Download PDF
Rofo 2024; 196(07): 664-670
DOI: 10.1055/a-2208-6487
Editorial

Artificial intelligence (AI) in diagnostic imaging

Discussion basis for clinical process support Article in several languages: English | deutsch
Rainer Braunschweig
1   Institute of Radiology, University Hospitals Erlangen Department of Radiology, Erlangen, Germany
,
Daniela Kildal
2   Radiology, Valais Hospital, Visp, Switzerland
3   Klinik für diagnostische und interventionelle Radiologie, University Hospital Ulm, Germany
,
Rolf Janka
1   Institute of Radiology, University Hospitals Erlangen Department of Radiology, Erlangen, Germany
› Author Affiliations
› Further Information
Also available at
    Permissions and Reprints

Abstract

Purpose

In the last few years artificial intelligence (AI) has increasingly become a topic of interest, especially in diagnostic imaging. There are two main expected potential benefits: workflow effectiveness and diagnostic support systems, particularly as far as quality assurance is concerned.

Methods

To meet these objectives, it is necessary to define what artificial intelligence in medicine means and to discuss which detailed steps should be fulfilled, e. g., for MSK imaging in the daily routine. In addition, this article provides an overview of what is necessary for an efficient IT-based workflow including the clinical question, the choice of modalities and investigation protocols, structured reports, and clinical classification. This is particularly interesting for potential providers, who are keen to apply new soft skills to support imaging diagnostic processes.

Results

The use of AI-supported diagnostic imaging could result in a paradigm shift from a modality-oriented to a clinical problem-oriented workflow. In order to streamline trauma, degeneration, inflammation, and oncology-MSK diagnostic imaging, the potential benefits of AI-based workflow optimization should be taken advantage of. The following article describes a five-point plan combining patient expectations and specialized radiological standards with respect to investigation protocols and reports. Moreover, this AI-based strategy could help to improve interdisciplinary networking, e. g., boards etc., and facilitate the required leap in quality towards “personalized precision medicine” for the patient. According to the global discussion, there is a need to point out that it is not currently realistic to replace doctors with AI.

Key Points

  • AI as support-system has a paramount clinical impact

  • AI in the daily routine could be for work-flow-support (processing) – a physician-replacement is un-realistic yet

  • Standardization of work-flow by AI could be an important contribution of quality assurance

Citation Format

  • Braunschweig R, Kildal D, Janka R. Artificial intelligence (AI) in diagnostic imaging . Fortschr Röntgenstr 2024; 196: 664 – 670

Keywords

artificial intelligence - musculoskeletal imaging - IT-based processing


Publication History

Received: 15 February 2023

Accepted: 17 October 2023

Article published online:
12 February 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 European Society of Radiology: What the radiologist should know about artificial intelligence – an ESR white paper. Insights Imaging 2019; 10: 44 DOI: 10.1186/s13244-019-0738-2.
    CrossrefPubMedGoogle Scholar
  • 2 Gyftopoulos S. et al. Artificial intelligence in musculoskeletal imaging: current status und future directions. Am J Roentgenol 2019; 213 (03) 506-513 DOI: 10.2214/Am J Roentgenol.19.21117.
    CrossrefPubMedGoogle Scholar
  • 3 Hyun JY. et al. Medical image analysis using artificial intelligence. Progress in Medical Physics 2019; 30 (02) 49-58
    CrossrefPubMedGoogle Scholar
  • 4 Russell SJ, Norvig P. Artificial Intelligence: A modern approach Prentice Hall. 1995: 932
    Google Scholar
  • 5 Eberl U. Smarte Maschinen: Wie Künstliche Intelligenz unser Leben verändert. Carl Hanser; 2016
    Google Scholar
  • 6 Holzinger A, Langs G, Denk H. et al. Causability and explainability of artificail intelligence in medicine. Wiley interdisciplinary Reviews-Data Mining and Knowledge-Discovery 2019; 9: 1312 DOI: 10.1002/widm.1312.
    CrossrefPubMedGoogle Scholar
  • 7 Hosny A. et al. Artificial intelligence in radiology. Nat rev Cancer 2018; 18: 500-510
    CrossrefPubMedGoogle Scholar
  • 8 Pesapane F. et al. Artificial intelligence as a medical device in radiology: ethical an regulatory issues in Europe and in the United States. Insights Imaging 2018; 9: 745-753
    CrossrefPubMedGoogle Scholar
  • 9 Precht RD. „Künstliche Intelligenz und der Sinn des Lebens“. Goldmann Verlag. Goldmann Verlag. 2020
    Google Scholar
  • 10 Attenberger UI. et al. Wie geht Radiomics eigentlich?. Fortschr. Röntgenstr 2021; 193: 652-657
    PubMedGoogle Scholar
  • 11 Al Masni MA. et al. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD systemComput Methods Programs. Biomed 2018; 157: 85-94
    PubMedGoogle Scholar
  • 12 Salem M. et al. A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis. Neuroimage Clin 2018; 17: 607-615
    CrossrefPubMedGoogle Scholar
  • 13 Titano JJ. et al. Automated deep-neural-network surveillance of cranial images for acute neurological events. Mat Med 2018; 24: 1337-1341
    PubMedGoogle Scholar
  • 14 Yeeps-Calderon F. et al. Automatically measuring brain ventricular volume within PACS using artificial intelligence. PloS One 2018; 13: e=193152
    PubMedGoogle Scholar
  • 15 Chilamkurthy S. et al. Deep learning algorithms for detection of critical findings in head CT scans. A retrospective study. Lancet 2018; DOI: 10.1007/s00330-018-5698-2.
    CrossrefPubMedGoogle Scholar
  • 16 Choi W. et al. Radiomics abalysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med Phys 2018; 45: 1537-1549
    CrossrefPubMedGoogle Scholar
  • 17 Orooji M. et al. Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. J Med Imaging 2018; 5: 024501
    CrossrefPubMedGoogle Scholar
  • 18 Gluchowski P, Chamoni P. Analytische Informationssysteme: Business Intelligence-Technologien und Anwendungen. 5. Überarbeitete Auflage. Springer; 2016
    CrossrefGoogle Scholar
  • 19 Forsting M. Künstliche Intelligenz mit der Radiologie als Vorreiter für Super-Diagnostics. Fortschr. Röntgenstr 2019; 191 (01) 73-78
    PubMedGoogle Scholar
  • 20 Nichols A. et al. Machine learning: application of artificial intelligence to imaging and diagnosis. Biophys Rev 2018; DOI: 10.1007/s12551-018-0449-9.
    CrossrefPubMedGoogle Scholar
  • 21 Abdollah H. et al. Cochlea CT radiomics predicts chemoradiotherapy induced sensoneural hearing loss in head and neck cancer patients: A machine learning and multi-variable modelling study. Phys Med 2018; 45: 192-197
    CrossrefPubMedGoogle Scholar
  • 22 Lambin P, Leijenaar RTH, Deist TM. et al. Radiomics: the bridgebetween medical imaging and personalized medicine. Nature Reviews Clinical Oncology 2017; 14: 749-762 DOI: 10.1038/nrclinonc.2017.141.
    CrossrefPubMedGoogle Scholar
  • 23 Burns JE, Yao J, Summers RM. Artificial intelligence in musculoskeletal imaging: a paradigm shift. J of Bone and Mineral Research 2019; 35 (01) 28-35 DOI: 10.1002/jbmr.3849.
    CrossrefPubMedGoogle Scholar
  • 24 Brockhaus Enzyklopädie. Band 10; 19. Auflage. Bertelsmann; 590
    Google Scholar
  • 25 Maio G. Mittelpunkt Mensch: Lehrbuch der Ethik in der Medizin. 2. Auflage. Schattauer; 2017
    Google Scholar
  • 26 Al Badawy EA. et al. Deep learning for segmentation of brain tumors: Impact of cross institutional training and testing. Med Phys 2018; 45: 1150-1158
    CrossrefPubMedGoogle Scholar
  • 27 Leitlinie der Bundesärztekammer zur Qualitätssicherung in der Röntgendiagnostik Deutsches Ärzteblatt 30. Mai 2023. DOI: 10.3238/arztebl.2023.LL_Qualitätssicherung_Röntgendiagnost
    CrossrefPubMedGoogle Scholar
  • 28 Freyschmidt J. et al. Knochentumoren. 2 Auflage. Springer; 1998
    Google Scholar
  • 29 Bunck AC. et al. Strukturierte Befundung in der Schnittbilddiagnostik des Herzens. Fortschr Röntgenstr 2020; 192 (01) 27-37 DOI: 10.1055/a-0998-4116.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 30 Wigge P. Anforderungen an den Facharztstandard. Fortschr. Röntgenstr 2021; 193 (04) 480-484
    Article in Thieme ConnectPubMedGoogle Scholar
  • 31 Al Ajmi E. et al. Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm. Eur Radiol 2018; 28: 2604-2611
    CrossrefPubMedGoogle Scholar
  • 32 Bolm-Audorff U. “Konsensempfehlungen zur Zusammenhangsbegutachtung der BK 2108/2110”. Trauma und Berufskrankheiten 2005; 3 (03) 211-252 DOI: 10.1007/s10039-005-1027-5.
    CrossrefPubMedGoogle Scholar