This document discusses artificial intelligence and its applications in radiology. It begins with definitions of artificial intelligence and its subsets of machine learning and deep learning. It then discusses how machine learning and deep learning are being used in medical imaging for tasks like cancer diagnosis and detection of findings in images. The document outlines how large amounts of medical image and patient data are being used to train AI models to perform tasks like segmentation and anomaly detection. It provides examples of startups and projects applying AI to problems in radiology. It concludes by discussing views on AI in radiology, noting that AI can increase radiologist efficiency and consistency if integrated into the workflow, rather than replacing radiologists.
Role of artificial intellegence (a.i) in radiology department nitish virmaniNitish Virmani
Machine Learning and Deep Learning is the key to Artificial Intelligence. Future of Radiology with Artificial Intelligence and advancements of Radiology Equipments
Artificial intelligence, such as neural networks, deep learning and predictive analytics, has the potential to transform radiology, by enhancing the productivity of radiologists and helping them to make better diagnoses. This short report from Signify Research presents 5 reasons why artificial intelligence will increasingly be used in radiology in the coming years and concludes with a list of the barriers that will first need to be overcome before mainstream adoption will occur.
ARTIFICIAL INTELLIGENCE(AI) IN RADIOLOGY.pptxHillaryFrancis
Radiology, the branch of medicine that uses medical imaging techniques to diagnose and treat diseases. Radiology is experiencing a transformative revolution with the integration of Artificial Intelligence (AI).
AI, the ability of computer systems to perform tasks that normally require human intelligence, is revolutionizing the field of radiology by enhancing diagnostic capabilities, optimizing workflow efficiency, and improving patient outcomes.
Radiology plays a vital role in healthcare by utilizing medical imaging techniques to diagnose and monitor diseases. However, with the advent of artificial intelligence (AI), the field of radiology is experiencing a significant revolution. In this presentation, we will delve into the transformative impact of AI in radiology and how it is revolutionizing healthcare as we know it.
PRESENTED BY
HILLARY FRANCIS
DDR SDH
Role of artificial intellegence (a.i) in radiology department nitish virmaniNitish Virmani
Machine Learning and Deep Learning is the key to Artificial Intelligence. Future of Radiology with Artificial Intelligence and advancements of Radiology Equipments
Artificial intelligence, such as neural networks, deep learning and predictive analytics, has the potential to transform radiology, by enhancing the productivity of radiologists and helping them to make better diagnoses. This short report from Signify Research presents 5 reasons why artificial intelligence will increasingly be used in radiology in the coming years and concludes with a list of the barriers that will first need to be overcome before mainstream adoption will occur.
ARTIFICIAL INTELLIGENCE(AI) IN RADIOLOGY.pptxHillaryFrancis
Radiology, the branch of medicine that uses medical imaging techniques to diagnose and treat diseases. Radiology is experiencing a transformative revolution with the integration of Artificial Intelligence (AI).
AI, the ability of computer systems to perform tasks that normally require human intelligence, is revolutionizing the field of radiology by enhancing diagnostic capabilities, optimizing workflow efficiency, and improving patient outcomes.
Radiology plays a vital role in healthcare by utilizing medical imaging techniques to diagnose and monitor diseases. However, with the advent of artificial intelligence (AI), the field of radiology is experiencing a significant revolution. In this presentation, we will delve into the transformative impact of AI in radiology and how it is revolutionizing healthcare as we know it.
PRESENTED BY
HILLARY FRANCIS
DDR SDH
How can we make a Radiologist more efficient?
Increased Imaging for Chronic Diseases and Emergencies raise the demand for radiologists globally & AI could definitely assist them in increasing their efficiency & meet the requirements.
Presentation that gives an overview of the impact of IT on radiology, including the growing role of biomarkers and artificial intelligence and deep learning on the (future) radiology profession. The shift to precision medicine and personalized care are explained, the reasons for a re-definition of radiology are addressed.
This slide best explains the introduction of CT, basis and types of CT image reconstructions with detailed explanation about Interpolation, convolution, Fourier slice theorem, Fourier transformation and brief explanation about the image domain i.e digital image processing.
Starting with the Definition, Coverage of field, Seldinger technique, Instruments used in IR we move forward into the embolization Techniques and applications, IR procedures in hepatobiliary system, Portal hypertension, Varicose veins
and lastly RFA for bone tumors like ostoid osteoma
A detailed description of ct coronary angiography and calcium scoring with various aspects regarding the preparation, procedure, limitations and a short review regarding post CABG imaging.
How can we make a Radiologist more efficient?
Increased Imaging for Chronic Diseases and Emergencies raise the demand for radiologists globally & AI could definitely assist them in increasing their efficiency & meet the requirements.
Presentation that gives an overview of the impact of IT on radiology, including the growing role of biomarkers and artificial intelligence and deep learning on the (future) radiology profession. The shift to precision medicine and personalized care are explained, the reasons for a re-definition of radiology are addressed.
This slide best explains the introduction of CT, basis and types of CT image reconstructions with detailed explanation about Interpolation, convolution, Fourier slice theorem, Fourier transformation and brief explanation about the image domain i.e digital image processing.
Starting with the Definition, Coverage of field, Seldinger technique, Instruments used in IR we move forward into the embolization Techniques and applications, IR procedures in hepatobiliary system, Portal hypertension, Varicose veins
and lastly RFA for bone tumors like ostoid osteoma
A detailed description of ct coronary angiography and calcium scoring with various aspects regarding the preparation, procedure, limitations and a short review regarding post CABG imaging.
In this presentation I share the ideas regarding Radiology and AI relation to each other . Its helps to explore more about radiology . Its key advaantage is that u find a detailed knowledge of Radio Imaging and AI at the same platform . If u want to know about the healthcare and research centers where AI is used you also find the collabration among various AI TECHNOLOGY MANUFACTURERS and RESEARCH CENTERS . If you are doing graduation in AI or RADIOLOGY field , it well define your stream . It enhance the workfield arena for radigraphers and how they will increase their job profiles and clearly differntiate them between Robots and AI because most of our assests thought AI in healthcare leads to robotic work but they are totally wrong in this , they need to understand that AI is just going to decrease the work pressure on them . This will not going to take their jobs and our radio imaging machines are only operated by our medical proffesssionalists i.e. our Radiotechnician . It will reduce the waiting time for patients and it increase the job satisfaction for us. As a Radio Imaging student , I try to clear all the doubts regarding the job misconceptiopns in our mind. We must be prgressive , truthful and honest while our job responsibility. AI will help in faster Image Analysis , it helps in Diagnostic accuracy, it also helps in early detection of disease , as it will pre analysize the data from various modalities and make a 3D view of the image form . It leads to increase the patients trust on our healthcaere providers . It helps in unnecessary excessive radiation to the patients , leads to decrease in the risk of radiation related diseases , like skin erethema , skin cancer and skin infections . Yeah.. you also find some challenges regarding smart radiology but by doing proper cincern in your imaging field you will going to solve all the errors in it . We must try to organize short confrence in which we share our views and plans for future innovations and this presentation is small effort towards it. It will also control the quality of your images . This presentation will help you differntiate between the TELEMEDICINE and PACS . I also share the requireds tools and techniques which are using in radiological field . Some of them are - machine learning, deep learning, natural language processing, computer aided detection, image segmentation, 3D- Imaging analysis, automated repoting , radiomics, generative adversial networks . It will show you how AI effects life of Radiographers and Technicians , by stating : efficiency and productivity, workload management , skill enhancement, career evolution, job satisfaction, and patients care, quality assurance. Here , I also share some case studies where AI ia implemented in Radiology, it includes Cleveland Clinic collabration with Zebra Medical Vision , Stanford Medicine use of ARTERYS and UNIVERSITY OF CALIFORNIA , SAN FRANCISCO [ UCSF] and TEMPU . Whereas some challenges in adopting AI in Radiology.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
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Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
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Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
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.
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
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.
7. • Set of available inputs and desired outputs.
• Common inputs in radiology are image data and report text.
8. • Human versus computer
vision.
Have to apply machine learning
algorithms to distinguish images on
the basis of these features
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.
10. Convolutional neural network
• Convolutions are mathematic transformations (similar to a basic filter in a
photograph editing application) that are applied to pixel data.
11.
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
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 filters,
and then feed data onto further layers with other kinds of filters.
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.
17.
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
19. • Radiographic images, coupled with data on clinical outcomes - rapid
expansion of radiomics as a field of medical research
20.
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.
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,
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.
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.
25.
26.
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
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.
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.
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.
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.
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.
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.
More amount of data is fed to the machine – it will learn and make a more acuurate diagnosis
take various attributes and program it to an algorithm based on featues such as edges, gradients, and textures. // Statistical analysis of the presence of these features in a given image can then be used to classify or interpret the image.//
And make associations and form output statistical probabilities, also known as nodes.
A human expert easily classifies this image as an image of the right kidney. // computer “sees” a matrix of numbers representing pixel brightness. Computer vision typically involves computing the presence of numerical patterns (features) in this matrix, then applying machine learning algorithms to distinguish images on the basis of these features.
Complex algorithms , which use multiple lays of processing
Gabor filters related to texture -
The imaging featues are known as evidence .. Many such evidence s are evaluated in layers which gets more complex.. and are integrated-- outputs a decision signal based on a weighted sum of evidences, and an activation function, which integrates signals from previous neurons. Hundreds of these basic computing units are assembled together to build an artificial neural network computing device. \
Fig. 3 |. Artificial intelligence impact areas within oncology imaging.
This schematic outlines the various tasks within radiology where artificial intelligence (AI) implementation is likely to have a large impact. a | The workflow comprises the following steps: preprocessing of images after acquisition, image-based clinical tasks (which usually involve the quantification of features either using engineered features with traditional machine learning or deep learning), reporting results through the generation of textual radiology reports and, finally, the integration of patient information from multiple data sources. b | AI is expected to impact image-based clinical tasks, including the detection of abnormalities; the characterization of objects in images using segmentation, diagnosis and staging; and the monitoring of objects for diagnosis and assessment of treatment response. TNM, tumour–node–metastasis.
Deep learning is a subset of machine learning that is
Radiomics studies have incorporated deep learning techniques to learn feature representations automatically from example images
Brain Tumor Image Segmentation (BRATS)
Much new radiology conferences have been focusing on artificial intellingenc eas the
Deep learning
In latest conferences have been urged .. Keep utptodated