6, Cochlear Implants International, Vol. Viewer, git clone git://github.com/slowvak/MachineLearningForMedicalImages.git, http://ww2.frost.com/news/press-releases/600-m-6-billion-artificial-intelligence-systems-poised-dramatic-market-expansion-healthcare/, https://open.library.ubc.ca/collections/ubctheses/24/items/1.0305854, http://deeplearning.net/software/pylearn2/, https://cran.r-project.org/web/packages/Boruta/index.html, https://cran.r-project.org/web/packages/GMMBoost/index.html, https://cran.r-project.org/web/packages/h2o/index.html, https://01.org/intel-deep-learning-framework, http://cs.stanford.edu/people/karpathy/convnetjs/, Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors, Low-Dose CT Screening for Lung Cancer: Evidence from 2 Decades of Study, Quantitative CT Analysis of Diffuse Lung Disease, Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement, Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists, Translation of Quantitative Imaging Biomarkers into Clinical Chest CT, Automated Triaging of Adult Chest Radiographs, Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide, Fostering a Healthy AI Ecosystem for Radiology: Conclusions of the 2018 RSNA Summit on AI in Radiology, Three-dimensional Distribution of Muscle and Adipose Tissue of the Thigh at CT: Association with Acute Hip Fracture, Imaging-Related Risk Factors for Bleeding Complications of US-Guided Native Renal Biopsy: A Propensity Score Matching Analysis, The Role of Artificial Intelligence in Interventional Oncology: A Primer, Machine Learning Methods for Classifying Mammographic Regions Using the Wavelet Transform and Radiomic Texture Features, Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future Use, Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network, A comprehensive survey on machine learning for networking: evolution, applications and research opportunities, Automated ASPECT rating: comparison between the Frontier ASPECT Score software and the Brainomix software, Artificial intelligence in medical imaging: threat or opportunity? 6, No. More recently, machines have demonstrated the capability to learn and even master tasks that were thought to be too complex for machines, showing that machine learning algorithms are potentially useful components of computer-aided diagnosis and decision support systems. 1, No. 116, 1 October 2019 | Radiology, Vol. Computer-Aided System Application Value for Assessing Hip Development. 42, No. Black line is the best hyperplane which can separate the two classes of data with maximum margin. 3, 27 March 2019 | Radiology: Artificial Intelligence, Vol. One popular way to estimate the accuracy of a machine learning system when there is a limited dataset is to use the cross-validation technique (38,39). 46, No. The pooling layer is another type of layer that is important to CNNs. In the past, activation functions were designed to simulate the sigmoidal activation function of a neuron, but current activation layers often have a much simpler function. 1, 20 November 2017 | Radiology, Vol. The set of weights or decision points for the model is updated until no substantial improvement in performance is achieved. 2, The British Journal of Radiology, Vol. As described earlier, during the training phase, examples are presented to the neural network system, the error for each example is computed, and the total error is computed. 1-D distributions of the two-classes after projection are also shown along the line perpendicular to the projection direction. In addition, with random forests, only a subset of the total number of features is randomly selected and the best split feature from the subset is used to split each node in a tree—unlike with bagging, whereby all features are considered for splitting a node. The axes are generically labeled feature 1 and feature 2 to reflect the first two elements of the feature vector. 8, Zeitschrift für Medizinische Physik, Vol. 7, Journal of the American College of Radiology, Vol. Right figure shows corresponding graph constructed from the blob image. Key contributions and common characteristics of machine learning techniques in radiology are discussed. You are here: Home / Resources / Machine learning in radiology—reflections and predictions Leading up to RSNA 2017, we published a report discussing our findings from talking to radiologists about where they see an added value of machine learning (ML) in their daily work. I have been excited about conversational agents for some time, previously building an iOS chatbot simulating a human radiologist powered by Watson.. As a delightful weekend project, I sat down with my glorious corgi and lots of coffee and built a radiology assistant for Google Home. Machine Learning in Medical Imaging. Personally, I want to be able use machine learning (ML) capabilities in some of my iOS apps using Apple’s CoreML framework as well. 132, No. 1, Journal of Magnetic Resonance Imaging, Vol. The second step predicts the fMRI image as a linear combination of the fMRI signatures associated with each of these intermediate semantic features. Get the latest public health information from CDC: https://www.coronavirus.gov, Get the latest research information from NIH: https://www.nih.gov/coronavirus, Find NCBI SARS-CoV-2 literature, sequence, and clinical content: https://www.ncbi.nlm.nih.gov/sars-cov-2/. It will then try to adjust one of the weights to see whether this reduces the number of wrong interpretations. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks, Deep Learning in Radiology: Recent Advances, Challenges and Future Trends. Machine learning radiology technology will be able to reduce human error, identifying image information that may be indistinguishable to the human eye. 16, No. In this example case, the algorithm system would be given several brain tumor images on which the tumors were labeled as benign or malignant. An important question to ask is “How many examples of each class of the thing do I need to learn it well?” It is easy to see that having too few examples will prevent a computer—or a person, for that matter—from recognizing those features of an object that allow one to distinguish between the different classes of that object (35). 1, Biomedical Physics & Engineering Express, Vol. 1, The Lancet Respiratory Medicine, Vol. Model: The set of weights or decision points learned by a machine learning system. This capability gives support vector machine algorithms a big advantage over many other machine learning methods. 2, Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, Vol. 1, Journal of Applied Clinical Medical Physics, Journal of Alzheimer's Disease, Vol. With unsupervised learning, data (eg, brain tumor images) are processed with a goal of separating the images into groups—for example, those depicting benign tumors and those depicting malignant tumors. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. 6, Journal of Magnetic Resonance Imaging, Vol. 2013 Apr;40(4):042301. doi: 10.1118/1.4793255. 22, No. 2, No. COMMENTARYMy review of a paper in the AJNR on machine learning and the future of Radiology. Figure 4. Please enable it to take advantage of the complete set of features! 100, No. 1, Journal of the Mechanical Behavior of Biomedical Materials, Vol. Epub 2020 Jul 15. 290, No. 1 It has recently become the dominant form of machine learning, due to a convergence of theoretic advances, openly available computer software, and … 2, PLOS Computational Biology, Vol. Some of the most commonly used libraries for machine learning are summarized in the ,Table. Supervised machine learning is so named because examples of each type of thing to be learned are required. Each node has an activation function (f) that computes its output (y) by using x and w as inputs. Machine learning is a rapidly evolving research field attracting increasing attention in the medical imaging community. A wide variety of open-source tools for developing and implementing machine learning are available. 1, 20 March 2018 | Radiology, Vol. 293, No. 4, American Journal of Roentgenology, Vol. 20, No. 30, No. Two different classes of data with “Gaussian-like” distributions are shown in different markers and ellipses. By taking the maximal value of the convolution, the pooling layer is rewarding the convolution function that best extracts the important features of an image. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). 106, Journal of Craniofacial Surgery, Vol. 4, npj 2D Materials and Applications, Vol. The following is one broadly accepted definition of machine learning: If a machine learning algorithm is applied to a set of data (in our example, tumor images)and to some knowledge about these data (in our example, benign or malignant tumors), then the algorithm system can learn from the training data and apply what it has learned to make a prediction (in our example, whether a different image is depicting benign or malignant tumor tissue) (Fig 1). 2, The Korean Journal of Helicobacter and Upper Gastrointestinal Research, Vol. Two different classes of data…, Illustration of margin learned by SVM. 173, Radiology of Infectious Diseases, Vol. 60, No. Machine learning will be a critical component of advanced software systems for radiology and is likely to have wider and wider application in the near future. Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. 52, No. However, this method can be used to acquire useful estimates of performance, even when this assumption is violated (48). 38, No. Then change directory (“cd”) to the MachineLearningForMedicalImages directory and follow the instructions in the Readme.md file. The similarity function, which determines how close one example object is to another, can be the Euclidean distance between the values of the input vector versus the values of the vector for the other examples. Because the algorithm system iterates to improve performance with the validation set, it may learn unique features of the training set. 18, No. 8, Journal of the American College of Radiology, Vol. 1, Journal of Korean Neurosurgical Society, Vol. 4, 27 March 2020 | Radiology: Imaging Cancer, Vol. Understanding the properties of machine learning tools is critical to ensuring that they are applied in the safest and most effective manner. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. 145, PROTEOMICS – Clinical Applications, Vol. The dominant language in machine learning is Python. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. Artificial Intelligence for Radiology. The Institute of Medicine at the National Academies of Science, Engineering and Medicine reports that “ diagnostic errors contribute to approximately 10 percent of patient deaths,” and also account for 6 … 20, No. 5, © 2021 Radiological Society of North America, From $600 M to $6 billion, artificial intelligence systems poised for dramatic market expansion in healthcare. 11, Journal of Shoulder and Elbow Surgery, Vol. From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905. Example of Machine Learning with Use of Cross Validation.—Having provided the preceding background information, we now describe a concrete though simple example of machine learning. 47, No. 1103, Journal of the American College of Radiology, Vol. Machine-learning algorithms are responsible for the vast majority of the artificial intelligence advancements and applications you hear about. Epub 2018 Feb 4. 2020 Dec 1;11:587161. doi: 10.3389/fphys.2020.587161. Some of the common techniques—specifically, those involving neural networks, k-nearest neighbors, support vector machines, decision trees, the naive Bayes algorithm, and deep learning—are described in the following sections. 2019 Oct 16;1(1):20190037. doi: 10.1259/bjro.20190037. However, it is critical that the normalization of the values in the feature vectors be performed correctly. In this case, the input values (×1, ×2, ×3) are multiplied by a weight (w) and passed to the next layer of nodes. Dropout regularization is typically implemented by having weights (often 50% or more between two layers) set to 0. It should be noted that none of the companies listed in this report claim to offer diagnostic tools, but their software could help radiologists find abnormalities in patient scan images that could lead to a diagnosis when interpreted by a medical professional. The last layer is the output layer. 2, IEEE Transactions on Radiation and Plasma Medical Sciences, Vol. 27, No. 2, No. CheXNet, a deep learning algorithm developed by scientists in Stanford, is one of the methods we can utilise machi 28, No. Pneumonia affects hundreds of millions of people a year around the world and early detection of the disease is one of the most important preventative measures to bring the numbers down. 53, No. Therefore, it is important to clarify how these terms are used. 4, American Journal of Roentgenology, Vol. Indian J Surg Oncol. These machines generally are “well behaved,” meaning that for new examples that are similar, the classifier usually yields reasonable results. The goal in this step is to determine where something starts and stops. In some cases, one can improve accuracy by using an ensemble method whereby more than one decision tree is constructed. Those outputs are compared with the expected values (the training sample labels), and an error is calculated. 21, No. 3, 13 November 2017 | RadioGraphics, Vol. The output nodes are summed and compared with the desired output by the error (loss) function, which then uses the weight optimizer to update the weights in the neural network. When the algorithm is run, one sets the maximal depth (ie, maximal number of decision points) and the maximal breadth that is to be searched and establishes how important it is to have correct results versus more decision points. Machine learning is now being applied in many areas outside of medicine, having a central role in such tasks as speech recognition and translation between languages, autonomous navigation of vehicles, and product recommendations. In this paper, we give a short introduction to machine learning and survey its applications in radiology. 2020 Nov 4;15(11):e0241798. 212, No. Most deep learning tool kits can now leverage graphics processing unit power to accelerate the computations of a deep network. 2, Magnetic Resonance in Medical Sciences, Vol. We have 10 subjects, and 10 regions of interest (ROIs) in normal white matter and 10 ROIs in tumor tissue have been drawn on the CT images obtained in each of these subjects. 1090, 15 August 2018 | Insights into Imaging, Vol. Snow is an independent variable and we show its a priori probabilities in the adjacent table. Once we have our tools configured properly, the job will be easier. 1. Segmentation: The splitting of the image into parts. 1, Current Psychiatry Reports, Vol. 1, Seminars in Roentgenology, Vol. Computer-aided detection and diagnosis performed by using machine learning algorithms can help physicians interpret medical imaging findings and reduce interpretation times (2). 1, Biomedical Physics & Engineering Express, Vol. Machine learning was undoubtedly one of the hottest topics in radiology last year, with a steady stream of academic research papers highlighting how machine learning, particularly deep learning, can outperform traditional algorithms or manual processes in certain use-cases. Artificial Intelligence and Acute Stroke Imaging. The key difference is that this is done without the algorithm system being provided with information regarding what the groups are. 287, No. This has been enabled by tools that leverage the massively parallel computing power of graphics processing units that were created for computer gaming, such as those built by NVidia Corporation (Santa Clara, Calif). It is also possible that parts of the tumor will not enhance. Clipboard, Search History, and several other advanced features are temporarily unavailable. 13, No. Front Physiol. 1, Current Pharmaceutical Biotechnology, Vol. From this perspective, it is important to recognize that accuracy alone is not sufficient and prior probability is an important piece of information that will affect performance measures. 4, International Journal of Dermatology, Vol. Machine learning in radiology aims at training computers to recognize patterns in medical images and to support diagnosis by linking these patterns to clinical parameters such as treatment or outcome. 4, Current Cardiology Reports, Vol. Enhancing tumor will have higher attenuation on the contrast-enhanced images. Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images. 1, Ultrasound in Medicine & Biology, Vol. The number of neighbors, or known objects that are closest to the example object, that “vote” on the classes that the example object may belong to is k. If k is equal to 1, then the unknown object is simply assigned to the class of that single nearest neighbor. 31, No. For training, the machine learning algorithm system uses a set of input images to identify the image properties that, when used, will result in the correct classification of the image—that is, depicting benign or malignant tumor—as compared with the supplied labels for these input images. The Current State of Artificial Intelligence in Medical Imaging and Nuclear Medicine. These algorithms have been used for several challenging tasks, such as pulmonary embolism segmentation with computed tomographic (CT) angiography (3,4), polyp detection with virtual colonoscopy or CT in the setting of colon cancer (5,6), breast cancer detection and diagnosis with mammography (7), brain tumor segmentation with magnetic resonance (MR) imaging (8), and detection of the cognitive state of the brain with functional MR imaging to diagnose neurologic disease (eg, Alzheimer disease) (9–11). HHS 4, Computational Intelligence and Neuroscience, Vol. Copyright © 2012. In the real world, perfect separation is not possible, but the optimal plane that minimizes misclassifications can be found. An important step in training deep networks is regularization, and one popular form of regularization is dropout (56). Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data. This would be an example of 70/30 cross validation. 2012 Apr;16(3):642-61. doi: 10.1016/j.media.2010.03.005. 6, 3 May 2018 | Journal of Digital Imaging, Vol. 12, Journal of King Saud University - Computer and Information Sciences, Japanese Journal of Radiology, Vol. According to the Bayes theorem, one of the oldest machine learning methods (47), the probability of an event is a function of related events. Machine learning and natural language processing algorithms could help track radiologists’ recommendation and reduce the chance of disconnect in communication of follow-up recommendations . Epub 2020 Nov 26. 107, No. USA.gov. One could make some guesses, but adding heights would improve the accuracy: a rather high weight value in conjunction with a low height value is more likely to reflect obesity than is a high weight value in conjunction with a high height value. If the sum is greater than 0, the algorithm system will designate the ROI as tumor; otherwise, the ROI will be designated as normal brain tissue. A review of 3D/2D registration methods for image-guided interventions. 1, 29 November 2019 | RadioGraphics, Vol. One can imagine that if random connection weights are set to 0 and a group of examples is tested, then those weights that are really important will affect performance, but those weights that are not so important and perhaps reflective of a few specific examples will have a much smaller influence on performance. 30, No. Later, the system would be tested by having it try to assign benign and malignant labels to findings on the new images, which would be the test dataset. 127, No. 1, Progress in Biophysics and Molecular Biology, Vol. In the past, machine learning required structured input, and some techniques would not enable successful learning if any single point of data was missing. 10, American Journal of Roentgenology, Vol. 24, No. 3, IEEE Journal of Biomedical and Health Informatics, Transactions on Emerging Telecommunications Technologies, Journal of Biomedical Science, Vol. 213, No. 48, No. Good performance with an “unseen” test set can increase confidence that the algorithm will yield correct answers in the real world. In the beginning, the models were simple and “brittle”—that is, they did not tolerate any deviations from the examples provided during training. 3, No. 4, No. As machine learning research progresses, we expect there to be more applications to radiology. It will then take the remaining 30 examples of each normal brain tissue ROIand each tumor ROI and evaluate the prediction accuracy; in this example case, let us say that it will designate 50 of these 60 ROIs correctly. 13, No. However, the system is then given unlabeled data, and it tries to further improve the classification by better characterizing these data—similar to how it behaves with unsupervised learning. 2, 22 March 2018 | American Journal of Neuroradiology, Vol. This example is two dimensional, but support vector machines can have any dimensionality required. Example shows two classes (●, ○) that cannot be separated by using a linear function (left diagram). 30, No. Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. 10, International Journal of Imaging Systems and Technology, IEEE Journal of Biomedical and Health Informatics, Vol. 4, 17 January 2018 | Journal of Magnetic Resonance Imaging, Vol. 1, 13 November 2017 | Scientific Reports, Vol. To many in the field it appears inevitable that many of the tasks that are currently considered core to the practice of radiology (e.g. For instance, if you wish to create an algorithm to separate cars and trucks and you provide a learning algorithm system with an image of a red car labeled “class A” and an image of a black truck labeled “class B,” then using an image of a red truck to test the learning algorithm system may or may not be successful. Suppose, for instance, that you are given a list of weights with binary classifications of whether each weight indicates or does not indicate obesity. The algorithm system will do this for all 140 examples. These considerations also raise the important issue of pretest probabilities and accuracy: if the prevalence of a positive finding were 1%, then one could simply designate all cases as those of negative findings and achieve 99% accuracy. Two commonly used ensemble methods are bagging and random forest techniques. Although all readers of this article probably have great familiarity with medical images, many may not know what machine learning means and/or how it can be used in medical image analysis and interpretation tasks (12–14). 2, Precision Radiation Oncology, Vol. Right figure shows corresponding graph…, Pulmonary embolism (shown in yellow circle) in the artery of a 52-year old…, Form of the model for predicting fMRI activation for arbitrary noun stimuli. With enough iterations, only the really important connections will be kept. 9, No. In many cases, 99% accuracy would be good, and this algorithm would also have 100% specificity; however, it would have 0% sensitivity. Published by Elsevier B.V. 108, Engineering Applications of Artificial Intelligence, Vol. Billing and Collections Advances in natural language processing (NLP) and machine learning can be used to better interpret and classify reports from image-based procedures such that more accurate claims can be … (Lehmann et al., 2004). 1641, Artificial Intelligence in Gastroenterology, Vol. Soun JE, Chow DS, Nagamine M, Takhtawala RS, Filippi CG, Yu W, Chang PD. 31, No. would be assigned to the ◆ class on the basis of the nearest neighbor (k = 1), but it would be assigned to the × class if k were equal to 3, because two of the three closest neighbors are × class objects. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ImageNet classification with deep convolutional neural networks, Very deep convolutional networks for large-scale image recognition, Deep residual learning for image recognition, Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization, Keras: Deep learning library for Theano and TensorFlow, DeePy: a highly extensible deep learning framework, Boruta: wrapper algorithm for all-relevant feature selection, GMMBoost: likelihood-based boosting for generalized mixed models, Shogun: unified and efficient machine learning, EBLearn: open source C++ machine learning library, Torch: a scientific computing framework for Luajit, Apache Spark: lightning-fast cluster computing, MALLET: machine learning for language toolkit, Deep learning for Java: open-source, distributed, deep learning library for the JVM, Node-SVM: support vector machine (SVM) library for nodejs, Robert van den Hoven van Genderen, Open in Image eCollection 2019. 4, Computers in Biology and Medicine, Vol. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Dropout: a simple way to prevent neural networks from overfitting, ImageNet large scale visual recognition challenge, Gradient-based learning applied to document recognition, Going deeper with convolutions. Wu S, Weinstein SP, Conant EF, Schnall MD, Kontos D. Med Phys. With k-nearest neighbors (41), one classifies an input vector—that is, a collection of features for one unknown example object—by assigning the object to the most similar class or classes (Fig 4). It is considered a branch of artificial intelligence because it enables the extraction of meaningful patterns from examples, which is a component of human intelligence. Machine learning includes a broad class of computer programs that improve with experience. If CNNs realize their promise in the context of radiology, they are anticipated to help radiologists achieve diagnostic excellence and to enhance patient healthcare. 12, European Radiology Experimental, Vol. 31, No. Examples of reinforcement learning algorithm systems include Maja (33) and Teaching-Box (34) systems. You must have the Git software installed on your computer. If the algorithm system optimizes its parameters such that its performance improves—that is, more test cases are diagnosed correctly—then it is considered to be learning that task. 215, No. Connections between different areas of machine learning. 212, No. AI radiology machines may need to become substantially better than human radiologists — not just as good — in order to drive the regulatory and reimbursement changes needed. To access this repository with the sample code and example images, run the following program from a command prompt: git clone git://github.com/slowvak/MachineLearningForMedicalImages.git. 159, 2 November 2017 | Radiology, Vol. 6, No. The difference is that CNNs assume that the inputs have a geometric relationship—like the rows and columns of images. 151, Circulation: Cardiovascular Imaging, Vol. 43, No. 31, No. 59, No. Deep into the Brain: Artificial Intelligence in Stroke Imaging, Invited Commentary on “CT Texture Analysis”, Diagnosis and Detection of Pancreatic Cancer. 2020 Dec;11(4):573-577. doi: 10.1007/s13193-020-01166-8. 2021 Jan;42(1):2-11. doi: 10.3174/ajnr.A6883. On the basis of the latter observation, we will also calculate the variance in attenuation and use this value as the third feature in the vector. 11, The British Journal of Radiology, Vol. We will take 70 of the normal brain tissue ROIs and 70 tumor ROIs and send them to the machine learning algorithm system. Kohli M(1), Prevedello LM(2), Filice RW(3), Geis JR(4). Epub 2017 Jan 26. In general, the training set needs to contain many more examples above the number of coefficients or variables used by the machine learning algorithm. Artificial Intelligence and Radiology: Have Rumors of the Radiologist's Demise Been Greatly Exaggerated? Validation set: The set of examples used during training. RSNA hosted 350 companies in 2019 in its "Machine Learning/Computer-Aided Diagnosis Systems" category. 1, 15 September 2018 | Neuroradiology, Vol. 1, 29 January 2019 | Radiology, Vol. Node: A part of a neural network that involves two or more inputs and an activation function. 5, Expert Systems with Applications, Vol. , ” meaning that for new examples that are similar, the British Journal of Imaging and. With experience Services and applications, Vol weights of the American College of Radiology, Vol Clinic, 200 St! 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