Computational Modeling & Simulation has the ability to revolutionize the orthopedic device industry by reducing and in some instances eliminating the need for benchtop testing and clinical trials. Dr. Afshari shared his experience in establishing the credibility of computational models for product design and development purposes, and highlighted was that modeling fits with the regulatory and standards framework.
CAE is the use of computer software to simulate performance in order to improve product designs or assist in the resolution of engineering problems for a wide of industries this includes simulation validation and optimization of products processes and manufacturing tools
Our Quality Engineer, Madison Wheeler, discusses the characteristics of an efficient product development process for medical devices and how medical device product development should incorporate Quality, Regulatory, and Business needs in parallel.
Medical Device Development - Concept to Commercialization | Jahnavi Lokre | L...UCICove
About UCI Applied Innovation:
UCI Applied Innovation is a dynamic, innovative central platform for the UCI campus, entrepreneurs, inventors, the business community and investors to collaborate and move UCI research from lab to market.
About the Cove @ UCI:
To accelerate collaboration by better connecting innovation partners in Orange County, UCI Applied Innovation created the Cove, a physical, state-of-the-art hub for entrepreneurs to gather and navigate the resources available both on and off campus. The Cove is headquarters for UCI Applied Innovation, as well as houses several ecosystem partners including incubators, accelerators, angel investors, venture capitalists, mentors and legal experts.
Follow us on social media:
Facebook: @UCICove
Twitter: @UCICove
Instagram: @UCICove
LinkedIn: @UCIAppliedInnovation
For more information:
cove@uci.edu
http://innovation.uci.edu/
Risk management in the development of medical devices. This presentation was for a webinar where we discussed the basics of risk management, a general risk management lifecycle, the requirements of certain relevant standards (ISO 14971, IEC 62304, US FDA Title 21 CFR Part 11), and the practical method called HFMEA. The live demonstration shows you how risks can be managed and compliance achieved using the advanced risk management features of codeBeamer ALM, and also demonstrates the use of our (general) FMEA template.
CAE is the use of computer software to simulate performance in order to improve product designs or assist in the resolution of engineering problems for a wide of industries this includes simulation validation and optimization of products processes and manufacturing tools
Our Quality Engineer, Madison Wheeler, discusses the characteristics of an efficient product development process for medical devices and how medical device product development should incorporate Quality, Regulatory, and Business needs in parallel.
Medical Device Development - Concept to Commercialization | Jahnavi Lokre | L...UCICove
About UCI Applied Innovation:
UCI Applied Innovation is a dynamic, innovative central platform for the UCI campus, entrepreneurs, inventors, the business community and investors to collaborate and move UCI research from lab to market.
About the Cove @ UCI:
To accelerate collaboration by better connecting innovation partners in Orange County, UCI Applied Innovation created the Cove, a physical, state-of-the-art hub for entrepreneurs to gather and navigate the resources available both on and off campus. The Cove is headquarters for UCI Applied Innovation, as well as houses several ecosystem partners including incubators, accelerators, angel investors, venture capitalists, mentors and legal experts.
Follow us on social media:
Facebook: @UCICove
Twitter: @UCICove
Instagram: @UCICove
LinkedIn: @UCIAppliedInnovation
For more information:
cove@uci.edu
http://innovation.uci.edu/
Risk management in the development of medical devices. This presentation was for a webinar where we discussed the basics of risk management, a general risk management lifecycle, the requirements of certain relevant standards (ISO 14971, IEC 62304, US FDA Title 21 CFR Part 11), and the practical method called HFMEA. The live demonstration shows you how risks can be managed and compliance achieved using the advanced risk management features of codeBeamer ALM, and also demonstrates the use of our (general) FMEA template.
Surrogate models for uncertainty quantification and reliability analysisBruno Sudret
Taking into account uncertainties in the design of complex industrial systems and civil infrastructures has received much attention in the last decade. Indeed, although the physical modelling of such systems has made tremendous progress, there are always discrepancies between ideal in silico designed systems and real-world manufactured ones.
Starting from a realistic computational model (a.k.a simulator) which reproduces the behaviour of the considered system and allows the engineer to predict its performance, uncertainty quantification aims at modeling the various sources of uncertainty (including natural variability and lack of knowledge) affecting the parameters of the model, propagate these uncertainties and get relevant statistics on the output quantities of interest (e.g. performance indicators). Due to the high-fidelity and related computational cost of simulators, the use of Monte Carlo methods for uncertainty propagation and reliability analysis is not a viable solution.
In the last decade, surrogate models of various kinds have been developed to bypass this issue. Roughly speaking, a surrogate model is built from a limited number of runs of the simulator at selected values of the input parameters (the so-called experimental design) and some learning algorithm. In this keynote lecture, an overview of the most efficient surrogate modelling techniques will be given: polynomial chaos expansions (including sparse approaches suitable to high-dimensional problems), Kriging (a.k.a Gaussian process modelling) and their combination into PC-Kriging. Low-rank tensor approximations will also be introduced. The advantages of the various approaches for sensitivity analysis and reliability (estimation of small failure probabilities) will be discussed. Numerous examples from structural mechanics, hydrogeology, computational electromagnetism, etc. addressed using the UQLab software will be presented to show the efficiency and versatility of these methods.
More economical automation is not so good at initial stage hence step wise automation need to be done to replace human work with automation, which defines need for low cost automation in india
This is related to describing various types of Ansys stimulations and it's application at industrial level. It will give an overview of benefits of using Ansys software.
Accelerated life testing plans are designed under multiple objective consideration, with the resulting Pareto optimal solutions classified and reduced using neural network and data envelopement analysis, respectively.
This presentation consist of what ISO 14971 is and why is it important to consider this standard while designing a medical device or any device for that matter. It will help u understand what Risk actual is and importance of risk management in medical device industry. It gives you insight about Risk management technique. You will Understand FMEA and how to use it.
Design controls are not an easy subject to address during and after the design of medical devices and manufacturing processes. Design controls should drive the device design process, not be an afterthought. This session focuses on treating design as a separate entity within the quality management system, user needs vs. design inputs, continuation of design controls after the transfer process, design review and more.
Digital twins represent the shape of physical objects in 3D.
A virtual twin experience starts with designing a 3D model that represents the shape, dimensions and properties of a physical product or system. Simulations are run on that virtual model to explore how the product will behave when assembled, operated or subjected to a range of events.
The presentation elaborates through practical examples and software solutions how modeling and simulation can aid in having better products to market faster in the era of digitalization.
If you have further interest contact us to know more and see how we can help with a consulting based approach.
Surrogate models for uncertainty quantification and reliability analysisBruno Sudret
Taking into account uncertainties in the design of complex industrial systems and civil infrastructures has received much attention in the last decade. Indeed, although the physical modelling of such systems has made tremendous progress, there are always discrepancies between ideal in silico designed systems and real-world manufactured ones.
Starting from a realistic computational model (a.k.a simulator) which reproduces the behaviour of the considered system and allows the engineer to predict its performance, uncertainty quantification aims at modeling the various sources of uncertainty (including natural variability and lack of knowledge) affecting the parameters of the model, propagate these uncertainties and get relevant statistics on the output quantities of interest (e.g. performance indicators). Due to the high-fidelity and related computational cost of simulators, the use of Monte Carlo methods for uncertainty propagation and reliability analysis is not a viable solution.
In the last decade, surrogate models of various kinds have been developed to bypass this issue. Roughly speaking, a surrogate model is built from a limited number of runs of the simulator at selected values of the input parameters (the so-called experimental design) and some learning algorithm. In this keynote lecture, an overview of the most efficient surrogate modelling techniques will be given: polynomial chaos expansions (including sparse approaches suitable to high-dimensional problems), Kriging (a.k.a Gaussian process modelling) and their combination into PC-Kriging. Low-rank tensor approximations will also be introduced. The advantages of the various approaches for sensitivity analysis and reliability (estimation of small failure probabilities) will be discussed. Numerous examples from structural mechanics, hydrogeology, computational electromagnetism, etc. addressed using the UQLab software will be presented to show the efficiency and versatility of these methods.
More economical automation is not so good at initial stage hence step wise automation need to be done to replace human work with automation, which defines need for low cost automation in india
This is related to describing various types of Ansys stimulations and it's application at industrial level. It will give an overview of benefits of using Ansys software.
Accelerated life testing plans are designed under multiple objective consideration, with the resulting Pareto optimal solutions classified and reduced using neural network and data envelopement analysis, respectively.
This presentation consist of what ISO 14971 is and why is it important to consider this standard while designing a medical device or any device for that matter. It will help u understand what Risk actual is and importance of risk management in medical device industry. It gives you insight about Risk management technique. You will Understand FMEA and how to use it.
Design controls are not an easy subject to address during and after the design of medical devices and manufacturing processes. Design controls should drive the device design process, not be an afterthought. This session focuses on treating design as a separate entity within the quality management system, user needs vs. design inputs, continuation of design controls after the transfer process, design review and more.
Digital twins represent the shape of physical objects in 3D.
A virtual twin experience starts with designing a 3D model that represents the shape, dimensions and properties of a physical product or system. Simulations are run on that virtual model to explore how the product will behave when assembled, operated or subjected to a range of events.
The presentation elaborates through practical examples and software solutions how modeling and simulation can aid in having better products to market faster in the era of digitalization.
If you have further interest contact us to know more and see how we can help with a consulting based approach.
Leveraging Data to Develop, Execute and Exceed the Expectations of Your Regu...April Bright
Scientific data, homegrown or from published literature, is essential to your regulatory strategy…be it establishing substantial equivalence in FDA 510(k) applications, qualifying a device as a legitimate predecessor in the context of a Technical File for CE Mark, predicting the performance of a device in development, judging a new device in verification and validation testing and, with proper planning, expanding indications for use and identifying new marketable claims of performance (or mitigation of risk and liability). In this session, participants will be exposed to various vital data sources and obtain practical examples for putting them to meaningful use.
Leverage Computational Modeling and Simulation for Device Design - OMTEC 2017April Bright
Computational modeling and simulation (CM&S) has the potential to revolutionize medical devices by accelerating innovation and providing comprehensive evidence of long-term safety. For example, CM&S can provide performance benchmarks, assess design parameter interdependencies, evaluate a variety of use conditions, provide visualization of complex processes and become a core element of device submissions and approvals. This presentation will begin with an overview of the use of CM&S throughout the orthopaedic implant lifecycle, followed by a review of the current regulatory direction regarding the use of CM&S in device submissions. Next, a series of case studies based on a variety of orthopaedic implants will demonstrate the application of CM&S at various phases of the product lifecycle in more detail. The examples will also highlight the effects of modeling assumptions on model credibility and some verification and validation best practices.
This presentation will position CM&S as a credible and common means for device companies and FDA to demonstrate the safety of medical devices, and thereby ensure safety, reduce cost and accelerate the pathway toward “first in the world” access to products in the U.S.
Project Report on Immunoassay Analyzer Manufacturing PlantIMARC Group
The report provides a complete roadmap for setting up an immunoassay analyzer manufacturing plant. It covers a comprehensive market overview to micro-level information such as unit operations involved, raw material requirements, utility requirements, infrastructure requirements, machinery and technology requirements, manpower requirements, packaging requirements, transportation requirements, etc.
More Info:- https://www.imarcgroup.com/immunoassay-analyzer-manufacturing-plant-project-report
Metrology & The Consequences of Bad Measurement DecisionsRick Hogan
Learn the risks of making decisions based on bad measurement data, including case studies like the torque wrench that cost NASA nearly 2 billion dollars.
The Food and Drug Administration (FDA) released Clinical Trial Imaging Endpoint Process Standards guidance for clinical trials industry. Why? To standardize. To automate. To move closer to zero-delay clinical trials. Read AG Mednet's perspective on this FDA guidance.
Quality Function Deployment (QFD) for Design ControlsEMMAIntl
The Food and Drug Administration (FDA) has requirements for medical device manufacturers to establish and maintain a quality system for their medical device(s). The requirement for a quality system does not necessarily introduce new concepts, but applies existing quality concepts to design, development, manufacturing, distribution and use of medical devices. Within the larger quality system requirement, a methodology to control device design and development of medical devices is required. This set of sub requirements is known as Design Controls. In this paper, the history and evolution of quality systems and their application to medical devices will be covered. A Quality tool that could be well applied to the specific area of design controls, Quality Function Deployment (QFD), is a focus of this paper.
Joint San Diego Chapters CLMA AACC / May 16 2010 Mtg Robert Parsonbpstat
With closer scrutiny by public and private payers of laboratory tests and their importance to medicine, evidence for their appropriate use often is limited and their cost effectiveness too often misunderstood.
The presentation will review the expanding use of evaluation processes and methodologies by which laboratory tests are evaluated and reimbursed. Learn the strategies manufacturers, laboratory service providers, and payers employ to collect outcome and cost data to better support the effective use of new laboratory tests which in turn increases appropriate use and reimbursement. What common language, nomenclature and information should be used to present that facilitates an open, straightforward dialogue from the development, review, and delivery of evidence-based findings by manufacturers, clinical laboratories, and healthcare providers to those entities that make coverage and reimbursement decisions.
Managing Reliability Expectations & Warranty Costs in Medical ElectronicsCheryl Tulkoff
What are ‘medical’ electronics?
Is it a realistic category?
Some implanted in the body; some outside
Some portable; some fixed
Some complex; some simple
Some control; some monitor; some medicate
All connected by the perception that one’s life
may be dependent upon this product
Creates a powerful emotional attachment/effect
Assuring reliability becomes critical
Medtech start-ups from inception to exit: what are the key milestones and what are the ACTUAL timelines and costs?
A data-driven approach to figuring out the new reality of medical device venture capital investing.
In this webinar, we will be covering what exactly an SaMDs, or Software as a Medical Device, and go over some examples with Artificial Intelligence. We will also look at Artificial Intelligence and Machine Learning versus the traditional software. Next, we will go into the regulatory framework for these types of software, then explain how EMMA International can help you get your SaMD to market.
Similar to Computational Modeling & Simulation in Orthopedics: Tools to Comply in an Evolving Field (20)
The Future of Digital Health and Wearables in OrthopedicsrablesApril Bright
Orthopedic device companies have responded to payors’ adoption of bundled payments and FDA’s promotion of digital health tools by commercializing products that track patients beyond the O.R. Digital health tools, including wearables, provide device companies with revenue streams that respond to hospitals’ episode of care requirements and patients’ personalized medicine needs, while simultaneously creating a feedback loop for product ideas. Christopher E. Pelt, M.D., a surgeon enrolled in Zimmer Biomet’s mymobility clinical study with the Apple Watch app, offered perspective on the benefits of wearables and shares ways that the technology will impact patients, surgeons and device companies in the future.
The Future of Personalized Implants in Joint Replacement: Additive, Robotics,...April Bright
Orthopedics is primed for mass customization of implants thanks to advancements in additive, AI and robotics. Fully leveraged, the technologies can produce patient-specific implants that achieve clinical benefit, decrease cost and maintain O.R. workflow. Founder and Chief Medical Officer of Monogram Orthopaedics, Douglas Unis, M.D., shares his reimagined vision of personalized joint replacement implants and just-in-time inventory solutions.
Innovation in Orthopedics: Surgeon PerspectivesApril Bright
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Antimicrobial Coatings: The Research and Regulatory PerspectiveApril Bright
Coatings have long been considered an avenue for infection prevention in orthopedic procedures. These coatings, some of which utilize silver, have largely not been commercialized because regulators seek greater evidence of their safety, creating a long, expensive road for device companies. Announcements in the last half of 2018 and early 2019 indicate that companies continue to push to get them on the market and that productive conversations are taking place with regulators. This session began with a history of antimicrobial coatings followed by a look at recent research and technology.
Leverage These Effective Communication Skills to Get Your Message AcrossApril Bright
Your success is highly dependent upon how well you communicate with your colleagues, your customers and your providers. Effective communication helps you reduce conflict and confusion while increasing motivation and productivity. No matter your age or title, communication is a timeless skill to practice and hone. Leveraging decades of training and managerial experience within device companies and his role as a professor, G. Bryan Cornwall provided the practical steps that you must take to become an excellent communicator.
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Long lead times remain one of the most vocalized challenges that orthopedic manufacturers face today. Customers, profits, plans and personnel are all negatively impacted by them. James Kwan has worked on the OEM and the supplier sides of orthopedics, and shared his ideas and successful experiences to help you optimally respond to lead times, reduce them and ultimately create and sustain an agile supply chain.
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Engineers: Practical Application of Project Management PrinciplesApril Bright
Predictability throughout the commercialization chain is critical to allow manufacturers to speed products to market and gain share within the growing orthopedic industry. As an engineer, your technical and regulatory expertise will be overshadowed if you cannot properly plan and execute a project. One skill every engineer must learn and hone is project management. Start with the steps shared in this session.
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As data is captured through electronic health records, registries and unique device identifiers, the generation of evidence based on this data is expected to play a crucial role in informing orthopedic manufacturers’ decisions before and after regulatory approval. While regulators, payors, hospitals and manufacturers support this shift, they acknowledge that gaps remain in its optimal execution. Priority considerations include how to generate evidence to expedite regulatory market decisions, device indication expansion, postmarket studies, postmarket surveillance and reimbursement decisions. The National Evaluation System for health Technology Coordinating Center (NESTcc), an initiative of the Medical Device Innovation Consortium (MDIC), is leading the conversation with various stakeholders, including FDA and orthopedic device companies to support the sustainable generation of Real-World Evidence (RWE) using Real-World Data (RWD).
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Unique Device Identification: Manufacturer, Hospital and Global ImplicationsApril Bright
Unique device identification (UDI) is gaining global adoption. Now is the time for companies to take a step back and ask: Is my UDI framework scaleable? UDI experts answered questions on the U.S. regulation and provided perspective on ways that device manufacturers can implement a working system—including data management—that can scale with product development and UDI compliance needs. Attendees gained an understanding of new global regulations and practical, implementable advice for compliance.
Additive Manufacturing - Mechanical Test Methods - OMTEC 2018April Bright
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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
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
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Title: Sense of Taste
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 structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
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
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
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
Computational Modeling & Simulation in Orthopedics: Tools to Comply in an Evolving Field
1.
2. OMTEC 2019
Chicago, Illinois
June 11-13, 2019
Payman Afshari, PhD
Sr. Principal Engineer
DePuy Synthes Spine
Johnson and Johnson
COMPUTATIONAL MODELING AND SIMULATION ROLE IN
REGULATORY DECISION MAKING AND EVIDENCE GENERATION
3. COMPUTATIONAL MODELING AND SIMULATION
(CM&S), A TOOL TO HELP MAKE BETTER DECISIONS
“A good decision is based on knowledge
and not on numbers”
Plato
427 BCE
“Love is a serious mental disease”
He also said
5. MAJOR ORGANIZATIONS ADVANCING ROLE OF M&S
IN REGULATORY DECISION MAKING
• Has identified an important role for computational modeling in
its strategic priorities since 2011
• Medical Device Innovation Consortium (2012)
• Work collectively to accelerate MedTech innovation from concept to
commercialization by improving the processes for development, regulatory
assessment, and reimbursement review of medical technologies
• Avicenna Alliance (2016)
• A global organization that brings together healthcare stakeholders with the
goal of making in silico medicine standard practice in healthcare
A global organization that brings together healthcare stakeholders
With the goal of making in silico medicine standard practice in healthcareMorrison, Tina, et al, “Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories” Frontiers
in Medicine 2018, 5
6. 5
A 501(c)(3) non-profit public-private partnership aimed to benefit patients by advancing medical device regulatory science,
established in 2012.
Work collectively to accelerate MedTech innovation from concept to commercialization by improving the processes for
development, regulatory assessment, and reimbursement review of medical technologies.
MDIC board includes the director of FDA/CDRH, the director of Coverage and Analysis at CMS, C-level executives representing
patient organizations, non-profits, and industry.
MDIC works on science, not policy or lobbying; MDIC work is complementary to trade associations such as AdvaMed and MDMA.
Accelerate Progress
Achieve Results
WORKING COOPERATIVELY
to re-engineer pre-competitive
technology innovation
REDUCING TIME
and resources needed for new
technology development,
assessment, and review
HELPING PATIENTS
gain access to new medical
technologies sooner
Align Resources
Accelerate Progress
Achieve Results
MDIC HIGHLIGHTS
60 participating
member
organizations
10+ Projects
have been
initiated
Leading resource on issues
important to the Medtech
innovation ecosystem
Congressional testimony
on modernizing clinical
trials
Over $35m funding from
grants and contracts for
Program initiatives.
WWW.MDIC.ORG
What is MDIC?
7. The Avicenna Alliance
Our Mission
Dramatically accelerate medical innovation and its practical
implementation,
To ensure safe, affordable and profitable health care
Through the large scale adoption of in silico modeling
(Computer modeling & simulation, CM&S)
European Parliament, September 4, 2018
US Senate with the FDA, May 17, 2017
A global organization that brings together healthcare stakeholders
With the goal of making in silico medicine standard practice in healthcare through a
collaborative ecosystem of patients, clinicians, academics, industries, policy makers,
regulators & payers
• A market focused partnership of healthcare industries and
researchers set up at the request of the European Commission
• Origins in two EU initiatives:
1. VPH Institute
2. Avicenna project: a “Roadmap for in silico medicine”
8. REGULATORY EVIDENCE GENERATION PARADIGM
Current valid scientific sources of
evidence for Regulatory Decision
Making.
Human Clinical Trials
Animal Testing
Benchtop Testing
Modeling and Simulation
Orthopedics
9. M&S RESULTS AS REGULATORY EVIDENCE
Credibility is the trust, through the collection of
evidence, in the predictive capability of a
computational model for a context of use.
Stakeholders
How can I trust this
model?
Is this device safe?
Did we pick the right WC?
What if the model is
wrong?
Can we use
Simulation?
How do we know if
your model is credible?
Is there a guidance
document we can use?
Lack of a guidance on evaluating the credibility of
computational modeling and simulation motivated FDA
and ASME™, in partnership with the medical device
industry and software providers, to develop a standard.
Device
Original Concept, Jeff Bodner Medtronic
11. • ASME V&V 40
ASME V&V 40 FRAMEWORK
• Provides a framework for
1. Establishing credibility goals for a computational
model for a context of use (COU) based on
model risk
2. Assessing the relevance and adequacy of
completed V&V activities
[1] Reprinted from ASME V&V 40-2018, by permission of The
American Society of Mechanical Engineers. All rights reserved.
12. ASME V&V 40 STANDARD –
MAIN BODY DETAILS THE PROCESS
Guides a team through the risk-informed credibility assessment framework,
to determine HOW MUCH verification and validation (V&V) is necessary to
support using a computational model for a context of use (COU).
13. The question of interest describes the specific question, decision or
concern that is being addressed.
Context of use defines the specific role and scope of the computational
model used to inform that decision.
QUESTION OF INTEREST AND CONTEXT OF USE
14. Model risk is the possibility that the model may
lead to a false/incorrect conclusion about device
performance, resulting in adverse outcomes.
- Model influence is the contribution of the
computational model to the decision relative to
other available evidence.
- Decision consequence is the significance of an
adverse outcome resulting from an incorrect
decision.
RISK ASSESSMENT
15. Model credibility refers to the trust in the
predictive capability of the computational
model for the COU.
Trust can be established through the
collection of V&V evidence and by
demonstrating the applicability of the
V&V activities to support the use of the
CM for the COU.
Goals for each credibility factor are
based on model risk.
CREDIBILITY GOALS
16. • Is the standard practical?
• Has it been implemented in an actual regulatory submission?
DePuy Synthes Spine Success Story in implementing V&V 40
• Background
Clinician needs to scan a patient implanted with a metallic spinal device.
Question of Interest:
Could the patient implanted with the device be harmed by the
RF induced temperature rise during a MRI scan?
Answer:
Check the MRI Safety Label of the device
V&V 40 IN ACTION
17. MR CONDITIONAL LABELING FOR RF HEATING
• Under the scan conditions defined the <device name> is
expected to produce a maximum temperature rise of less
than <specific value>ºC after 15 minutes of continuous
scanning
∆T@15 Minutes
Question of
Interest
18. APPLYING THE V&V FRAMEWORK TO RF HEATING
COMPUTATIONAL MODEL Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
Establishing the framework
Creating the Virtual RF Coil
Assessment Activity
19. FRAME WORK
CONTEXT OF USE
• Computational model (CM) to be used to predict the temperature
increase within a specified confidence interval due to the
presence of a passive metallic spinal implant inside an ASTM
F2182 Phantom scanned in a 1.5T and 3T MR Scanner.
• CM is an ASTM F2182 RF Coil/Phantom Replicator
(A virtual RF Coil)
• CM&S will be the sole source of evidence to inform the MRI
labeling parameters for safe RF exposure (see Risk Profile).
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
20. RISK PROFILE
Model Influence: High
CM&S will be the sole source of evidence to
inform the MRI labeling parameters for safe
RF exposure.
Decision Consequence: Low/Mid
Spinal implants are:
• Anchored in bony tissues of spine
• Encapsulated by scar tissue, proximity to fat,
muscle and other soft tissues
• No major vasculature or neural impingements
• No historical complaints reported
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
21. MODEL AND CALIBRATION
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
Frequency Power
Output = ∆T@15 Minutes
22. UNCERTAINTY QUANTIFICATION (BENCHTOP)
Description Units
Variation
(+/-)
Source
P/S
Shape
Divisor
-1 0 1
Sensitivity
Coef. (ci)
R
2
Standard
Uncert. (ui)
[°C]
Standard
Uncert.
[% Nom.]
Experimental Uncertainty
EM
Gel - Electrical conductivity S/m 0.047 [1] P Rect. 1.73 10.323 10.11 8.7776 -16.440 0.851 -0.446 4.4%
Gel - Electric permitivity (n/a) 11.55 [1] P Rect. 1.73 9.9868 10.11 10.508 0.023 0.915 0.150 1.5%
TAV - Electrical conductivity S/m 5.71E+04 * P Normal 1 10.08 10.11 10.05 -2.63E-07 0.250 -0.015 0.1%
Thermal
Gel - Thermal conductivity W/m·K 0.1 * P Normal 1 11.525 10.11 9.0555 -12.348 0.993 -1.235 12.2%
Gel - Density Kg/m3
100 * P Normal 1 10.41 10.11 9.8468 -0.003 0.999 -0.282 2.8%
TAV - Specific heat capacity J/Kg·K 52.63 * P Normal 1 10.113 10.11 10.106 -6.65E-05 0.993 -0.003 0.0%
TAV - Density Kg/m3
443 * P Normal 1 10.113 10.11 10.106 -7.90E-06 0.993 -0.003 0.0%
TAV - Thermal conductivity W/m·K 0.67 * P Normal 1 10.173 10.11 10.049 -0.093 1.000 -0.062 0.6%
Test setup
Probe sensing location mm 0.5 [2] P Rect. 1.73 10.308 10.11 9.6506 -0.657 0.950 -0.190 1.9%
[Implant] X-axis displacement mm 1 [2] P Normal 1 9.9432 10.11 10.245 0.151 0.996 0.151 1.5%
[Implant] Y-axis displacement mm 1 [2] P Normal 1 10.096 10.11 10.047 -0.024 0.549 -0.024 0.2%
[Implant] Z-axis displacement mm 1 [2] P Normal 1 10.182 10.11 10.072 -0.055 0.969 -0.055 0.5%
[Implant] X-axis rotation ° 1 [2] P Normal 1 10.004 10.11 10.152 0.074 0.941 0.074 0.7%
[Implant] Y-axis rotation ° 1 [2] P Normal 1 9.9185 10.11 10.313 0.197 1.000 0.197 2.0%
[Implant] Z-axis rotation ° 1 [2] P Normal 1 10.072 10.11 10.152 0.040 0.999 0.040 0.4%
[Implant] Tolerance (dia.) mm 0.1 * P Normal 1 9.9185 10.11 10.082 0.818 0.625 0.082 0.8%
[Phantom] X-axis displacement mm 1 [2] P Normal 1 9.9794 10.11 10.051 0.036 0.300 0.036 0.4%
[Phantom] Y-axis displacement mm 1 [2] P Normal 1 10.241 10.11 10.115 -0.063 0.720 -0.063 0.6%
[Phantom] Z-axis displacement mm 1 [2] P Normal 1 10.087 10.11 10.1 0.006 0.318 0.006 0.1%
Temp. probe meas. system °C 0.5 [2] S Rect. 1.73 N/A 10.11 N/A 1.000 N/A 0.289 2.9%
Results
[°C] [%/Nom.] [°C] [%/Nom.]
Comb. Stand. Uncert. (uc) 1.40 14% 1.46 14%
Coverage factor (k) -2.85 -28%
Expanded Uncertainty (U) 2.79 28% 2.86 28%
Calculated (3T)
Proportional Stand.
Simulated (3T)
Proportional
Parameter / Uncertainty Contributor PDF CM&S Results [dT °C] (Coded) Calculation
0.58 U @ 97.5 %ile (p=95%)
[°C]
0.29 uc @ 1 St. Dev
2 U @ 2.5 %ile (p=95%)
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
23. Computational Model Error
• Boundary conditions
• Domain discretization
• Convergence
• User error
• Hardware dependencies, HPC / OS
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
UNCERTAINTY QUANTIFICATION (MODEL)
24. EXPLORE VALIDATION SPACE
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
25. MODEL UNCERTAINTY PROFILE
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
26. CREDIBILITY ASSESSMENT
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
First Attempt did not meet the
credibility requirements
A new method and tighter
convergence was implemented
The model met the credibility
requirements
Received 510(k)
Clearance with no
Deficiencies!
Is the model credible for the Context of Use?
27. ADVANCING M&S ACCEPTABILITY THROUGH COLLABORATION
STANDARDS, GUIDANCE DOCUMENTS AND BEST PRACTICES
Collaboration
in advancing
CM&S in
Orthopedics
ASME V&V 40 Working Groups:
• End to End Example (Tibial Tray)
• Solution Verification (Hip Stem)
• Using Real World Data (Tibial Tray)
• Patient Specific (3D Printed Femoral Cage)
• F2077 IBF Cage
• F2182 RF Heating
• F1717 Static Compression
• F-2996 Standard Practice for Finite
Element Analysis (FEA) of Non-Modular
Metallic Orthopaedic Hip Femoral Stems
• STM WK59162- New Test Method for
Finite Element Analysis (FEA) of Metallic
Orthopaedic Total Knee Tibial Components
In Progress
In Progress
28. Geometry
Material
BC
Software
Hardware
….
CHALLENGES IN IMPLEMENTING VVUQ
Uncertainty Quantification:
• Quantitative
characterization of
predictive capability of
both computational and
real world models.
• Probabilistic in nature; it
could require significant
resources to develop it.
Geometry,
Equipment
Procedures
Patient Data
Imaging
….
Clinical Data
Animal Testing
Benchtop Testing
Computational
Model
30. TAKEAWAYS
• The Role of CM&S as a powerful predictive tool impacting all aspects of
product life cycle is expected to grow.
• CM&S is being recognized by the world’s leading regulatory agencies as the
fourth paradigm of evidence generation.
• FDA is leading and promoting the effort in developing standards and
guidance documents to be used in regulatory submission.
• ASME V&V 40 Standard is a practical document that can be the conduit to
communicate the credibility of the CM&S to all the stakeholders in their
decision making and regulatory submissions.
• The burden of developing UQ can be reduced through collaboration with
all the stakeholders.
31. THANK YOU FOR YOUR ATTENTION
“Knowledge which is acquired under
compulsion obtains no hold on the mind”
