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

Artificial Intelligence and 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
› Author Affiliations
› Further Information
Also available at
    Permissions and Reprints

Abstract

Medical images play an important role in ophthalmology and radiology. Medical image analysis has greatly benefited from the application of “deep learning” techniques in clinical and experimental radiology. Clinical applications and their relevance for radiological imaging in ophthalmology are presented.

Key words

artificial intelligence - deep learning - MRI - 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