Künstliche Intelligenz in der Kinderpneumologie – Chancen und unbeantwortete Fragen

Zusammenfassung

Künstliche Intelligenz (KI) findet bereits Anwendung in den meisten medizinischen Disziplinen, so auch in der pädiatrischen Pneumologie. Diese Übersichtsarbeit beschreibt aktuelle Entwicklungen KI-gestützter Technologien und erörtert deren Potenzial für die Diagnostik und Behandlung von Lungenerkrankungen im Kindes- und Jugendalter. Das Spektrum reicht von Modellen zur Analyse von Atemgeräuschen, über die automatisierte Auswertung medizinischer Bildgebung bis hin zu Systemen zur Unterstützung klinischer Entscheidungen. Hierbei werden insbesondere auch Herausforderungen bei der Anpassung von KI für pädiatrische Bevölkerungsgruppen beschrieben. Schließlich werden offene Fragen, beispielsweise zur Implementierung KI-basierter Software in den klinischen Alltag erörtert.

Abstract

Artificial intelligence (AI) is already being used in most medical disciplines, including paediatric pneumology. This review describes current developments in AI-supported technologies and discusses their potential for the diagnosis and treatment of lung diseases in children and adolescents. The spectrum ranges from models for analysing respiratory sounds and the automated evaluation of medical imaging to systems for supporting clinical decisions. In particular, challenges in the adaptation of AI for paediatric populations are also described. Finally, open questions, such as the implementation of AI-based software in everyday clinical practice, will be discussed.

Schlüsselwörter

künstliche Intelligenz - Kinderpneumologie - Geräuscherkennung - maschinelles Lernen - Bildauswertung - Modellierung

Keywords

artificial intelligence - paediatric pulmonology - sound recognition - machine learning - image analysis - modellingArtikel online veröffentlicht 2025

Publication History

Article published online:
03 February 2025

• Literatur

• 1 Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019; Jun 6: 94-98
• 2 Artificial intelligence in healthcare: Applications, risks, and ethical and societal impacts; EPRS | European Parliamentary Research Service, Scientific Foresight Unit (STOA) PE 729.512 – June 2022. https://www.europarl.europa.eu/stoa/en/document/EPRS_STU( 2022 729512 (zuletzt besucht am 17.7.2024)
• 3 Bajwa J, Munir U, Nori A. et al. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021; 8: e188-e194
• 4 Haug CJ, Drazen JM. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. N Engl J Med 2023; Mar 30 388: 1201-1208
• 5 Pates KM, Shang Z, Periselneris J. et al. Breaking the mould, a first parse at natural language processing in aspergillosis diagnosis. J Thorac Dis. 2023; 31 15: 17-21 Epub 2023 Jan 5
• 6 Niimi A. Natural Language Processing: A Promising Research Tool of Chronic Cough for the Big Data Era. Chest. 2021; Jun 159: 2149-2150
• 7 Walsh SLF, Mackintosh JA, Calandriello L. et al. Deep Learning-based Outcome Prediction in Progressive Fibrotic Lung Disease Using High-Resolution Computed Tomography. Am J Respir Crit Care Med 2022; Oct 1 206: 883-891
• 8 Longjian E, Zhao B, Liu H. et al. Image-based deep learning in diagnosing the etiology of pneumonia on pediatric chest X-rays. Pediatr Pulmonol. 2021; 56: 1036-1044 Epub 2021 Mar 19
• 9 Longjian E, Zhao B, Guo Y. et al. Using deep-learning techniques for pulmonary-thoracic segmentations and improvement of pneumonia diagnosis in pediatric chest radiographs. Pediatr Pulmonol 2019; Oct 54: 1617-1626 Epub 2019 Jul 3
• 10 Zirkle J, Han X, Racz R. et al. Deep learning-enabled natural language processing to identify directional pharmacokinetic drug-drug interactions. BMC Bioinformatics 2023; Nov 1 24: 413
• 11 Hossain E, Rana R, Higgins N. et al. Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review. Comput Biol Med. 202r 155: 106649 Epub 2023 Feb 10
• 12 Zirkle J, Han X, Racz R. et al. Deep learning-enabled natural language processing to identify directional pharmacokinetic drug-drug interactions. BMC Bioinformatics 2023; Nov 1 24: 413
• 13 Alowais SA, Alghamdi SS, Alsuhebany N. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 2023; Sep 22 23: 689
• 14 Arnold C. Inside the nascent industry of AI-designed drugs. Nat Med 2023; Jun 29: 1292-1295
• 15 Maqsood K, Hagras H, Zabet NR. An overview of artificial intelligence in the field of genomics. Discov Artif Intell 2024; 4: 9
• 16 Nkoy FL, Stone BL, Zhang Y, Luo G. A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection. JMIR Med Inform. 2024; 12: e56572. doi: 10.2196/56572
• 17 Malhotra A, Molloy EJ, Bearer CF. et al. Emerging role of artificial intelligence, big data analysis and precision medicine in pediatrics. Pediatr Res. 2023; Jan 93: 281-283 Epub 2023 Feb 17. PMID: 36807652
• 18 Kim BJ, Kim BS, Mun JH. et al. An accurate deep learning model for wheezing in children using real world data. Sci Rep 12: 22465 2022;
• 19 Hafke-Dys H, Kuźnar-Kamińska B, Grzywalski T. et al. Artificial Intelligence Approach to the Monitoring of Respiratory Sounds in Asthmatic Patients. Front Physiol. 2021; Nov 11 12: 745635 PMID: 34858203; PMCID: PMC8632553.
• 20 Emeryk A, Derom E, Janeczek K. et al. Home monitoring of asthma exacerbations in children and adults with use of an AI-aided stethoscope. The Annals of Family Medicine 2023; 21: 517-525
• 21 van Leeuwen KG, de Rooij M, Schalekamp S. et al. How does artificial intelligence in radiology improve efficiency and health outcomes?. Pediatr Radiol 52: 2087-2093 2022;
• 22 How Generative AI is Changing Healthcare Payers – From Fraud Detection to Personalized Plans https://quantiphi.com/how-generative-ai-is-changing-healthcare-payers-from-fraud-detection-to-personalized-plans/ (zuletzt besucht am 18.7.2024)
• 23 Roguin A. Rene Theophile Hyacinthe Laënnec (1781–1826): the man behind the stethoscope. Clin Med Res 2006; 4: 230-235
• 24 Melbye H, Garcia-Marcos L, Brand P. et al. Wheezes, crackles and rhonchi: simplifying description of lung sounds increases the agreement on their classification: a study of 12 physicians' classification of lung sounds from video recordings. BMJ Open Respir Res 2016; Apr 28 3: e000136
• 25 Ferreira-Cardoso H, Moniz AC, Almeida R. et al. Comparison of lung auscultation between smartphone and digital stethoscope in patients with asthma: a feasibility study. Eur Respir J 2022; 60 Suppl. 66 1859
• 26 Portnoy JM, Waller M, De Lurgio S. et al. Telemedicine is as effective as in-person visits for patients with asthma. Ann Allergy Asthma Immunol 2016; 117: 241-245
• 27 Oweis RJ, Abdulhay EW, Khayal A. et al. An alternative respiratory sounds classification system utilizing artificial neural networks. Biomed J. 2015; 38: 153-161
• 28 Hsu FS, Huang SR, Huang CW. et al. Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1. PLoS ONE 2021; 16: e0254134
• 29 Park JS, Kim K, Kim JH. et al. A machine learning approach to the development and prospective evaluation of a pediatric lung sound classification model. Sci Rep 13: 1289 2023;
• 30 Tabata H, Hirayama M, Enseki M. et al. A novel method for detecting airway narrowing using breath sound spectrum analysis in children. Respir Investig. 2016; Jan 54: 20-28 Epub 2015 Oct 2
• 31 Grzywalski T, Piecuch M, Szajek M. et al. Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination. Eur J Pediatr. 2019; Jun 178: 883-890 Epub 2019 Mar 29
• 32 Kim Y, Hyon Y, Jung SS. et al. Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci Rep 2021; 11: 17186
• 33 Park DE, Watson NL, Focht C. et al. Digitally recorded and remotely classified lung auscultation compared with conventional stethoscope classifications among children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case-control study. BMJ Open Respir Res 2022; May 9: e001144
• 34 Porter P, Brisbane J, Abeyratne U. et al. Diagnosing community-acquired pneumonia via a smartphone-based algorithm: a prospective cohort study in primary and acute-care consultations. Br J Gen Pract. 202;26 71: e258-e265
• 35 Porter P, Abeyratne U, Swarnkar V. et al. A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children. Respir Res 2019; 20: 81
• 36 Kuruma K, Otomo T, Sakama T. et al. Breath sound analyses of infants with respiratory syncytial virus acute bronchiolitis. Pediatr Pulmonol. 2022; 57: 2320-2326 Epub 2022 Jun 21
• 37 Shimoda T, Obase Y, Nagasaka Y. et al. Lung Sound Analysis and Airway Inflammation in Bronchial Asthma. J Allergy Clin Immunol Pract 2016; 4: 505-511 Epub 2016 Mar 25
• 38 Miyamoto M, Yoshihara S, Shioya H. et al. Lung sound analysis in infants with risk factors for asthma development. Health Sci Rep 2021; 17 4: e379
• 39 Guerra S, Lombardi E, Stern DA. et al. Fetal Origins of Asthma: A Longitudinal Study from Birth to Age 36 Years. Am J Respir Crit Care Med 2020; 15 202: 1646-1655
• 40 Habukawa C, Ohgami N, Arai T. et al. Wheeze Recognition Algorithm for Remote Medical Care Device in Children: Validation Study. JMIR Pediatr Parent 2021; Jun 17 4: e28865
• 41 Do YH, van Aalderen W, Dellbrügger E. et al. Clinical efficacy and satisfaction of a digital wheeze detector in a multicentre randomised controlled trial: the WheezeScan study. ERJ Open Res 2024; Jan 15 10: 00518-02023
• 42 Urban C, Kiefer A, Conradt R. et al. Validation of the LEOSound^® monitor for standardized detection of wheezing and cough in children. Pediatr Pulmonol 2022; Feb 57: 551-559 Epub 2021 Nov 30
• 43 Yacoub B, Varga-Szemes A, Schoepf UJ. et al. Impact of Artificial Intelligence Assistance on Chest CT Interpretation Times: A Prospective Randomized Study. AJR Am J Roentgenol. 2022; Nov 219: 743-751 Epub 2022 Jun 15
• 44 Quah J, Liew CJY, Zou L. et al. Chest radiograph-based artificial intelligence predictive model for mortality in community-acquired pneumonia. BMJ Open Respir Res 2021; Aug 8: e001045
• 45 Sjoding MW, Taylor D, Motyka J. et al. Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation. Lancet Digit Health. 2021; Jun 3: e340-e348 Epub 2021 Apr 20
• 46 Rueckel J, Huemmer C, Fieselmann A. et al. Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training. Eur Radiol. 2021; Oct 31: 7888-7900 Epub 2021 Mar 27
• 47 de Margerie-Mellon C, Chassagnon G. Artificial intelligence: A critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging 2023; Jan 104: 11-17 Epub 2022 Dec 10
• 48 Lakhani P, Flanders A, Gorniak R. Endotracheal Tube Position Assessment on Chest Radiographs Using Deep Learning. Radiol Artif Intell 2020; Nov 18 3: e200026
• 49 Otjen JP, Moore MM, Romberg EK. et al. The current and future roles of artificial intelligence in pediatric radiology. Pediatr Radiol 2022; Oct 52: 2065-2073 Epub 2021 May 27
• 50 Shin H, Son NH, Kim M. et al. Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs. Sci Rep 12: 10215 2022;
• 51 American College of Radiology, Data Science Instritute, AI Central Database https://aicentral.acrdsi.org zuletzt besucht am 18.07.2024
• 52 Morcos G, Yi PH, Jeudy J. Applying Artificial Intelligence to Pediatric Chest Imaging: Reliability of Leveraging Adult-Based Artificial Intelligence Models. J Am Coll Radiol. 2023; Aug 20: 742-747 Epub 2023 Jul 17
• 53 Mahomed N, van Ginneken B, Philipsen RHHM. et al. Computer-aided diagnosis for World Health Organization-defined chest radiograph primary-endpoint pneumonia in children. Pediatr Radiol. 2020; 50: 482-491 Epub 2020 Jan 13
• 54 Vrbancic G, Podgorelec V. Efficient ensemble for image-based identification of Pneumonia utilizing deep CNN and SGD with warm restarts. 2022 Expert Systems with Applications. 187, art. no. 115834
• 55 Gardner RL, Cooper E, Haskell J. et al. Physician stress and burnout: the impact of health information technology. Journal of the American Medical Informatics Association: JAMIA 2019; 26: 106-114
• 56 Seol HY, Shrestha P, Muth JF. et al. Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial. PLoS One 2021; Aug 2 16: e0255261
• 57 Dramburg S, Marchante Fernández M, Potapova E. et al. The Potential of Clinical Decision Support Systems for Prevention, Diagnosis, and Monitoring of Allergic Diseases. Front Immunol 2020; Sep 10 11: 2116
• 58 103 Sagheb E, Wi CI, Yoon J. et al. Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records. J Allergy Clin Immunol Pract. 2022; Apr 10: 1047-1056.e1 Epub 2021 Nov 17
• 59 Tso CF, Lam C, Calvert J. et al. Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients. Front Pediatr 2022; Aug 4 10: 886212
• 60 He P, Moraes TJ, Dai D. et al. Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning. Pediatr Res 95: 1818-1825 2024;
• 61 Grzybowski A, Jin K, Wu H. Challenges of artificial intelligence in medicine and dermatology. Clin Dermatol. 2024; May-Jun 42: 210-215 Epub 2024 Jan 4
• 62 Marques M, Almeida A, Pereira H. The Medicine Revolution Through Artificial Intelligence: Ethical Challenges of Machine Learning Algorithms in Decision-Making. Cureus. 2024; Sep 14 16: e69405
• 63 Gichoya JW, Banerjee I, Bhimireddy AR. et al. AI recognition of patient race in medical imaging: a modelling study. Lancet Digit Health 2022; Jun 4: e406-e414 Epub 2022 May 11
• 64 Duarte-Rojo A, Sejdic E. Artificial Intelligence and the Risk for Intuition Decline in Clinical Medicine. Am J Gastroenterol 2022; Mar 1 117: 401-402
• 65 Dayan I, Roth HR, Zhong A. et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med 2021; 27: 1735-1743
• 66 Seefeldt M, Buhl R, Hamelmann E. et al. German Asthma Net – Was haben wir bisher gelernt?. Z Pneumologie 20: 88-93 2023;
• 67 Datenbank geförderter Projekte des Bundesministeriums für Bildung und Forschung https://www.gesundheitsforschung-bmbf.de/de/calm-qe-medizininformatik-use-case-copd-und-asthma-longitudinale-und-sektorubergreifende-16644.php zuletzt besucht am 18.07.2024
• 68 https://www.fiercehealthcare.com/ai-and-machine-learning/himss24-how-epic-building-out-ai-ambient-technology-clinicians zuletzt besucht am 11.12.2024
• 69 Yu G, Li Z, Li S. et al. The role of artificial intelligence in identifying asthma in pediatric inpatient setting. Ann Transl Med 2020; Nov 8: 1367
• 70 World Health Organisation – Meet S.A.R.A.H. A Smart AI Resource Assistant for Health https://www.who.int/campaigns/s-a-r-a-h zuletzt besucht am 18.7.2024
• 71 Gilbert S, Mehl A, Baluch A. et al. How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs. BMJ Open 2020; 10: e040269
• 72 Epstein D, Barak-Corren Y, Isenberg Y. et al. Clinical Decision Support System: A Pragmatic Tool to Improve Acute Exacerbation of COPD Discharge Recommendations. COPD: Journal of Chronic Obstructive Pulmonary Disease 2019; 16: 18-24
• 73 Nagi F, Salih R, Alzubaidi M. et al. Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review. Stud Health Technol Inform 2023; Jun 29 305: 648-651