Künstliche Intelligenz und Big Data

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

• References/Literatur

• 1 Hosny A, Parmar C, Quackenbush J. et al. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18: 500-510
• 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
• 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;
• 4 Rizzo S, Botta F, Raimondi S. et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2018; 2: 36
• 5 Chassagnon G, Vakalopoulou M, Paragios N. et al. Artificial intelligence applications for thoracic imaging. Eur J Radiol 2020; 123: 108774
• 6 Montagnon E, Cerny M, Cadrin-Chênevert A. et al. Deep learning workflow in radiology: a primer. Insights Imaging 2020; 11: 22
• 7 Guthoff RF, Stachs O. [Artificial Intelligence in Ophthalmology]. Klin Monbl Augenheilkd 2019; 236: 1397-1398
• 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
• 9 Du XL, Li WB, Hu BJ. Application of artificial intelligence in ophthalmology. Int J Ophthalmol 2018; 11: 1555-1561
• 10 Ting DSW, Pasquale LR, Peng L. et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019; 103: 167-175
• 11 Pinto Dos Santos D, Baeßler B. Big data, artificial intelligence, and structured reporting. Eur Radiol Exp 2018; 2: 42
• 12 Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. JAMA 2018; 319: 1317-1318
• 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
• 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
• 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
• 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
• 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
• 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
• 19 Shieh AC, Blandford AD, Hwang CJ. et al. Age-related Changes in Globe Position. Ophthalmic Plast Reconstr Surg 2019; 35: 155-158
• 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
• 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
• 22 Ittermann T, Jürgens C. Thyroid function: a new road to understanding age-related macular degeneration?. BMC Med 2015; 13: 95
• 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
• 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
• 25 Soffer S, Ben-Cohen A, Shimon O. et al. Convolutional Neural Networks for Radiologic Images: A Radiologistʼs Guide. Radiology 2019; 290: 590-606
• 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
• 27 Yamashita R, Nishio M, Do RKG. et al. Convolutional neural networks: an overview and application in radiology. Insights Imaging 2018; 9: 611-629
• 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
• 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;
• 30 Chaudhari AS, Fang Z, Kogan F. et al. Super-resolution musculoskeletal MRI using deep learning. Magn Reson Med 2018; 80: 2139-2154
• 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
• 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
• 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
• 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
• 35 Hamm CA, Beetz NL, Savic LJ. et al. [Artificial intelligence and radiomics in MRI-based prostate diagnostics]. Radiologe 2020; 60: 48-55
• 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
• 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
• 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
• 39 Aghdasi N, Li Y, Berens A. et al. Efficient orbital structures segmentation with prior anatomical knowledge. J Med Imaging (Bellingham) 2017; 4: 034501
• 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
• 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