AI in Healthcare: How to Implement Medical Imaging Using Machine Learning?
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About the webinar According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused. The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time, and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care, and interconnected health conditions. Through this webinar, you will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly. What you will learn: - How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging - Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months - Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
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AI in Healthcare: How to Implement Medical Imaging Using Machine Learning?
- 1. AI in Healthcare How to Implement Medical Imaging using Machine Learning?
- 2. Technology leader with 20+ years expertise in Product Development, Business strategy and Artificial Intelligence acceleration. Active contributor in the New York AI community Extensively worked with global organizations in BFSI, Healthcare, Insurance, Manufacturing, Retail and Ecommerce to define and implement AI strategies Nisha Shoukath Co-founder, People10 & Skyl.ai The Speaker
- 3. Extensive experience building future tech products using Machine Learning and Artificial Intelligence. Areas of expertise includes Deep Learning, Data Analysis, full stack development and building world class products in ecommerce, travel and healthcare sector. Shruti Tanwar Lead - Data Science The Speaker
- 4. Bikash Sharma CTO and Co-founder at Skyl.ai CTO & Software Architect with 15 years of experience working at the forefront of cutting-edge technology leading innovative projects Areas of expertise include Architecture design, rapid product development, Deep Learning and Data Analysis The Panelist
- 5. Getting familiar with ‘Zoom’ All dial-in participants will be muted to enable the presenters to speak without interruption Questions can be submitted via Zoom Questions chat window and will be addressed at the end during Q&A The recording will be emailed to you after the webinar Please familiarize yourself with the Zoom ‘Control Panel’ on your screen
- 6. Live Demo of Medical Imaging (COVID-19 detection) with AI and Computer Vision Why AI & Computer Vision is important in Healthcare How to quickly overcome the challenges in building ML models 1 2 3 In the next 45 minutes
- 7. The importance of Artificial Intelligence & Computer Vision in Healthcare 01
- 8. Machine Learning automation platform for unstructured data A quick intro about Skyl.ai Guided Machine Learning Workflow Build & deploy ML models faster on unstructured data Collaborative Data Collection & Labelling Easy-to-use & scalable AI SaaS platform
- 9. POLL #1 At what stage of Machine learning adoption your organization is at? ⊚ Exploring - Curious about it ⊚ Planning - Creating AI/ML strategy ⊚ Experimenting - Building proof of concepts ⊚ Scaling up - Some departments are using it ⊚ In production - Using it in product features ⊚ Transforming - AI/Ml driven business
- 10. Medical Imaging - Challenges now and ahead... 1 Billion Estimate of radiologic examinations/year 50 Ratio of radiologists per million population in UK 60-80% Perceptual error estimate 9% Expected increase in demand for MRI and CT scan Evaluating complex data from MRI machines, CT scanners, and x- rays is difficult and time consuming for humans Large Volume of Data Shortage of experts Complex and error- prone data
- 11. Machine Learning and Computer vision Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Computer vision is the branch of machine learning specializing in how computers “see” photos and videos. Hence, Medical imaging is treated as Computer Vision problems in the AI world Hence, Medical imaging is treated as Computer Vision problem in the AI world
- 12. Handle large volume of Data ● Learn automatically from previous diagnosis and continuously improve ● Streamline and automate the repetitive tasks Faster processing to aid human experts ● Identify features in images quickly and precisely ● Aid human experts to be more efficient and focus on value-add areas Eliminate human errors Machine Learning to the rescue!
- 14. Analysing Brain anomalies As per ACR DSI - Manual segmentation and quantitative susceptibility mapping (QSM) assessments of the motor cortex are necessary, difficult, and time consuming. Machine Learning can flag images that offer risk ratios that the images contain evidence of ALS or PLS, populate reports and streamline the process
- 15. Identifying Fractures & Musculoskeletal injury Fractures contribute to long-term, chronic pain if not treated quickly and correctly AI can help in identifying hard-to-see fractures, dislocations, or soft tissue injuries helping doctors to avoid misdiagnosis
- 16. Detecting cardiovascular abnormalities Measuring various structures of the heart to reveal an individual’s risk for cardiovascular abnormality Computer Vision can help in Cardiac segmentation ( coronary CT angiography, artery diameter segmentations, Ventricle segmentation etc) that helps heart surgeons to be minimally invasive in diagnosis and treatments
- 17. Detecting COVID-19 from Chest X-Ray Radiology images are used to segment COVID- 19 from conditions like pneumonia. X-ray readings may resemble community acquired pneumonia (bacterial) or other respiratory infections. An X-ray will show formations in the lung that are associated with a number of respiratory conditions including pneumonia. AI can detect patterns specific to COVID-19 to provide accurate diagnosis.
- 18. Live Demo of solving Medical Imaging problem with Machine Learning & Computer Vision 02
- 19. 8 stages of Machine Learning workflow
- 20. We would like to credit Joseph Paul Cohen for collecting this data and making it available as open source. Github Repo https://github.com/ieee8023/covid-chestxray-dataset By: Joseph Paul Cohen Github: https://github.com/ieee8023 Website: http://josephpcohen.com Sources: https://radiopaedia.org/ (license CC BY-NC-SA) https://www.sirm.org/category/senza-categoria/covid-19/ https://www.eurorad.org/ (license CC BY-NC-SA) Credits
- 21. Live Demo of Covid-19 from chest X-rays using AI and Computer Vision
- 22. POLL #2 Some challenges that you are facing while implementing AI & Machine Learning ⊚ Not started yet, so no challenges ⊚ Data collection ⊚ Data Labeling ⊚ Large volumes of data ⊚ Identifying the right data set to train ⊚ Data Security ⊚ Lack of knowledge of ML tools ⊚ Lack of end to end platform ⊚ Lack of expertise ⊚ Choosing the right algorithms
- 23. Advantages of a unified platform Speed, Visibility, Quality, Collaboration, Flexibility 03
- 24. Data Collection - Flexible options (CSV bulk upload, APIs, Mobile capture, Form based…)
- 25. DataSecurity- on premise solutions (encrypted data sources, access controlled flow..)
- 26. Data Labeling - Simple 4 steps process (collaboration jobs, guided workflow…)
- 27. Data Labeling - Real-time early visibility (class balance, missing data…)
- 28. Data Labeling - Early Visibility (data frequency, data intuition, outliers, trends, labeling accuracy…)
- 29. Data Labeling with Effective Collaboration (Job allocation, trend, statistics, interactive messaging…) Analyse trends and progress of your data labeling job in real time with statistics and interactive visualizations Manage collaborator progress, activity, interactive messaging
- 30. Data Visualization to build strong data intuition ( visuals for data composition, data adequacy)
- 31. One click training at scale (Easy feature sets, out of the box algorithms, API integration, hyper parameter tuning, auto scaling…) ● Train, Deploy and Version your models by creating feature-sets in no time with our easy feature selection provision. ● Choose from state-of-art neural network algorithms, tune hyperparameters and see logs for your training in real time. ● Integrate our powerful inference API with your application for AI-driven actionable intelligence. ● Auto scaling of model training based on data and hyperparameters.
- 32. Model Monitoring of metrics in real-time (inference count, execution time, accuracy…) ● Monitor your deployed models and analyse inference count, accuracy and execution time. ● See how your models are performing in real-time. No black boxes here.
- 33. ● Monitor your deployed models and analyse inference count, accuracy and execution time. ● See how your models are performing in real-time. No black boxes here. Model Evaluation - Release Confidently (Accuracy, Precision, Recall, F1 Score)
- 34. No upfront cost in Infrastructure set up (no DevOps needed, auto-deploy, SaaS & On-prem models…) No DevOps required 01 Latest tech stack 02 On premise and saas models 03 Scalable On demand 04
- 35. Skyl.ai - as ML automation platform
- 36. Offers for you! ● Personalised ‘demo’ ● 15 days free trial with data credits ● Complimentary consultation on pilot project ● AI Implementation Playbook www.skyl.ai contact@skyl.ai
- 37. Questions? ?
- 38. We hope to hear from you soon Thank you for joining!
Editor's Notes
- Hello everyone and welcome. Thank you for joining, my name’s Ethan. I’ll be hosting today’s webinar on How to Implement Medical Imaging using Machine Learning. First off, I’d like to go ahead and introduce our 3 speakers and panelist for today’s webinar
- First we have Nisha Shoukath - Nisha is a technology entrepreneur with background in investment banking. She has over 20+ years expertise in Product Development, Business strategy and Artificial Intelligence acceleration. She’s co-founded two successful technology startups and has worked with wide variety of global organizations from different industries. She helps enterprises with defining AI strategy, and AI adoption roadmaps. Welcome, Nisha!
- Next we have Shruti Tanwar - Shruti has vast knowledge in the field of data science. She has experience in building SaaS products using Machine Learning and AI. Her expertise includes Deep Learning and Data Analysis, as well as full stack development and building tech products in various different fields such as ecommerce, travel, and healthcare. Welcome, Shruti!
- Finally, we have Bikash Sharma joining today. Bikash is CTO and Software Architect with 15 years of experience in leading innovative software projects and solutions. He’s co-founded Skyl with his expert knowledge in AI and Machine Learning. Welcome, Bikash!
- Before we begin, I’d like to briefly talk about Zoom features available to us. All participants in the webinar will be muted to avoid any interruptions during the session. Any questions you might have can be submitted to the Zoom Questions chat window in the control panel which is located on the bottom of the screen and we’ll make sure to address them towards the end during the Q&A session. Also, the recording of the webinar will be emailed to you afterwards, so don’t worry if you’ve missed any points during the session or wish to view it again So that’s all for the introduction - now, we’ll get started with the webinar and I’ll hand over the session to Nisha
- Exploring - curious about it Planning - Creating AI/ML strategy Experimenting - Building proof of concepts Scaling up - some departments are using it In production - Using it in product features Transforming - AI/Ml driven business
- AI tools can augment the workflow of radiologists and pathologists, acting as clinical decision support and enhancing care delivery. https://emea.gehealthcarepartners.com/images/pdfs/Rapid-Review--Radiology-Workforce-Review-FINAL.pdf
- Not started yet, so no challenges Data collection Data Labeling Data Bias Large volumes of data Identifying the right data set to train Lack of knowledge of ML tools Lack of end to end platform Lack of expertise Choosing the right algorithms Monitoring the model performance
- Use images and expand the points Automating the detection of abnormalities in commonly-ordered imaging tests, such as chest x-rays, could lead to quicker decision-making and fewer diagnostic errors. For example, when a patient enters the emergency department with a complaint such as shortness of breath, “the chest radiograph is often the first imaging study that is available,” ACR DSI says. “It can be used as a quick initial screening tool for cardiomegaly, which in and of itself can be used as a marker for heart disease. A quick visual assessment by a radiologist is sometimes inaccurate.” 3. Pneumonia and pneumothorax are two conditions that require quick reactions from providers. Both may also be prime targets for artificial intelligence algorithms. Pneumonia, either acquired in the community or after a medical procedure, can be life threatening if left untreated. Radiology images are often used to diagnose pneumonia and distinguish the condition from other lung conditions, such as bronchitis. Yet radiologists may not always be available to read images – and even if radiologists are present, they may have difficulty identifying pneumonia if the patient has pre-existing lung conditions, such as malignancies or cystic fibrosis. In addition, “subtle pneumonias, such as those projecting below the dome of the diaphragms on front chest radiographs, can easily be overlooked and lead to unnecessary CT scans, which AI could help reduce,” ACR DSI says. An AI algorithm could assess x-rays and other images for evidence of opacities that indicate pneumonia, then alert providers to the potential diagnoses to allow for speedier treatment. 4. Fractures and musculoskeletal injuries can contribute to long-term, chronic pain if not treated quickly and correctly. Injuries such as hip fractures in elderly patients are also tied to poor overall outcomes due to reductions in mobility and associated hospitalizations. Using artificial intelligence to identify hard-to-see fractures, dislocations, or soft tissue injuries could allow surgeons and specialists to be more confident in their treatment choices. Images obtained by MRI machines, CT scanners, and x-rays, as well as biopsy samples, allow clinicians to see the inner workings of the human body. However, these images often contain large amounts of complex data that can be difficult and time consuming for human providers to evaluate. AI tools can augment the workflow of radiologists and pathologists, acting as clinical decision support and enhancing care delivery.
- Use images and expand the points Automating the detection of abnormalities in commonly-ordered imaging tests, such as chest x-rays, could lead to quicker decision-making and fewer diagnostic errors. For example, when a patient enters the emergency department with a complaint such as shortness of breath, “the chest radiograph is often the first imaging study that is available,” ACR DSI says. “It can be used as a quick initial screening tool for cardiomegaly, which in and of itself can be used as a marker for heart disease. A quick visual assessment by a radiologist is sometimes inaccurate.” 3. Pneumonia and pneumothorax are two conditions that require quick reactions from providers. Both may also be prime targets for artificial intelligence algorithms. Pneumonia, either acquired in the community or after a medical procedure, can be life threatening if left untreated. Radiology images are often used to diagnose pneumonia and distinguish the condition from other lung conditions, such as bronchitis. Yet radiologists may not always be available to read images – and even if radiologists are present, they may have difficulty identifying pneumonia if the patient has pre-existing lung conditions, such as malignancies or cystic fibrosis. In addition, “subtle pneumonias, such as those projecting below the dome of the diaphragms on front chest radiographs, can easily be overlooked and lead to unnecessary CT scans, which AI could help reduce,” ACR DSI says. An AI algorithm could assess x-rays and other images for evidence of opacities that indicate pneumonia, then alert providers to the potential diagnoses to allow for speedier treatment. 4. Fractures and musculoskeletal injuries can contribute to long-term, chronic pain if not treated quickly and correctly. Injuries such as hip fractures in elderly patients are also tied to poor overall outcomes due to reductions in mobility and associated hospitalizations. Using artificial intelligence to identify hard-to-see fractures, dislocations, or soft tissue injuries could allow surgeons and specialists to be more confident in their treatment choices. Images obtained by MRI machines, CT scanners, and x-rays, as well as biopsy samples, allow clinicians to see the inner workings of the human body. However, these images often contain large amounts of complex data that can be difficult and time consuming for human providers to evaluate. AI tools can augment the workflow of radiologists and pathologists, acting as clinical decision support and enhancing care delivery.
- Use images and expand the points Automating the detection of abnormalities in commonly-ordered imaging tests, such as chest x-rays, could lead to quicker decision-making and fewer diagnostic errors. For example, when a patient enters the emergency department with a complaint such as shortness of breath, “the chest radiograph is often the first imaging study that is available,” ACR DSI says. “It can be used as a quick initial screening tool for cardiomegaly, which in and of itself can be used as a marker for heart disease. A quick visual assessment by a radiologist is sometimes inaccurate.” 3. Pneumonia and pneumothorax are two conditions that require quick reactions from providers. Both may also be prime targets for artificial intelligence algorithms. Pneumonia, either acquired in the community or after a medical procedure, can be life threatening if left untreated. Radiology images are often used to diagnose pneumonia and distinguish the condition from other lung conditions, such as bronchitis. Yet radiologists may not always be available to read images – and even if radiologists are present, they may have difficulty identifying pneumonia if the patient has pre-existing lung conditions, such as malignancies or cystic fibrosis. In addition, “subtle pneumonias, such as those projecting below the dome of the diaphragms on front chest radiographs, can easily be overlooked and lead to unnecessary CT scans, which AI could help reduce,” ACR DSI says. An AI algorithm could assess x-rays and other images for evidence of opacities that indicate pneumonia, then alert providers to the potential diagnoses to allow for speedier treatment. 4. Fractures and musculoskeletal injuries can contribute to long-term, chronic pain if not treated quickly and correctly. Injuries such as hip fractures in elderly patients are also tied to poor overall outcomes due to reductions in mobility and associated hospitalizations. Using artificial intelligence to identify hard-to-see fractures, dislocations, or soft tissue injuries could allow surgeons and specialists to be more confident in their treatment choices. Images obtained by MRI machines, CT scanners, and x-rays, as well as biopsy samples, allow clinicians to see the inner workings of the human body. However, these images often contain large amounts of complex data that can be difficult and time consuming for human providers to evaluate. AI tools can augment the workflow of radiologists and pathologists, acting as clinical decision support and enhancing care delivery.
- Use images and expand the points Automating the detection of abnormalities in commonly-ordered imaging tests, such as chest x-rays, could lead to quicker decision-making and fewer diagnostic errors. For example, when a patient enters the emergency department with a complaint such as shortness of breath, “the chest radiograph is often the first imaging study that is available,” ACR DSI says. “It can be used as a quick initial screening tool for cardiomegaly, which in and of itself can be used as a marker for heart disease. A quick visual assessment by a radiologist is sometimes inaccurate.” 3. Pneumonia and pneumothorax are two conditions that require quick reactions from providers. Both may also be prime targets for artificial intelligence algorithms. Pneumonia, either acquired in the community or after a medical procedure, can be life threatening if left untreated. Radiology images are often used to diagnose pneumonia and distinguish the condition from other lung conditions, such as bronchitis. Yet radiologists may not always be available to read images – and even if radiologists are present, they may have difficulty identifying pneumonia if the patient has pre-existing lung conditions, such as malignancies or cystic fibrosis. In addition, “subtle pneumonias, such as those projecting below the dome of the diaphragms on front chest radiographs, can easily be overlooked and lead to unnecessary CT scans, which AI could help reduce,” ACR DSI says. An AI algorithm could assess x-rays and other images for evidence of opacities that indicate pneumonia, then alert providers to the potential diagnoses to allow for speedier treatment. 4. Fractures and musculoskeletal injuries can contribute to long-term, chronic pain if not treated quickly and correctly. Injuries such as hip fractures in elderly patients are also tied to poor overall outcomes due to reductions in mobility and associated hospitalizations. Using artificial intelligence to identify hard-to-see fractures, dislocations, or soft tissue injuries could allow surgeons and specialists to be more confident in their treatment choices. Images obtained by MRI machines, CT scanners, and x-rays, as well as biopsy samples, allow clinicians to see the inner workings of the human body. However, these images often contain large amounts of complex data that can be difficult and time consuming for human providers to evaluate. AI tools can augment the workflow of radiologists and pathologists, acting as clinical decision support and enhancing care delivery.
- How
- 5 minutes intro - 10 industry awareness - 15 min demo - 20 minutes QnA Define problem - Features model - How this model is built using skyl.ai Add slide of Pneumonia detection
- Not started yet, so no challenges Data collection Data Labeling Data Bias Large volumes of data Identifying the right data set to train Lack of knowledge of ML tools Lack of end to end platform Lack of expertise Choosing the right algorithms Monitoring the model performance
- Benefit
- Now, we
- Thank you Nisha and Shruti, for the wonderful presentation and demo. For those of you that are interested in learning more about machine learning or incorporate it to your businesses, Skyl offers a personalized demo as well as a free trial for 15 days. This is a great opportunity to get to know exactly how Skyl can achieve machine learning solutions to your personal challenges and problems that you might have, customized to your needs. You’ll be able to use the platform to see actual numbers on the UI like the workbench shruti’s demoed earlier and see how you can go from collection, labeling all the way to model deployment Skyl also offers a complimentary consultation on a pilot project of your choice and an AI implementation playbook, So if you’re interested in finding out more or have any questions, please visit the skyl.ai website or you can send an email directly to contact@skyl.ai like you see on the screen
- Now we will go ahead and take some time for questions. Once again as a reminder, if you have any questions, you can type your questions in the question box in your control panel - located on the bottom of your Zoom screen and I’ll try to address as many as possible in the available time. Sample questions: For shruti 1. Why is re-training required for ML models? 2. If I build a lot of models, how do I handle model deployment in that case? For Nisha: 1. Apart from images, can we use Skyl for classifying text data or extracting data from documents like pdfs? 2. If I have security concerns about my data, how can skyl help on that?
- All right, so that’s it for today’s webinar, I hope you enjoyed it We have a lot more webinars coming up on different machine learning topics and how they can be implemented into different businesses and industries, So don’t miss out and make sure you sign up for upcoming ones as well Thank you for joining and I hope you have a wonderful day.