 1703–1710 (2021).

    Article  Google Scholar 

  18. Lueschow, S. R., Boly, T. J., Jasper, E., Patel, R. M. & McElroy, S. J. A critical evaluation of current definitions of necrotizing enterocolitis. Pediatr. Res. 91, 590–597 (2022).

    Article  Google Scholar 

  19. Bell, M. J. et al. Neonatal necrotizing enterocolitis: therapeutic decisions based upon clinical staging. Ann. Surg. 187, 1–7 (1978).

    Article  CAS  Google Scholar 

  20. Battersby, C., Longford, N., Costeloe, K. & Modi, N., for the UK Neonatal Collaborative Necrotising Enterocolitis Study Group. Development of a gestational age–specific case definition for neonatal necrotizing enterocolitis. JAMA Pediatr. 171, 256 (2017).

    Article  Google Scholar 

  21. Gephart, S. M. et al. Changing the paradigm of defining, detecting, and diagnosing NEC: perspectives on Bell’s stages and biomarkers for NEC. Semin. Pediatr. Surg. 27, 3–10 (2018).

    Article  Google Scholar 

  22. van Druten, J., Sharif, M. S., Chan, S. S., Chong, C. & Abdalla, H. A deep learning based suggested model to detect necrotising enterocolitis in abdominal radiography images. In: 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE) 118–123 (IEEE, London, United Kingdom, 2019, Accessed 28 March 2022). https://ieeexplore.ieee.org/document/8941615/.

  23. Gao, W. et al. Multimodal AI system for the rapid diagnosis and surgical prediction of necrotizing enterocolitis. IEEE Access 9, 51050–51064 (2021).

    Article  Google Scholar 

  24. Lin, Y. C., Salleb-Aouissi, A. & Hooven, T. A. Interpretable prediction of necrotizing enterocolitis from machine learning analysis of premature infant stool microbiota. BMC Bioinforma. 23, 104 (2022).

    Article  CAS  Google Scholar 

  25. Endo, S. et al. Association of maternal factors with perinatal complications in pregnancies complicated with diabetes: a single-center retrospective analysis. J. Clin. Med. 7, 5 (2018).

    Article  Google Scholar 

  26. Liu, B. et al. Predicting pregnancy using large-scale data from a women’s health tracking mobile application. In: The World Wide Web Conference on – WWW ’19 2999–3005 (ACM Press, San Francisco, CA, USA, 2019, Accessed 28 March 2022). http://dl.acm.org/citation.cfm?doid=3308558.3313512.

  27. Goyal, A., Kuchana, M. & Ayyagari, K. P. R. Machine learning predicts live-birth occurrence before in-vitro fertilization treatment. Sci. Rep. 10, 20925 (2020).

    Article  CAS  Google Scholar 

  28. Khosravi, P. et al. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. Npj Digit Med. 2, 21 (2019).

    Article  Google Scholar 

  29. Gao, C. et al. Deep learning predicts extreme preterm birth from electronic health records. J. Biomed. Inf. 100, 103334 (2019).

    Article  Google Scholar 

  30. Włodarczyk, T. et al. Spontaneous preterm birth prediction using convolutional neural networks. In: Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis (eds Hu, Y. et al.) 274–283 (Springer International Publishing, Cham, 2020, Accessed 28 March 2022). https://link.springer.com/10.1007/978-3-030-60334-2_27.

  31. Włodarczyk, T. et al. Machine learning methods for preterm birth prediction: a review. Electronics 10, 586 (2021).

    Article  Google Scholar 

  32. Płotka, S. et al. FetalNet: multi-task deep learning framework for fetal ultrasound biometric measurements. In: Neural Information Processing (eds Mantoro, T., Lee, M., Ayu, M. A., Wong, K. W. & Hidayanto, A. N.) 257–265 (Springer International Publishing, Cham, 2021, Accessed 28 March 2022). https://link.springer.com/10.1007/978-3-030-92310-5_30.

  33. Płotka, S. et al. Deep learning fetal ultrasound video model match human observers in biometric measurements. Phys. Med. Biol. 67, 045013 (2022).

    Article  Google Scholar 

  34. Bano, S. et al. AutoFB: automating fetal biometry estimation from standard ultrasound planes. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (eds de Bruijne, M. et al.) 228–238 (Springer International Publishing, Cham, 2021, Accessed 28 March 2022). https://link.springer.com/10.1007/978-3-030-87234-2_22.

  35. Budd, S. et al. Detecting hypo-plastic left heart syndrome in fetal ultrasound via disease-specific Atlas maps. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (eds de Bruijne, M. et al.) 207–217 (Springer International Publishing, Cham, 2021, Accessed 28 March 2022). https://link.springer.com/10.1007/978-3-030-87234-2_20.

  36. Zhao, Z. et al. DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network. BMC Med. Inf. Decis. Mak. 19, 286 (2019).

    Article  Google Scholar 

  37. Chen, J. et al. EllipseNet: anchor-free ellipse detection for automatic cardiac biometrics in fetal echocardiography. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (eds de Bruijne, M. et al.) 218–227 (Springer International Publishing, Cham, 2021, Accessed 28 March 2022). https://link.springer.com/10.1007/978-3-030-87234-2_21.

  38. Casella, A. et al. A shape-constraint adversarial framework with instance-normalized spatio-temporal features for inter-fetal membrane segmentation. Med. Image Anal. 70, 102008 (2021).

    Article  Google Scholar 

  39. Bano, S. et al. Deep placental vessel segmentation for fetoscopic mosaicking. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (eds Martel, A. L. et al.) 763–773 (Springer International Publishing, Cham, 2020, Accessed 28 March 2022). https://link.springer.com/10.1007/978-3-030-59716-0_73.

  40. Adegboro, C. O., Choudhury, A., Asan, O. & Kelly, M. M. Artificial intelligence to improve health outcomes in the NICU and PICU: a systematic review. Hosp. Pediatr. 12, 93–110 (2022).

    Article  Google Scholar 

  41. Moccia, S., Migliorelli, L., Carnielli, V. & Frontoni, E. Preterm infants’ pose estimation with spatio-temporal features. IEEE Trans. Biomed. Eng. 67, 2370–2380 (2020).

    Article  Google Scholar 

  42. Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).

    Article  Google Scholar 

  43. Gunning, D. et al. XAI—explainable artificial intelligence. Sci. Robot 4, eaay7120 (2019).

    Article  Google Scholar 

  44. Selvaraju, R. R. et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV) 618–626 (IEEE, Venice, 2017, Accessed 5 April 2022). http://ieeexplore.ieee.org/document/8237336/.

  45. Ribeiro, M. T., Singh, S. & Guestrin, C. “Why Should I Trust You?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (ACM, San Francisco, California, USA, 2016, Accessed 5 April 2022). https://dl.acm.org/doi/10.1145/2939672.2939778.

  46. Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems (eds Guyon, I. et al.) (Curran Associates, Inc., 2017). https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf.

  47. van Druten, J., Khashu, M., Chan, S. S., Sharif, S. & Abdalla, H. Abdominal ultrasound should become part of standard care for early diagnosis and management of necrotising enterocolitis: a narrative review. Arch. Dis. Child Fetal Neonatal Ed. 104, F551–F559 (2019).

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank Editage (www.editage.com) for English language editing.

Funding

This publication is partly supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement Sano No. 857533 and the International Research Agendas programme of the Foundation for Polish Science, co-financed by the European Union under the European Regional Development Fund.

Author information

Authors and Affiliations

Authors

Contributions

A.S. and J.S.-S. conceptualised and designed the review. K.W. performed literature search. All authors wrote and contributed to all sections. A.S. produced the final version and all authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Arkadiusz Sitek or Joanna Seliga-Siwecka.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Search strategy for narrative review.

PUBMED

(“artificial intelligence”[tiab] OR “artificial intelligences”[tiab] OR AI[tiab] OR “machine learning”[tiab] OR “machines learning”[tiab] OR “deep learning”[tiab] OR “decision tree”[tiab] OR “decision trees”[tiab] OR “neural network”[tiab] OR “neural networks”[tiab] OR “neural net”[tiab] OR “neural nets”[tiab] OR “medical image processing”[tiab] OR “Artificial Intelligence”[Mesh]) AND (“necrotizing enterocolitis”[tiab] OR NEC[tiab] OR “Enterocolitis, Necrotizing”[Mesh]).

EMBASE

(‘artificial intelligence’:ti,ab,kw OR ‘artificial intelligences’:ti,ab,kw OR ai:ti,ab,kw OR ‘machine learning’:ti,ab,kw OR ‘machines learning’:ti,ab,kw OR ‘deep learning’:ti,ab,kw OR ‘decision tree’:ti,ab,kw OR ‘decision trees’:ti,ab,kw OR ‘neural network’:ti,ab,kw OR ‘neural networks’:ti,ab,kw OR ‘neural net’:ti,ab,kw OR ‘neural nets’:ti,ab,kw OR ‘medical image processing’:ti,ab,kw OR ‘artificial intelligence’/exp) AND (‘necrotizing enterocolitis’:ti,ab,kw OR nec:ti,ab,kw OR ‘necrotizing enterocolitis’/exp).

ARXIV

“necrotizing enterocolitis”.

IEEE Xplore

(“artificial intelligence” OR “artificial intelligences” OR AI OR “machine learning” OR “machines learning” OR “deep learning” OR “decision tree” OR “decision trees” OR “neural network” OR “neural networks” OR “neural net” OR “neural nets” OR “medical image processing”) AND “necrotizing enterocolitis”.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sitek, A., Seliga-Siwecka, J., Płotka, S. et al. Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns. Pediatr Res (2022). https://doi.org/10.1038/s41390-022-02322-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41390-022-02322-2

Search

Quick links