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Klin Monbl Augenheilkd 2020; 237(12): 1438-1441
DOI: 10.1055/a-1303-6482
Übersicht

Künstliche Intelligenz und Big Data

Article in several languages: English | deutsch
Soenke Langner 
Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie, Universitätsmedizin Rostock, Deutschland
,
Ebba Beller
Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie, Universitätsmedizin Rostock, Deutschland
,
Felix Streckenbach
Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie, Universitätsmedizin Rostock, Deutschland
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Zusammenfassung

In der Augenheilkunde und der Radiologie spielen Bilddaten eine entscheidende Rolle. Für die Auswertung dieser Datensätze stehen in der radiologischen Diagnostik und Forschung zunehmend „deep-learning“-basierte Algorithmen zur Verfügung. Anwendungsgebiete und die Bedeutung für die radiologische Bildgebung in der Ophthalmologie werden aufgezeigt.

Schlüsselwörter

künstliche Intelligenz - Deep Learning - MRT - Big Data


Publication History

Received: 26 August 2020

Accepted: 03 November 2020

Article published online:
19 November 2020

© 2020. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References/Literatur

  • 1 Hosny A, Parmar C, Quackenbush J. et al. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18: 500-510
    CrossrefPubMedSearch in Google Scholar
  • 2 Wong SH, Al-Hasani H, Alam Z. et al. Artificial intelligence in radiology: how will we be affected?. Eur Radiol 2019; 29: 141-143
    CrossrefPubMedSearch in Google Scholar
  • 3 Meyer-Base A, Morra L, Tahmassebi A. et al. AI-Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer. J Magn Reson Imaging 2020;
    CrossrefPubMedSearch in Google Scholar
  • 4 Rizzo S, Botta F, Raimondi S. et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2018; 2: 36
    CrossrefPubMedSearch in Google Scholar
  • 5 Chassagnon G, Vakalopoulou M, Paragios N. et al. Artificial intelligence applications for thoracic imaging. Eur J Radiol 2020; 123: 108774
    CrossrefPubMedSearch in Google Scholar
  • 6 Montagnon E, Cerny M, Cadrin-Chênevert A. et al. Deep learning workflow in radiology: a primer. Insights Imaging 2020; 11: 22
    CrossrefPubMedSearch in Google Scholar
  • 7 Guthoff RF, Stachs O. [Artificial Intelligence in Ophthalmology]. Klin Monbl Augenheilkd 2019; 236: 1397-1398
    Thieme ConnectPubMedSearch in Google Scholar
  • 8 Bartschat A, Allgeier S, Bohn S. et al. [Digital Image Processing and Deep Neural Networks in Ophthalmology – Current Trends]. Klin Monbl Augenheilkd 2019; 236: 1399-1406
    Thieme ConnectPubMedSearch in Google Scholar
  • 9 Du XL, Li WB, Hu BJ. Application of artificial intelligence in ophthalmology. Int J Ophthalmol 2018; 11: 1555-1561
    PubMedSearch in Google Scholar
  • 10 Ting DSW, Pasquale LR, Peng L. et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019; 103: 167-175
    CrossrefPubMedSearch in Google Scholar
  • 11 Pinto Dos Santos D, Baeßler B. Big data, artificial intelligence, and structured reporting. Eur Radiol Exp 2018; 2: 42
    CrossrefPubMedSearch in Google Scholar
  • 12 Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. JAMA 2018; 319: 1317-1318
    CrossrefPubMedSearch in Google Scholar
  • 13 Yabuuchi H, Kamitani T, Sagiyama K. et al. Clinical application of radiation dose reduction for head and neck CT. Eur J Radiol 2018; 107: 209-215
    CrossrefPubMedSearch in Google Scholar
  • 14 Hegenscheid K, Kühn JP, Völzke H. et al. Whole-body magnetic resonance imaging of healthy volunteers: pilot study results from the population-based SHIP study. Rofo 2009; 181: 748-759
    Thieme ConnectPubMedSearch in Google Scholar
  • 15 Bamberg F, Kauczor HU, Weckbach S. et al. Whole-Body MR Imaging in the German National Cohort: Rationale, Design, and Technical Background. Radiology 2015; 277: 206-220
    CrossrefPubMedSearch in Google Scholar
  • 16 Nell C, Bülow R, Hosten N. et al. Reference values for the cervical spinal canal and the vertebral bodies by MRI in a general population. PLoS One 2019; 14: e0222682
    CrossrefPubMedSearch in Google Scholar
  • 17 Bülow R, Ittermann T, Dörr M. et al. Reference ranges of left ventricular structure and function assessed by contrast-enhanced cardiac MR and changes related to ageing and hypertension in a population-based study. Eur Radiol 2018; 28: 3996-4005
    CrossrefPubMedSearch in Google Scholar
  • 18 Schmidt P, Kempin R, Langner S. et al. Association of anthropometric markers with globe position: A population-based MRI study. PLoS One 2019; 14: e0211817
    CrossrefPubMedSearch in Google Scholar
  • 19 Shieh AC, Blandford AD, Hwang CJ. et al. Age-related Changes in Globe Position. Ophthalmic Plast Reconstr Surg 2019; 35: 155-158
    CrossrefPubMedSearch in Google Scholar
  • 20 Chaker L, Buitendijk GH, Dehghan A. et al. Thyroid function and age-related macular degeneration: a prospective population-based cohort study – the Rotterdam Study. BMC Med 2015; 13: 94
    CrossrefPubMedSearch in Google Scholar
  • 21 Ittermann T, Dörr M, Völzke H. et al. High serum thyrotropin levels are associated with retinal arteriolar narrowing in the general population. Thyroid 2014; 24: 1473-1478
    CrossrefPubMedSearch in Google Scholar
  • 22 Ittermann T, Jürgens C. Thyroid function: a new road to understanding age-related macular degeneration?. BMC Med 2015; 13: 95
    CrossrefPubMedSearch in Google Scholar
  • 23 Chaganti S, Mundy K, DeLisi MP. et al. Assessment of Orbital Computed Tomography (CT) Imaging Biomarkers in Patients with Thyroid Eye Disease. J Digit Imaging 2019; 32: 987-994
    CrossrefPubMedSearch in Google Scholar
  • 24 Le WT, Maleki F, Romero FP. et al. Overview of Machine Learning: Part 2: Deep Learning for Medical Image Analysis. Neuroimaging Clin N Am 2020; 30: 417-431
    CrossrefPubMedSearch in Google Scholar
  • 25 Soffer S, Ben-Cohen A, Shimon O. et al. Convolutional Neural Networks for Radiologic Images: A Radiologistʼs Guide. Radiology 2019; 290: 590-606
    CrossrefPubMedSearch in Google Scholar
  • 26 Maier A, Syben C, Lasser T. et al. A gentle introduction to deep learning in medical image processing. Z Med Phys 2019; 29: 86-101
    CrossrefPubMedSearch in Google Scholar
  • 27 Yamashita R, Nishio M, Do RKG. et al. Convolutional neural networks: an overview and application in radiology. Insights Imaging 2018; 9: 611-629
    CrossrefPubMedSearch in Google Scholar
  • 28 Willemink MJ, Noël PB. The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 2019; 29: 2185-2195
    CrossrefPubMedSearch in Google Scholar
  • 29 Chaudhari AS, Sandino CM, Cole EK. et al. Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. J Magn Reson Imaging 2020;
    CrossrefPubMedSearch in Google Scholar
  • 30 Chaudhari AS, Fang Z, Kogan F. et al. Super-resolution musculoskeletal MRI using deep learning. Magn Reson Med 2018; 80: 2139-2154
    CrossrefPubMedSearch in Google Scholar
  • 31 Lee YH. Efficiency Improvement in a Busy Radiology Practice: Determination of Musculoskeletal Magnetic Resonance Imaging Protocol Using Deep-Learning Convolutional Neural Networks. J Digit Imaging 2018; 31: 604-610
    CrossrefPubMedSearch in Google Scholar
  • 32 Jiang H, Wang S, Xian J. et al. Efficacy of PROPELLER in reducing ocular motion artefacts and improving image quality of orbital MRI at 3 T using an eye surface coil. Clin Radiol 2019; 74: 734.e7-734.e12
    CrossrefPubMedSearch in Google Scholar
  • 33 Paul K, Graessl A, Rieger J. et al. Diffusion-sensitized ophthalmic magnetic resonance imaging free of geometric distortion at 3.0 and 7.0 T: a feasibility study in healthy subjects and patients with intraocular masses. Invest Radiol 2015; 50: 309-321
    CrossrefPubMedSearch in Google Scholar
  • 34 Lin W, Nielsen T, Qin Q. et al. Real-time motion correction in two-dimensional multislice imaging with through-plane navigator. Magn Reson Med 2014; 71: 1995-2005
    CrossrefPubMedSearch in Google Scholar
  • 35 Hamm CA, Beetz NL, Savic LJ. et al. [Artificial intelligence and radiomics in MRI-based prostate diagnostics]. Radiologe 2020; 60: 48-55
    CrossrefPubMedSearch in Google Scholar
  • 36 Shaver MM, Kohanteb PA, Chiou C. et al. Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging. Cancers (Basel) 2019; 11: 829
    CrossrefPubMedSearch in Google Scholar
  • 37 Jansen J, Schreurs R, Dubois L. et al. Orbital volume analysis: validation of a semi-automatic software segmentation method. Int J Comput Assist Radiol Surg 2016; 11: 11-18
    CrossrefPubMedSearch in Google Scholar
  • 38 Hsung TC, Lo J, Chong MM. et al. Orbit Segmentation by Surface Reconstruction With Automatic Sliced Vertex Screening. IEEE Trans Biomed Eng 2018; 65: 828-838
    CrossrefPubMedSearch in Google Scholar
  • 39 Aghdasi N, Li Y, Berens A. et al. Efficient orbital structures segmentation with prior anatomical knowledge. J Med Imaging (Bellingham) 2017; 4: 034501
    CrossrefPubMedSearch in Google Scholar
  • 40 van Timmeren JE, Cester D, Tanadini-Lang S. et al. Radiomics in medical imaging-“how-to” guide and critical reflection. Insights Imaging 2020; 11: 91
    Search in Google Scholar
  • 41 Mannil M, von Spiczak J, Manka R. et al. Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography: Unveiling the Invisible. Invest Radiol 2018; 53: 338-343
    CrossrefPubMedSearch in Google Scholar