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)

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

• 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
• 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
• 3 Rajpurkar P, Lungren MP. The Current and Future State of AI Interpretation of Medical Images. N Engl J Med 2023; 388: 1981-1990
• 4 Davendralingam N, Sebire NJ, Arthurs OJ. et al. Artificial intelligence in paediatric radiology: Future opportunities. Br J Radiol 2020; 94
• 5 Mentzel HJ. Artificial intelligence in image evaluation and diagnosis. Monatsschr Kinderheilkd 2021; 169: 694-704
• 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
• 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
• 8 Gallo-Bernal S, Bedoya MA, Gee MS. et al. Pediatric magnetic resonance imaging: faster is better. Pediatr Radiol 2023; 53: 1270-1284
• 9 Ng CKC. Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review. Children (Basel) 2022; 9: 1044
• 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
• 11 Offiah AC. Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology. Pediatr Radiol 2022; 52: 2149-2158
• 12 Halabi SS, Prevedello LM, Kalpathy-Cramer J. et al. The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology 2019; 290: 498-503
• 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
• 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;
• 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
• 16 Katz ME. Pediatric teleradiology: The benefits. Pediatr Radiol 2010; 40: 1345-1348
• 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
• 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
• 19 Coppola F, Bibbolino C, Grassi R. et al. Results of an Italian survey on teleradiology. Radiol med 2016; 121: 652-659
• 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
• 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
• 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
• 23 Roos JE, Agten C, Treumann T. et al. SGR-SSR TELERADIOLOGIE WHITE PAPER 2.0. 2019.
• 24 Bohrer E, Schäfer SB, Krombach GA. Die neue Strahlenschutzgesetzgebung – Teil 2. Der Radiologe 2020; 60: 959-965
• 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
• 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
• 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
• 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
• 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
• 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