Download PDF
Eur J Pediatr Surg
DOI: 10.1055/a-2257-5122
Review Article

Artificial Intelligence in the Diagnosis and Management of Appendicitis in Pediatric Departments: A Systematic Review

Robin Rey
1   Department of Human Medicine, Faculty of Medicine, University of Geneva, Genève, Switzerland
,
Renato Gualtieri 
2   Department of Pediatrics, Gynecology and Obstetrics, University of Geneva, Genève, Switzerland
,
Giorgio La Scala
3   Division of Pediatric Surgery, Hôpital des enfants, Geneva University Hospitals, Genève, Switzerland
,
Klara Posfay Barbe
4   Division of General Pediatrics, Hôpital des enfants, Geneva University Hospitals, Genève, Switzerland
› Author Affiliations Funding None.
› Further Information
    Permissions and Reprints

Abstract

Introduction Artificial intelligence (AI) is a growing field in medical research that could potentially help in the challenging diagnosis of acute appendicitis (AA) in children. However, usefulness of AI in clinical settings remains unclear. Our aim was to assess the accuracy of AIs in the diagnosis of AA in the pediatric population through a systematic literature review.

Methods PubMed, Embase, and Web of Science were searched using the following keywords: “pediatric,” “artificial intelligence,” “standard practices,” and “appendicitis,” up to September 2023. The risk of bias was assessed using PROBAST.

Results A total of 302 articles were identified and nine articles were included in the final review. Two studies had prospective validation, seven were retrospective, and no randomized control trials were found. All studies developed their own algorithms and had an accuracy greater than 90% or area under the curve >0.9. All studies were rated as a “high risk” concerning their overall risk of bias.

Conclusion We analyzed the current status of AI in the diagnosis of appendicitis in children. The application of AI shows promising potential, but the need for more rigor in study design, reporting, and transparency is urgent to facilitate its clinical implementation.

Keywords

acute appendicitis - children - artificial intelligence - diagnostic accuracy

Ethical Approval

No ethical approval was required as the review concerns data from previously published studies.


Consent for Publication

The manuscript does not contain any individual's data in any form.


Availability of Data and Materials

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.


Competing Interests

None declared.


Author Contributions

All authors contributed to the study conception and design. R.R. and R.G. carried out the literature search, extracted and analyzed the data, and wrote the first draft of the manuscript; K.P.B. and G.L.S. reviewed the manuscript for important intellectual content; all authors approved the final version of the manuscript.


Supplementary Material



Publication History

Received: 23 October 2023

Accepted: 25 January 2024

Accepted Manuscript online:
30 January 2024

Article published online:
29 February 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Rothrock SG, Pagane J. Acute appendicitis in children: emergency department diagnosis and management. Ann Emerg Med 2000; 36 (01) 39-51
    CrossrefPubMedGoogle Scholar
  • 2 Benabbas R, Hanna M, Shah J, Sinert R. Diagnostic accuracy of history, physical examination, laboratory tests, and point-of-care ultrasound for pediatric acute appendicitis in the emergency department: a systematic review and meta-analysis. Acad Emerg Med 2017; 24 (05) 523-551
    CrossrefPubMedGoogle Scholar
  • 3 Rentea RM, St Peter SD. Pediatric appendicitis. Surg Clin North Am 2017; 97 (01) 93-112
    CrossrefPubMedGoogle Scholar
  • 4 Reynolds SL, Jaffe DM. Diagnosing abdominal pain in a pediatric emergency department. Pediatr Emerg Care 1992; 8 (03) 126-128
    CrossrefPubMedGoogle Scholar
  • 5 Scholer SJ, Pituch K, Orr DP, Dittus RS. Clinical outcomes of children with acute abdominal pain. Pediatrics 1996; 98 (4, Pt 1): 680-685
    CrossrefPubMedGoogle Scholar
  • 6 Bhangu A, Søreide K, Di Saverio S, Assarsson JH, Drake FT. Acute appendicitis: modern understanding of pathogenesis, diagnosis, and management. Lancet 2015; 386 (10000): 1278-1287
    CrossrefPubMedGoogle Scholar
  • 7 Narsule CK, Kahle EJ, Kim DS, Anderson AC, Luks FI. Effect of delay in presentation on rate of perforation in children with appendicitis. Am J Emerg Med 2011; 29 (08) 890-893
    CrossrefPubMedGoogle Scholar
  • 8 Lipsett SC, Monuteaux MC, Shanahan KH, Bachur RG. Nonoperative management of uncomplicated appendicitis. Pediatrics 2022; 149 (05) e2021054693
    CrossrefPubMedGoogle Scholar
  • 9 Minneci PC, Mahida JB, Lodwick DL. et al. Effectiveness of patient choice in nonoperative vs surgical management of pediatric uncomplicated acute appendicitis. JAMA Surg 2016; 151 (05) 408-415
    CrossrefPubMedGoogle Scholar
  • 10 Minneci PC, Hade EM, Lawrence AE. et al; Midwest Pediatric Surgery Consortium. Association of nonoperative management using antibiotic therapy vs laparoscopic appendectomy with treatment success and disability days in children with uncomplicated appendicitis. JAMA 2020; 324 (06) 581-593
    CrossrefPubMedGoogle Scholar
  • 11 Bundy DG, Byerley JS, Liles EA, Perrin EM, Katznelson J, Rice HE. Does this child have appendicitis?. JAMA 2007; 298 (04) 438-451
    CrossrefPubMedGoogle Scholar
  • 12 Yu CW, Juan LI, Wu MH, Shen CJ, Wu JY, Lee CC. Systematic review and meta-analysis of the diagnostic accuracy of procalcitonin, C-reactive protein and white blood cell count for suspected acute appendicitis. Br J Surg 2013; 100 (03) 322-329
    CrossrefPubMedGoogle Scholar
  • 13 Pogorelić Z, Rak S, Mrklić I, Jurić I. Prospective validation of Alvarado score and Pediatric Appendicitis Score for the diagnosis of acute appendicitis in children. Pediatr Emerg Care 2015; 31 (03) 164-168
    CrossrefPubMedGoogle Scholar
  • 14 Koberlein GC, Trout AT, Rigsby CK. et al; Expert Panel on Pediatric Imaging. ACR Appropriateness Criteria® suspected appendicitis-child. J Am Coll Radiol 2019; 16 (5S, 5s): S252-S263
    CrossrefPubMedGoogle Scholar
  • 15 Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet 2020; 395 (10236): 1579-1586
    CrossrefPubMedGoogle Scholar
  • 16 World Health Organization.. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021
    Google Scholar
  • 17 Aggarwal R, Sounderajah V, Martin G. et al. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med 2021; 4 (01) 65
    CrossrefPubMedGoogle Scholar
  • 18 Liopyris K, Gregoriou S, Dias J, Stratigos AJ. Artificial intelligence in dermatology: challenges and perspectives. Dermatol Ther (Heidelb) 2022; 12 (12) 2637-2651
    CrossrefPubMedGoogle Scholar
  • 19 Reddy S. Explainability and artificial intelligence in medicine. Lancet Digit Health 2022; 4 (04) e214-e215
    CrossrefPubMedGoogle Scholar
  • 20 Quinn TP, Jacobs S, Senadeera M, Le V, Coghlan S. The three ghosts of medical AI: can the black-box present deliver?. Artif Intell Med 2022; 124: 102158
    CrossrefPubMedGoogle Scholar
  • 21 Wolff RF, Moons KGM, Riley RD. et al; PROBAST Group†. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 2019; 170 (01) 51-58
    CrossrefPubMedGoogle Scholar
  • 22 Moons KGM, Wolff RF, Riley RD. et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med 2019; 170 (01) W1-W33
    CrossrefPubMedGoogle Scholar
  • 23 Akgül F, Er A, Ulusoy E. et al. Integration of physical examination, old and new biomarkers, and ultrasonography by using neural networks for pediatric appendicitis. Pediatr Emerg Care 2021; 37 (12) e1075-e1081
    CrossrefPubMedGoogle Scholar
  • 24 Akmese OF, Dogan G, Kor H, Erbay H, Demir E. The use of machine learning approaches for the diagnosis of acute appendicitis. Emerg Med Int 2020; 2020: 7306435
    CrossrefPubMedGoogle Scholar
  • 25 Aydin E, Türkmen İU, Namli G. et al. A novel and simple machine learning algorithm for preoperative diagnosis of acute appendicitis in children. Pediatr Surg Int 2020; 36 (06) 735-742
    CrossrefPubMedGoogle Scholar
  • 26 Grigull L, Lechner WM. Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department. Pediatr Res 2012; 71 (06) 725-731
    CrossrefPubMedGoogle Scholar
  • 27 Marcinkevics R, Reis Wolfertstetter P, Wellmann S, Knorr C, Vogt JE. Using machine learning to predict the diagnosis, management and severity of pediatric appendicitis. Front Pediatr 2021; 9: 662183
    CrossrefPubMedGoogle Scholar
  • 28 Reismann J, Kiss N, Reismann M. The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study. BMC Pediatr 2021; 21 (01) 268
    CrossrefPubMedGoogle Scholar
  • 29 Shikha A, Kasem A. The development and validation of artificial intelligence pediatric appendicitis decision-tree for children 0 to 12 years old. Eur J Pediatr Surg 2022; 33 (05) 395-402
    Article in Thieme ConnectPubMedGoogle Scholar
  • 30 Stiel C, Elrod J, Klinke M. et al. The modified Heidelberg and the AI appendicitis score are superior to current scores in predicting appendicitis in children: a two-center cohort study. Front Pediatr 2020; 8: 592892
    CrossrefPubMedGoogle Scholar
  • 31 Su D, Li Q, Zhang T. et al. Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department. BMC Med Res Methodol 2022; 22 (01) 18
    CrossrefPubMedGoogle Scholar
  • 32 Galai T, Beloosesky OZ, Scolnik D, Rimon A, Glatstein M. Misdiagnosis of acute appendicitis in children attending the emergency department: the experience of a large, tertiary care pediatric hospital. Eur J Pediatr Surg 2017; 27 (02) 138-141
    Article in Thieme ConnectPubMedGoogle Scholar
  • 33 Mahajan P, Basu T, Pai C-W. et al. Factors associated with potentially missed diagnosis of appendicitis in the emergency department. JAMA Netw Open 2020; 3 (03) e200612-e200612
    CrossrefPubMedGoogle Scholar
  • 34 Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput 2023; 14 (07) 8459-8486
    CrossrefPubMedGoogle Scholar
  • 35 Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J 2019; 6 (02) 94-98
    CrossrefPubMedGoogle Scholar
  • 36 Alowais SA, Alghamdi SS, Alsuhebany N. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 2023; 23 (01) 689
    CrossrefPubMedGoogle Scholar
  • 37 Jayakumar S, Sounderajah V, Normahani P. et al. Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study. NPJ Digit Med 2022; 5 (01) 11
    CrossrefPubMedGoogle Scholar
  • 38 Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett 2020; 471: 61-71
    CrossrefPubMedGoogle Scholar
  • 39 Hunter B, Hindocha S, Lee RW. The role of artificial intelligence in early cancer diagnosis. Cancers (Basel) 2022; 14 (06) 1524
    CrossrefPubMedGoogle Scholar
  • 40 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 (01) 30-36
    CrossrefPubMedGoogle Scholar
  • 41 Cacciamani GE, Chu TN, Sanford DI. et al. PRISMA AI reporting guidelines for systematic reviews and meta-analyses on AI in healthcare. Nat Med 2023; 29 (01) 14-15
    CrossrefPubMedGoogle Scholar