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Learning About Current
Medical Innovations
Lori Fontaine, R.N., B.S.N, M.P.H.
Vice President, Global Clinical Affairs
Hologic, Inc.
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1. Brief History of Medical Development
2. Medical Innovation Today
3. Evolution of A.I. in Breast Cancer Screening
4. STEM Driving Future Innovation
Topics
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History of Medical Development
400-33 BCE
Hippocrates
“Father of Medicine”
Hippocratic Oath
500 - 1400
Medieval Medicine
Diagnosis via urology, pulse and astrology
Black Plague
1400 - 1700
Renaissance Medicine
Anatomical art
Leonardo da Vinci
Blood Circulation
1700 - 1900
18th and 19th Century
Microscope
1st Vaccine
Anesthetics
1900 - 2000
20th Century
Surgery, transplants
Bioengineering
Penicillin
2000 -
21st Century
Human Genome Project
Stem Cells
Robotics / Bionics
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U.S. Regulatory Progress –Drugs & Devices
• 1820 – 1st compendium of drugs
• 1906 – Food and Drugs Act
• 1938 – Federal Food, Drug, and Cosmetic Act
• 1968 – Radiation Control for Health and Safety Act
• 1976 – Medical Device Amendments
• 1988 – Food and Drug Administration Act
• 1992 – Mammography Quality Standards Act
• 1999 – ClinicalTrials.gov
• 2016 – 21st Century Cures Act
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FDA Drug Approvals 1993 - 2018
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FDA Device Development Timeline
Concept /
Design
Pre-clinical
Engineering
Development
Class 1
Class 2
Class 3
510(k)
Clearance
PMA
ApprovalClinical Trials
Reimbursement
Assignment
~ 1 year ~1-3 years ~3-6 months
~1-3 years ~12-36 months
510(k) exempt
Engineering /
Clinical
PMA
Supplement
~3-12 months
Up
dat
e to
Dev
ice
~6-9 months
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FDA Novel Device Approvals 2009 - 2018
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Medical Innovation TodayTop 10 in 2019
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Fighting the Opioid Crisis - Alternative Therapy for Pain
Pharmacogenic Testing:
• Uses a patient’s genetic makeup to predict an individual’s metabolism of drugs, including some opiate-based drug
• May avoid adverse reactions and ineffective prescriptions
• Can be used to predict who may have little or no pain relief to some opiate-based analgesics
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Advances in Immunotherapy for Cancer Treatment
• Cancer immunotherapy, or biologic therapy, is a technique that uses the body’s own immune system to fight cancer.
• Scientists are creating life-changing new cancer treatments through the concepts of joint therapy (immunotherapy and chemotherapy) and engineered T-cells.
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Increasing the Window for Stroke Intervention
Research:
• Dawn and Defuse III Trials Research:• expanded that the window for mechanical
clot removal from 6 hours to 24 hours
• broaden the eligibility criteria by allowing patients who are ineligible for IV tPA to undergo mechanical thrombectomy within 6 hours
• January 2018 new recommendations from American Heart and American Stroke Associations
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Visor for Prehospital Hemorrhagic Stroke Diagnosis
• Rapid diagnosis is necessary for effective treatment, as uncontrolled bleeding can lead to swelling of, and damage to, the brain.
• Advanced technologies such as the hemorrhage scanning visor can detect bleeding in the brain
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Virtual Reality for Medical Education
• Virtual reality involves computer technology to create simulated and hybrid environments.
• VR programs provide simulation training to enhance traditional medical school education
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3-D Printing of Patient-Specific
Products
• Utilizing 3D printing technology, medical devices can now be matched to the exact specifications of a patient providing:
• Greater acceptance by the body
• Increased comfort
• Improved outcomes
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Innovation in Robotic Surgery
• Robots in the operating room provide surgeons with guidance for extreme precision in surgery.
• Shortened recovery time and limited pain after surgery are just a few of the patient benefits seen with minimally invasive robotized surgery.
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Percutaneous Heart Valve Replacement
• Many cardiac surgeries today are performed via a catheter through the skin, no longer requiring an open surgical approach.
• Aortic, mitral and now tricuspid valves can be replaced in a minimally invasive procedure yielding better post-op results.
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RNA Based Therapies
• RNA-based therapies are the newest innovation in labs nationwide and have shown immense potential.
• Interfering with genetic data at the ribonucleic acid (RNA) level gives scientists the ability to intercept a patient’s genetic abnormality before it is translated into functioning (or nonfunctioning) proteins.
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A.I. in Healthcare
Machine Learning Algorithms:
• Helping physicians make smarter decisions at the point of care –improving accuracy of patient scans and reducing physician burnout
• Highlights problems on images
• Caring for patients will a matter or working smarter, not harder
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Evolution of A.I. in Breast Cancer Screening
“Classic to Deep Learning”
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‘Classic’ AI: Using Computer Aided Detection (CAD)1
• Teach the computer to predict if a mammogram has signs of cancer
• Use similar language that you would use if teaching a student
• Create classifiers, as is done in breast cancer imaging
• Is there a calcification? • Is it part of a cluster? • Is it in a vessel?• Is there a mass? • Is it spiculated? • What are its borders like?• Does the breast have an asymmetry left/right? • Is there a change in the breast from previous
mammograms?
AI, artificial intelligence; CAD, computer-aided detection1. Jalalian A, et al. Foundation and Methodologies in Computer-Aided Diagnosis Systems For Breast Cancer Detection. EXCLI J 2017;16:113–137
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‘Classic’ AI: Using CAD as an example1
• For given image, feed the probabilities of classifiers into the neural network
• Compare the calculated probability of cancer
• Repeat for large set of images
• Adjust the weightings (yellow, green layers) to give optimal accuracy in prediction
AI, artificial intelligence; CAD, computer-aided detection1. Jalalian A, et al. Foundation and Methodologies in Computer-Aided Diagnosis Systems For Breast Cancer Detection. EXCLI J 2017;16:113–137
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Deep learning AI: Using CAD as an example1
• Don’t teach the computer anything about the appearance of cancer
• Don’t directly create classifiers
• As inputs to the neural network, feed the original image as is
• In classic AI, there are a small number of classifiers, maybe 20
• In this approach, there are a large number of inputs, as # of pixels can be millions
• Approach needs many more input and hidden layers
AI, artificial intelligence; CAD, computer-aided detection1. Lee J-G, et al. Deep Learning in Medical Imaging: General Overview. Korean J Radiol 2017;18:570–584
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Deep Learning Lessons
• Computationally very intense
• Needs many, many more images to train properly than traditional CAD• Perhaps millions as compared to thousands
• No radiologist needs to see 100,000 cancers to learn how to diagnose
• Performance can be a lot better, in certain situations, than classic method
• It may be seeing features not well understood by humans
• If you want to change the task, you need to start over from the beginning• With deep learning training, it is like teaching a radiologist starting from when she was a baby
• In curious situations, it can be very unstable
AI, artificial intelligence; CAD, computer-aided detection
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Example of instability in deep learning1
• This is a well-known example
• A neural network was trained to recognize Pandas
• Predictions were confident
1. Goodfellow IJ, et al. Explaining and Harnessing Adversarial Examples. Presented at the International Conference on Learning Representations (ICLR) 2015;arXiv:1412.6572
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Example of instability in deep learning1
• Noise was added to create a new image
• The new image looks basically the same to humans
1. Goodfellow IJ, et al. Explaining and Harnessing Adversarial Examples. Presented at the International Conference on Learning Representations (ICLR) 2015;arXiv:1412.6572
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Example of instability in deep learning1
• Now the algorithm has low confidence the image is a Panda, but much higher that is a Gibbon.
Gibbon
1. Goodfellow IJ, et al. Explaining and Harnessing Adversarial Examples. Presented at the International Conference on Learning Representations (ICLR) 2015;arXiv:1412.6572
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New Mammography System for Better A.I.
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New Mammography System for Better A.I.
6 slices combined to 1 slice
Location driven by AIto receive high importance during combination process
A.I. drives the locations which get higher weights during the process of combining the slices
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Deep machine learning CAD1
• Works on 3D images
• Much higher performance than historical 2D CAD
• Early results show superior performance to many radiologists• Improve workflow by allowing radiologists
to focus on “problematic” cases
• Provide clinical decision support
• One possible application: Reduce the number of needed image interpreters in under-resourced screening programs
CAD, computer-aided detection1. Harvey H, et al. The Role of Deep Learning in Breast Screening. Current Breast Cancer Rep 2019;11:17–22
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STEM Driving Future Innovation
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Career Opportunities – Beyond Medical Degrees
STEM Occupations: Past, Present and Future. U.S. Bureau of Labor Statistics. January 2017
• The national average wage for all STEM occupations was $87,570, nearly double the national average wage for non-STEM occupations ($45,700).
• Employment in STEM occupations grew by 10.5 percent, or 817,260 jobs, between May 2009 and May 2015, compared with 5.2 percent net growth in non-STEM occupations.
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Foundational Framework for STEM Students
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Why Empathy is Important….
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• Recent study showed <50% compliance with yearly mammogram in African American Women
• Income, education, family doctor, age and health insurance were independent predictors for the low utilization rate of mammography
…..Medical innovation alone will not improve patient outcomes!
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Summary - Medical Innovation and STEM
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• Innovation is rapidly changing the way we diagnose and treat disease
• Tremendous opportunity for future career success
• Medical advances can improve lives if people have access to it
• Empathy
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my next boss?
or this one?
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Questions?
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