Download Free Medical ARTIFICIAL INTELLIGENCE AND RADIOLOGY PowerPoint Presentation

ARTIFICIAL INTELLIGENCE AND RADIOLOGY PowerPoint Presentation

DOWNLOAD MEDICAL POWERPOINT PRESENTATION

About This Presentation

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.

Tags : Radiology, Imaging, applicaton, artificial intelligence

Published on : Aug 02, 2021 | Views : 7973 | Downloads : 48

Shareable Url :

Embed Code :

Comment

Transcript

Slide 1-

ARTIFICIAL INTELLIGENCE AND RADIOLOGY Dr. DEVKANT LAKHERA
Slide 2-

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.
Slide 3-

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
Slide 4-

SUBSETS OF AI
Slide 5-

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.
Slide 6-

Feature engineering Attributes shared by independant units on which analysis is to be done
Slide 7-

Set of available inputs and desired outputs. Common inputs in radiology are image data and report text.
Slide 8-

Human versus computer vision. Have to apply machine learning algorithms to distinguish images on the basis of these features
Slide 9-

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.
Slide 10-

Convolutional neural network Convolutions are mathematic transformations (similar to a basic filter in a photograph editing application) that are applied to pixel data.
Slide 11-

ppt slide no 11 content not found
Slide 12-

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
Slide 13-

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,
Slide 14-

Conceptual analogy biologic neurons artificial neurons visual cortex retina An artificial neural network is composed of interconnected artificial neurons
Slide 15-

ppt slide no 15 content not found
Slide 16-

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.
Slide 17-

ppt slide no 17 content not found
Slide 18-

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
Slide 19-

Radiographic images, coupled with data on clinical outcomes - rapid expansion of radiomics as a field of medical research
Slide 20-

ppt slide no 20 content not found
Slide 21-

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.
Slide 22-

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,
Slide 23-

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.
Slide 24-

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.
Slide 25-

ppt slide no 25 content not found
Slide 26-

ppt slide no 26 content not found
Slide 27-

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
Slide 28-

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.
Slide 29-

AI RADIOLOGY STARTUPS Butterfly Network Hand held probe connected to iPhone. Starts 2000 dollars DL system will identify characteristics in image and make diagnosis.
Slide 30-

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.
Slide 31-

Artificial intelligence in radiology: Friend or foe?
Slide 32-

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.
Slide 33-

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.
Slide 34-

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.
Slide 35-

THANK YOU