Download PDF
Rofo
DOI: 10.1055/a-2516-3057
Pediatric Radiology

Artificial Intelligence and Teleradiology in Pediatric Radiology: A Survey by the Society for German-speaking Pediatric Radiologists (GPR) and the Swiss Society for Pediatric Radiology (SGPR)

Article in several languages: deutsch | English
Max-Johann Sturm
1   Pediatric Radiology, Jena University Hospital, Jena, Germany (Ringgold ID: RIN39065)
,
Thekla von Kalle
2   Radiology, Olgahospital Klinikum Stuttgart, Stuttgart, Germany
,
Diane Miriam Renz 
3   Institute of Diagnostic and Interventional Radiology, Medizinische Hochschule Hannover, Hannover, Germany (Ringgold ID: RIN9177)
,
Dirk Klee
4   Radiology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
5   Department of Pediatric Radiology, University Hospital of Düsseldorf, Düsseldorf, Germany (Ringgold ID: RIN39064)
,
Janina Patsch
6   Institute for Radiology and Nuclear Medicine, Medical University of Vienna, Wien, Austria (Ringgold ID: RIN27271)
,
Stephanie Spieth
7   Institute and Polyclinic for Diagnostic and Interventional Radiology, Dresden University Hospital, Dresden, Germany (Ringgold ID: RIN39063)
,
Seema Toso
8   Department of Pediatric Radiology, Hôpitaux Universitaires Genève, Geneve, Switzerland (Ringgold ID: RIN27230)
,
Enno Stranzinger
9   Department of Diagnostic, Interventional and Paediatric Radiology, Inselspital University Hospital Bern, Bern, Switzerland (Ringgold ID: RIN27252)
,
Hans-Joachim Mentzel
1   Pediatric Radiology, Jena University Hospital, Jena, Germany (Ringgold ID: RIN39065)
› Author Affiliations
› Further Information
Also available at
  Permissions and Reprints

Abstract

Purpose

The aim of our study was to assess the attitudes towards AI and teleradiology and their current usage in pediatric radiology within German-speaking countries.

Materials and Methods

From March to May 2023, we conducted an anonymous online survey among members of the Society for German-speaking Pediatric Radiologists (GPR) and the Swiss Society for Pediatric Radiology (SGPR) via the SurveyMonkey platform. The survey consisted of 25 items with rating scales and open-ended responses.

Results

Out of 418 society members, 36 completed the questionnaire (8.6%). Teleradiology (50% fully agree, 27.8% partly agree) and AI (38.9% fully agree, 22.2% partly agree) were considered relevant for pediatric radiology by the majority of respondents. Teleconsultation for second opinions is regularly used in 58% of the departments. Currently, AI does not play a significant role in the daily work of 52.8% of respondents. Beyond segmentation, AI is used primarily for image acquisition and dose reduction. Over 80% of respondents indicated that bone age determination is well-suited for an AI solution, yet only 31% routinely use such a solution.

Conclusion

AI and teleradiology have a high level of acceptance in German-speaking pediatric radiology (Germany, Austria, Switzerland, i.e. the DACH region) and are seen as a possible strategy for improving pediatric radiology care. This contrasts with the current low level of use in clinical routine.

Key Points

  • Pediatric radiologists in the DACH region consider AI and teleradiology to be important for pediatric radiology care.

  • AI/teleradiology are seen as viable options to enhance pediatric radiology care.

  • However, the actual use of AI/teleradiology in everyday routine is low.

Citation Format

  • Sturm M, von Kalle T, Renz DM et al. Artificial Intelligence and Teleradiology in Pediatric Radiology: A Survey by the Society for German-speaking Pediatric Radiologists (GPR) and the Swiss Society for Pediatric Radiology (SGPR). Rofo 2025; DOI 10.1055/a-2516-3057

Keywords

economics - teleradiology - cost-effectiveness - acceptance testing - diagnostic radiology - pediatric radiology


Publication History

Received: 11 September 2024

Accepted after revision: 09 January 2025

Article published online:
06 February 2025

© 2025. Thieme. All rights reserved.

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

  • Literatur

  • 1 Mentzel HJ, von Kalle T, Antoch G. et al. Challenges in Pediatric Radiology – Strategies of the board of the German speaking Society of Pediatric Radiology. RoFo 2020; 192: 531-536
    Thieme ConnectPubMedSearch in Google Scholar
  • 2 Staatz G, Daldrup-Link HE, Herrmann J. et al. From Xrays to PET/MR, and then? – Future imaging in pediatric radiology. RoFo 2019; 191: 357-366
    Thieme ConnectPubMedSearch in Google Scholar
  • 3 Rajpurkar P, Lungren MP. The Current and Future State of AI Interpretation of Medical Images. N Engl J Med 2023; 388: 1981-1990
    CrossrefPubMedSearch in Google Scholar
  • 4 Davendralingam N, Sebire NJ, Arthurs OJ. et al. Artificial intelligence in paediatric radiology: Future opportunities. Br J Radiol 2020; 94
    CrossrefPubMedSearch in Google Scholar
  • 5 Mentzel HJ. Artificial intelligence in image evaluation and diagnosis. Monatsschr Kinderheilkd 2021; 169: 694-704
    CrossrefPubMedSearch in Google Scholar
  • 6 Sorantin E, Grasser MG, Hemmelmayr A. et al. The augmented radiologist: Artificial intelligence in the practice of radiology. Pediatr Radiol 2022; 52: 2074-2086
    CrossrefPubMedSearch in Google Scholar
  • 7 Krueger P-C, Ebeling K, Waginger M. et al. Evaluation of the post-processing algorithms SimGrid and S-Enhance for paediatric intensive care patients and neonates. Pediatr Radiol 2022; 52: 1029-1037
    CrossrefPubMedSearch in Google Scholar
  • 8 Gallo-Bernal S, Bedoya MA, Gee MS. et al. Pediatric magnetic resonance imaging: faster is better. Pediatr Radiol 2023; 53: 1270-1284
    CrossrefPubMedSearch in Google Scholar
  • 9 Ng CKC. Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review. Children (Basel) 2022; 9: 1044
    CrossrefPubMedSearch in Google Scholar
  • 10 Tschauner S, Zellner M, Pistorius S. et al. Ultra-low-dose lung multidetector computed tomography in children – Approaching 0.2 millisievert. Eur J Radiol 2021; 139
    CrossrefPubMedSearch in Google Scholar
  • 11 Offiah AC. Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology. Pediatr Radiol 2022; 52: 2149-2158
    CrossrefPubMedSearch in Google Scholar
  • 12 Halabi SS, Prevedello LM, Kalpathy-Cramer J. et al. The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology 2019; 290: 498-503
    CrossrefPubMedSearch in Google Scholar
  • 13 Thodberg HH, Thodberg B, Ahlkvist J. et al. Autonomous artificial intelligence in pediatric radiology: The use and perception of BoneXpert for bone age assessment. Pediatr Radiol 2022; 52: 1338-1346
    CrossrefPubMedSearch in Google Scholar
  • 14 Pape J, Hirsch FW, Deffaa OJ. et al. Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort. RoFo 2023;
    Thieme ConnectPubMedSearch in Google Scholar
  • 15 Franken Jr. EA, Berbaum KS, Brandser EA. et al. Pediatric radiology at a rural hospital: Value of teleradiology and subspecialty consultation. AJR Am J Roentgeno 1997; 168: 1349-1352
    CrossrefPubMedSearch in Google Scholar
  • 16 Katz ME. Pediatric teleradiology: The benefits. Pediatr Radiol 2010; 40: 1345-1348
    CrossrefPubMedSearch in Google Scholar
  • 17 Sammer MBK, Kan JH. Providing second-opinion interpretations of pediatric imaging: Embracing the call for value-added medicine. Pediatr Radiol 2021; 51: 523-528
    CrossrefPubMedSearch in Google Scholar
  • 18 Shelmerdine SC, Rosendahl K, Arthurs OJ. Artificial intelligence in paediatric radiology: International survey of health care professionals’ opinions. Pediatr Radiol 2022; 52: 30-41
    CrossrefPubMedSearch in Google Scholar
  • 19 Coppola F, Bibbolino C, Grassi R. et al. Results of an Italian survey on teleradiology. Radiol med 2016; 121: 652-659
    CrossrefPubMedSearch in Google Scholar
  • 20 Huisman M, Ranschaert E, Parker W. et al. An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: Fear of replacement, knowledge, and attitude. Eur Radiol 2021; 31: 7058-7066
    CrossrefPubMedSearch in Google Scholar
  • 21 Horst KK, Leschied JR, Janitz EM. et al. Neonatal neurosonography practices: A survey of active Society for Pediatric Radiology members. Pediatr Radiol 2023; 53: 112-120
    CrossrefPubMedSearch in Google Scholar
  • 22 Seghers MC, Seghers VJ, Sher AC. et al. Working from home during the COVID-19 pandemic: Surveys of the Society for Pediatric Radiology and the Society of Chiefs of Radiology at Children’s Hospitals. Pediatr Radiol 2022; 52: 1242-1254
    CrossrefPubMedSearch in Google Scholar
  • 23 Roos JE, Agten C, Treumann T. et al. SGR-SSR TELERADIOLOGIE WHITE PAPER 2.0. 2019.
    PubMed
  • 24 Bohrer E, Schäfer SB, Krombach GA. Die neue Strahlenschutzgesetzgebung – Teil 2. Der Radiologe 2020; 60: 959-965
    CrossrefPubMedSearch in Google Scholar
  • 25 Pearce MS, Salotti JA, Little MP. et al. Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: A retrospective cohort study. Lancet 2012; 380: 499-505
    CrossrefPubMedSearch in Google Scholar
  • 26 Padash S, Mohebbian MR, Adams SJ. et al. Pediatric chest radiograph interpretation: How far has artificial intelligence come? A systematic literature review. Pediatr Radiol 2022; 52: 1568-1580
    CrossrefPubMedSearch in Google Scholar
  • 27 Ahn SY, Chae KJ, Goo JM. The Potential Role of Grid-Like Software in Bedside Chest Radiography in Improving Image Quality and Dose Reduction: An Observer Preference Study. Korean J Radiol 2018; 19: 526-533
    CrossrefPubMedSearch in Google Scholar
  • 28 Zhang H, Wang J, Zeng D. et al. Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review. Med Phys 2018; 45: e886-e907
    CrossrefPubMedSearch in Google Scholar
  • 29 Mese I, Altintas Mese C, Demirsoy U. et al. Innovative advances in pediatric radiology: Computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond. Pediatr Radiol 2024; 54: 1-11
    CrossrefPubMedSearch in Google Scholar
  • 30 Codari M, Melazzini L, Morozov SP. et al. Impact of artificial intelligence on radiology: A EuroAIM survey among members of the European Society of Radiology. Insights Imaging 2019; 10: 105
    CrossrefPubMedSearch in Google Scholar