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ARTIFICIAL INTELLIGENCE AND RADIOLOGY PowerPoint Presentation

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Description : Check out this medical presentation on Radiology and Imaging, which is titled "ARTIFICIAL INTELLIGENCE AND RADIOLOGY", to know about the applicaton of artificial intelligence in radiology.

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Published on : Aug 02, 2021 | Views : 8038 | Downloads : 48

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ARTIFICIAL INTELLIGENCE AND RADIOLOGY Dr. DEVKANT LAKHERA
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ARTIFICIAL INTELLIGENCE Branch of computer science devoted to creating systems to perform tasks that ordinarily require human intelligence. Creation of intelligent machines that work and react like humans Reasoning, planning, learning, language processing, perception and the ability to move and manipulate objects.
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Medical imaging Medical diagnosis Autonomous vehicles (drones and self-driving cars) Search engines (such as Google search), online assistants (such as Siri) Creating art ,mathematical theorems Image recognition in photographs Predicting flight delays
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SUBSETS OF AI
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MACHINE LEARNING Subfield of artificial intelligence. Expert humans discern and encode features that appear distinctive in the data (Feature engineering) Algorithms are trained to perform tasks by learning patterns from data rather than by explicit programming Machine learning uses algorithms to learn from data and make informed decision on what it has learned.
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Feature engineering Attributes shared by independant units on which analysis is to be done
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Set of available inputs and desired outputs. Common inputs in radiology are image data and report text.
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Human versus computer vision. Have to apply machine learning algorithms to distinguish images on the basis of these features
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DEEP LEARNING Subfield of machine learning. No feature engineering is used. Instead, the algorithm learns on its own the best features to classify the provided data. Structure algorithms in layers to create artificial neural network and can learn and make decision.
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Convolutional neural network Convolutions are mathematic transformations (similar to a basic filter in a photograph editing application) that are applied to pixel data.
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ARTIFICIAL NEURAL NETWORK Artificial neural network is a biologically inspired network of artificial neurons configured to perform specific task. Neural network acquires knowledge through learning. Contains large number of artificial neurons arranged in series Based on a neural network structure loosely inspired by the human brain – Convolutional neural network
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ARTIFICAL NEURAL NETWORK (ANN) ANNs are clusters of interconnected nodes, like brain neurons. Feed those blocks into multiple (deep) processing layers, which act as and then feed data onto further layers with other kinds of filters. filters,
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Conceptual analogy biologic neurons artificial neurons visual cortex retina An artificial neural network is composed of interconnected artificial neurons
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Oncology - assisting clinical decision making related to the diagnosis and risk stratification of different cancers. non-small-cell lung cancer (NSCLC) used radiomics to predict distant metastasis in lung – adenocarcinoma and tumour histological subtypes as well as disease recurrence, somatic mutations, gene-expression profile and overall survival.
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To train a model, we need data. Massive amounts of digital data now available to train algorithms and modern, powerful computational hardware Radiomics: Medical study that aims to extract large amount of quantitative features from medical images using data- characterization algorithms
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Radiographic images, coupled with data on clinical outcomes - rapid expansion of radiomics as a field of medical research
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DEEP LEARNING SYSTEM DEEP MIND GOOGLE Struck a deal with NHS 700 scans of head and neck cancer to be given to system so that areas to be treated and avoided during radiotherapy can be delineated. 1 million eye scans with information to be given to teach it to recognize eye illness.
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Massachusetts General Hospital (MGH) and Brigham and Women’s Hospital (BWH) spent more than $1 billion on data collection infrastructures. Recently, these institutions started a Center for Clinical Data Science to produce new clinical AI applications, trained with these data,
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RECENT USES Efforts to enable radiologists to utilize AI as part of their normal PACS workflow. Triaging studies that need urgent review by radiologists. Facilitating the communication of urgent results.
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Detecting Intracranial hemorrhage Chest Xray diagnosis Classifying liver lesions on MRI scans and explaining the findings, Characterizing pulmonary nodules on CT Helping to avoid unnecessary thyroid nodule biopsies.
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AI RADIOLOGY START UPS Zebra technologies Using million high quality images to develop DL engine to automatically detect various medical findings. Automatic detection of liver, lung CVS and bone diseases. Deployed at more than 50 hospitals globally
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AI RADIOLOGY START UPS AIDENCE Product provides fully automated analysis and reporting of pulmonary nodules. Accuracy levels equal and even surpassing human capabilities. Artery’s FDA clearance for product that provides automated, editable ventricle segmentations based on cardiac MRI images as accurate as performed manually.
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AI RADIOLOGY STARTUPS Butterfly Network Hand held probe connected to iPhone. Starts 2000 dollars DL system will identify characteristics in image and make diagnosis.
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INDIAN START UPS QURE.AI Deep learning based diagnosis of tuberculosis on chest x ray and intracerebral hemorrhage on CT Processed more than 1.5 million x-rays with accuracy level near to humans. Deployed across 4 clinics in mumbai.
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Artificial intelligence in radiology: Friend or foe?
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Artificial intelligence Fear has been AI would begin to chip away at jobs. While that concern isn’t coming true as yet. Radiologists are being urged to accept and incorporate AI into their interpretations.
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Artificial intelligence Primary driver - desire for greater efficacy and efficiency in clinical care. Studies report that, in some cases, an average radiologist must interpret one image every 3–4 seconds in an 8-hour workday to meet workload demands Involves visual perception as well as independent knowledge, errors are inevitable Integrated AI component within the imaging workflow - increase efficiency, reduce errors and achieve objectives providing trained radiologists with pre-screened images and identified features.
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Improves their consistency and quality and potentially lowers operating costs. Scientists have shown that few pixels from other image can drastically alter results. Artificial intelligence won’t necessarily replace radiologists, but it will replace radiologists who don’t use artificial intelligence in future.
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THANK YOU