Wat betekent A.I. voor de radiologie?
•
2 j'aime•562 vues
Wat zijn de mogelijkheden van AI voor radiologie, en wat is de impact van deze nieuwe ontwikkeling op het specialisme?
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à Wat betekent A.I. voor de radiologie?
Similaire à Wat betekent A.I. voor de radiologie? (20)
Plus de Erik R. Ranschaert, MD, PhD
Plus de Erik R. Ranschaert, MD, PhD (20)
Dernier
Dernier (20)
Wat betekent A.I. voor de radiologie?
- 1. Wat betekent A.I. voor de radiologie? Erik Ranschaert, MD, PhD, CIIP President EuSoMII @eranrad
- 2. Disclosures • President EuSoMII • CMO Diagnose.me • Advisory Board MedicalPHIT PACS congres 2018
- 3. EOS, Oct.2017 “We gaan de komende jaren naar een technologiegestuurde wetenschap van objectieve data. Het zal nog steeds radiologie heten, maar de term zal een andere lading dekken.”
- 5. Recent changes Cloud technology Processing power, GPU’s Big data A.I. PACS congres 2018
- 6. A.I. Terminology PACS congres 2018
- 7. What is Machine Learning? PACS congres 2018
- 8. ML vs DL • Machine Learning (ML) learns computers “to think” without being programmed • ML makes advanced statistical calculations with algorithms – it makes “prospections” based upon skills learned from “training data” – ML can deal with large, complex datasets • DL is the type of ML based upon multiple layers “Layered cake” D E E P PACS congres 2018
- 9. Neural Network model • “Multistage information distillation” model to “purify” information • Input layer = fed with information • The “hidden layers” have artificial neurons combining signals and calculating different “weights” (parameters) for the data in each neuron = mathematical framework • The output of the layer is passed through to the next layer. • Output layer = “fully connected layer” = classifier PACS congres 2018
- 10. Deep Learning Layers PACS congres 2018
- 11. Backpropagation PACS congres 2018 Goal: finding the right values for these weights Score is used as feedback signal to adjust the value of the weights, to finally lower the loss score = backpropagation
- 12. Convolutional Neural Network CNN or ConvNet PACS congres 2018
- 13. Radiologists are not unfamiliar with AI • 1963 – 2013: first 50 years failed • 2012 ImageNet competition: AlexNet CNN gave a dramatic reduction in image classification error rate 26 ->15% • 2016 Geoffry Hinton: “it's quite obvious that we should stop training radiologists” • Last 2–3 yrs: increased activity in development of DL algorithms for radiology • For narrow-based tasks the accuracy rates of CNNs surpass those of humans (e.g. nodule detection) PACS congres 2018
- 14. Current trend for deep learning. Fei Jiang et al. Stroke Vasc Neurol 2017;2:230-243 PACS congres 2018
- 15. Challenges MICCAI 2018 NIPS 2018 Neural Information Processing SystemsMedical Image Computing & Computer Assisted Intervention • >1600 attendees • >1000 submissions • +33% vs. 2017 • 8000 attendees in 2017 • 3240 submissions PACS congres 2018
- 16. Radiology AI challenges RSNA ML challenges • 2017: Pediatric bone age • 2018: Pneumonia detection challenge – Large NIH data set – ML showcase RSNA 2018 – 30.000 USD donation by Kaggle • 1400 teams • 346 submissions Prices • 1st Place - $ 12,000 • 2nd Place - $ 7,000 • 3rd Place - $ 4,000 • 4th Place - 10th Places - $ 1,000 each PACS congres 2018 https://rsna2018.rsna.org/dailybulletin/index.cfm?pg=18wed42 http://press.rsna.org/timssnet/media/pressreleases/14_pr_target.cfm?ID=2059
- 17. • 100.000 anonymised X-ray images and corresponding data • Pneumonia accounts for 15% of deaths of children < 5 yrs • In 2015 920.000 children died from pneumonia • In top 10 causes of death in USA PACS congres 2018
- 18. ML Showcase RSNA 2018 PACS congres 2018
- 19. Supervised Learning PACS congres 2018 • Machine-learning tasks in radiology now mostly rely on supervised learning (> 90%) • In supervised learning, the algorithm is presented with data (input; here, images) that have already been mapped by hand to the correct labels (output; here, the label to be predicted by AI)
- 22. Unsupervised learning PACS congres 2018
- 23. Medical imaging applications in which DL has achieved state-of-the-art results • Mammographic mass classification (Kooi et al., 2016), • Segmentation of lesions in the brain (top ranking in BRATS, ISLES and MRBrains challenges, image from Ghafoorian et al. • Leak detection in airway tree segmentation (Charbonnier et al., 2017) • Diabetic retinopathy classification (Kaggle Diabetic Retinopathy challenge 2015, image from van Grinsven et al. (2016) • Prostate segmentation (top rank in PROMISE12 challenge) • Nodule classification (top ranking in LUNA16 challenge), • Breast cancer metastases detection in lymph nodes (top ranking and human expert performance in CAMELYON16) • Human expert performance in skin lesion classification (Esteva et al., 2017), and • State-of-the-art bone suppression in x-rays, image from Yang et al. (2016). Litjens, G. et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. http://doi.org/10.1016/j.media.2017.07.005 PACS congres 2018
- 24. AI Startups PACS congres 2018 22% of companies focus on CT, 13% each on mammography and MRI, 9% on ultrasound, and 3% on X-rays and nuclear imaging. Remaining 31% is not focusing on a modality https://doi.org/10.1016/j.jacr.2018.09.050
- 25. UK can lead in Radiology AI. Here’s how...Hugh Harvey, 2017, Medium - http://bit.ly/2CoKJE8 Industry partnerships • Dominant industry vendors formed strategic partnerships with academic institutions. • GE, IBM Watson, Philips and many others have created deals to allow data access in return for funding research. • The American College of Radiologists (ACR) has announced it’s own Data Science Institute. • In Europe we can see EU/industry partnerships forming with the academy PACS congres 2018
- 26. Is the future of radiology in the hands of robots?
- 29. Fake news? • “Algorithm can diagnose pneumonia better than radiologists” ≠ • “Algorithm can detect pneumonia from chest X-rays better than radiologists” PACS congres 2018
- 30. Analysis Luke Oakden-Rayner • The training dataset has labels that don’t really match the images, it has questionable relevance. • For detecting pneumonia-like image features on chest x-rays, this system performs at least on par with human experts. https://lukeoakdenrayner.wordpress.com/2018/01/24/chexnet-an-in-depth-review/ PACS congres 2018
- 31. Canadian Association of Radiologists Journal 2018 69, 120-135DOI: (10.1016/j.carj.2018.02.002) Copyright © 2018 The Authors Terms and Conditions Tasks of radiologists
- 32. AI Patient and Referring Provider Imaging Appropriateness & Utilization Patient Scheduling Imaging Protocol selection Imaging Modality operations, QA, dose reduction Hanging protocols, Optimization staffing & worklist Interpretation and reporting Communication and billing Source: JM Morey et al.Applications of AI Beyond Image Interpretation, Springer 2018 – in press A.I. Imaging Value Chain
- 33. Reduced acquistion time • Collaboration between NYU (CAI2R) and Facebook (FAIR) to make MRI scans 10x faster with neural networks (AI) • Automated calculation of missing information gaps with DL • 10.000 clinical cases with 3 million images PACS congres 2018
- 34. Left: Standard high dose CT at 12.4mGy. Middle: Ultra-low dose CT at 1.3mGy. Right: AI-enhanced ultra-low dose CT at 1.3mGy. Diagnostic image quality between the left and right images was rated as comparable by independent radiologists, despite a significant dose reduction of 11.1mGy. The middle image is noisy and non-diagnostic. Images courtesy of Algomedica. SIIM 2017 Poster
- 35. P. Lakhani et al. JACR https://doi.org/10.1016/j.jacr.2017.09.044 Automated hanging protocol • Radiologist opens study • ML assistant creates optimal hanging protocol • Radiologist makes report and sends it to RIS or EHR PACS congres 2018
- 36. Triage and prioritization • Detection of Intracranial Hemorrhage (ICH) • Algorithm processes exam and generates binary output (negative or positive ICH). • If results are positive, priority of study is upgraded to “stat”. • The reading list is updated in real-time. • Possible to reduce TAT up to 96% for head CT exams npj Digital Medicine (2018) 1:9 ; doi:10.1038/s41746-017-0015-z PACS congres 2018
- 37. FINDINGS T11-T12 • X1 X2 X3 X4 X5 X6 • X7 X8 X9 X10 X11 X12 T12-L1 • X1 X2 X3 X4 X5 X6 • X7 X8 X9 X10 X11 X12 L1-L2 • X1 X2 X3 X4 X5 X6 • X7 X8 X9 X10 X11 X12 L2-L3 • X1 X2 X3 X4 X5 X6 • X7 X8 X9 X10 X11 X12 L3-L4 • X1 X2 X3 X4 X5 X6 • X7 X8 X9 X10 X11 X12 L4-L5 • X1 X2 X3 X4 X5 X6 • X7 X8 X9 X10 X11 X12 L5-S1 • X1 X2 X3 X4 X5 X6 • X7 X8 X9 X10 X11 X12 (COMMON DATA ELEMENTS) Source: K. Dreyer, Clinical Data Science Interagency Working Group on Medical Imaging National Science and Technology Council 2016, https://slideplayer.com/slide/11642143/ PACS congres 2018
- 38. Google and others? • Google (GCP), Amazon (AWS), Microsoft believe that delivering AI through the cloud will be a big, lucrative trend in computing in coming years. PACS congres 2018
- 39. Amazon • Amazon sees a new grow market in HC and will offer HC services • Own health insurance – first for Amazon employees (500.000 !) • “We have the size for influencing the insurance companies. The problem of the rising costs in HC is only becoming worse. Innovation is necessary”. Chief Technology Officer Amazon NRC, 6 april 2018 PACS congres 2018
- 40. Human bias & transparency • Training data can be biased – Google Photos image classifier tagged black person as gorilla • What is the real purpose of the algorithm? – Transparency of “black box” https://medium.com/@DrHughHarvey/building-ethical-ai-in-healthcare-why-we-must-demand-it-ca60f4d28412 PACS congres 2018
- 41. AI-bias • Automation bias (automation induced complacency) – the tendency to over-rely on automation, failure to recognise new errors that AI can introduce Carol Beer, Little Britain “The exemplary receptionist” The computer says no... PACS congres 2018
- 42. Adversarial Examples (AE’s) • Inputs to ML models that have been crafted to force the model to make a classification error. • AE’s can be crafted to be very effective... without being visible to human eyes! PACS congres 2018
- 43. Adversarial noise • Although deep neural networks have been able to achieve high levels of accuracy in the ImageNet competition distinguishing between highly similar species of animals, they also can be fooled by adversarial noise that is imperceptible to the human eye PACS congres 2018 2018 American College of Radiology 1546-1440/18/$36.00 n https://doi.org/10.1016/j.jacr.2018.10.008
- 45. Major roadblocks for AI Sli.do live survey, EuSoMII Annual Meeting, Rotterdam, Nov 3, 2018
- 46. Automated analysis Integration of quantitative data in SR Seamless integration of AI with PACS and EMR, “all-in-one” Interoperability Facilitates training of new algorithms & applications Data Swiss-knife for radiologists: all-in-one
- 48. Curtis Langlotz “Artificial intelligence will not replace radiologists. Yet, those radiologists who use AI will replace the ones who don’t.” Curtis Langlotz, Professor of Radiology and Biomedical Informatics at Stanford University, GPU Tech Conference in San Jose, May 2017 PACS congres 2018
- 49. PACS congres 2018 springer.com 1st ed. 2019, X, 396 p. 105 illus., 93 illus. in color. Printed book Hardcover 119,99 € | £109.99 | $149.99 128,39 € (D) | 131,99 € (A) | CHF[1] 141,50 eBook 101,14 € | £87.50 | $109.00 101,14 € (D) | 101,14 € (A) | CHF[2] 113,00 Available from your library or springer.com/shop MyCopy [3] Printed eBook for just € | $ 24.99 springer.com/ mycopy Erik R. Ranschaert, Sergey Morozov, Paul R. Algra (Eds.) Artificial Intelligence in Medical Imaging Opportunities, Applications and Risks Provides a thorough overview of the impact of artificial intelligence (AI ) on medical imaging I ncludes contributions from radiologists and I T professionals, ensuring a multidisciplinary approach Makes practical recommendations for the use of AI technology for both clinical and nonclinical applications This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging.After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
- 51. Join EuSoMII now! www.eusomii.org PACS congres 2018