A free webinar is scheduled this week for service technicians to learn more about diagnostic tools.
The National Institute for Automotive Service Excellence (ASE) welcomes Using diagnostic tools for a successful repair Oct. 21 at 4 p.m. ET.
Bryan Bott and Bryan Lewis of Triad Diagnostic Solutions will provide an overview of diagnostics and DTCs for heavy duty applications. This includes:
- Diagnostic inspection process – analysis of the total health of the vehicle and identification of problems and analysis of potential causes
- Overview of diagnostic trouble codes – different types of DTCs, how to identify with associated repair processes
- Diagnostic Tool Features – use information from the scan tool to diagnose issues including circuit diagrams, live data, component replacement guides, service data, lead times repair
- DTC or symptom troubleshooting and repair steps – why are they important to follow
- Technical information – why it’s important to follow OEM specifications during repair
- Vehicle health check after repair – why is it important
After you register, a confirmation email will be sent to you with information about registering for the webinar. Those not sure if they can attend are encouraged to register as they will receive a follow-up email with details on how to attend a recorded session, if applicable.
For more information or to register, Click here.
Pharmaceutical companies interested in joining the global digital health revolution increasingly face difficult decisions about how to take advantage of innovative technology. Is it better to provide a low-risk digital tool that, for example, improves the patient experience, or to invest in a long-term digital technology, such as the development of a diagnostic tool with the potential to help improve clinical outcomes? While the answer will greatly depend on patient needs and the business model, here are some important considerations for success in the digital diagnostic space.
1. What is at risk?
Software functions1 that meet the definition of a device or software as a medical device (SaMD), can be deployed on mobile platforms, other general purpose computing platforms or in function or control of a hardware device. Digital technologies that are regulated by the United States Food and Drug Administration (FDA) include:2
- Mobile applications that use the built-in functionality of a mobile platform such as light, vibration, camera, or other similar sources to perform the functions of a medical device (for example, the mobile medical applications used by a licensed practitioner to diagnose or treat a disease).
- Software functions that control the operation or function (eg, changing parameters) of an implantable or body worn medical device.
- Software functions used in active patient monitoring to analyze patient-specific medical device data.
The FDA regulates based on risk, so companies often think the best bet is to create a Class I digital tool to avoid going through the much more rigorous Class II authorization and approval processes or class III. However, going the Class I path usually means launching a digital tool with limited functionality to meet a treatment goal or solve serious healthcare needs. It will also generally have limited opportunities for differentiation, as consumers can already choose from hundreds of thousands of simple health-related apps. In comparison, investing in a high-risk digital diagnostic product with proven clinical utility can provide clinicians with a more impactful tool and pharmaceutical companies a path to monetization.
2. What is your use case?
Basically, digital involves the pursuit of one of two main revenue models:
- Generate indirect income for supportive therapy
- To create a stand-alone direct income stream
Many companies lead with the first option, but often inadvertently end up performing a mishmash of the two. This can arise when there is a lack of clarity on the business case or a significant change in accompanying therapy (FDA approval, clinical data, etc.). For example, various pharmaceutical companies have invested in accompanying digital diagnostics, only to find the main asset will fail later. In such cases, many companies let inertia move the digital product forward without reassessing the investment thesis and business model. Establishing a stand-alone revenue stream for a digital product requires a radically different investment thesis, level of risk, and time horizon. In this case, the investment / risk ratio may signal the need for a different strategy, including a partnership to sublicense the product or discontinue the idea.
When developing a digital product, pharmaceutical companies should consider direct and indirect revenue pathways early in the product development process, especially when entering new markets or seeking FDA approval. for accompanying therapies. Although regulatory clearance is more effective than the approval of a traditional medicine, digital products can face significant barriers to post-market adoption, as well as commercial and marketing risks. refund. Moving from an indirect revenue model to a direct revenue model can be difficult and may require a complete reset of product design and functionality, regulatory strategy, and clinical planning. By mapping direct and indirect revenue pathways, companies can be better placed to assess product development tradeoffs and make strategic decisions with a clear end goal.
To fully understand your best option, perform a strategic analysis of each path taking into account your business’ market opportunities, positioning, assets, and risk tolerance. Consider the market need and what type of digital diagnostics will best meet that need, and whether there is a path in which you get a potentially greater ROI, for example by pursuing a reimbursable diagnostic tool. Also, keep in mind that the results of a strategic analysis might reveal that your best option is not to proceed. Time, opportunity cost, investment (clinical and otherwise) and risk – without guaranteed income – can be excessive.
3. Can you make the work of clinicians easier?
Some companies aim to take advantage of artificial intelligence (AI) technologies to automate clinical work. As physicians are typically the end customer, the prospect of automating their work is sensitive and is likely to introduce additional barriers to adoption, barriers to commercialization, and legal considerations. In contrast, it stands to reason that a digital health product that complements clinical decision making, leading to improved utility and outcomes, will gain popularity (and increase the chances of reimbursement). As you develop your digital diagnostic strategy, consider how the product can help improve a physician’s practice and / or improve clinical outcomes.
4. Is your development process FDA ready?
Traditionally, software developers have been accustomed to working with agility through development sprints, with the goal of getting a minimum viable product (MVP) to market as quickly as possible, and knowing that they can make adjustments, fix bugs and implement upgrades in later versions if necessary. While developers typically verify and validate these products along the way, the level of rigor required is usually much less intense than for a high-risk Class II or Class III medical product that has a direct impact on the lives of patients. .
Navigating your way through the FDA’s strict regulatory approval process requires both a thorough understanding of compliance as well as personal patience. This means following regulatory design controls: maintaining detailed documentation throughout the development process, as well as throughout the rigorous testing, evaluation and validation processes throughout a product’s lifecycle to ‘to obsolescence. And, while products can be updated and improved upon after approval, the initial FDA-approved version must be proven to fulfill its clinical purpose and perform at an exceptionally high level.
Also, while all machine learning (ML) model developers should perform some form of model validation, the due diligence threshold is much higher for medical products. If the validation is done incorrectly, the results of the clinical trial will reveal that it does not perform reliably in a real environment and you will return to research and development. In technical terms, this means using best practice validation, such as nested cross-validation, which is appropriate given the size of the sample, the patient, and the characteristics of the disease. It also means throughout the validation process to avoid “target leaks” – that is, the subtle validation errors that will cause a model to appear effective during the development process but ultimately fail when it does. will be deployed in the real world. This is essential for the results of clinical trials to provide the expected evidence.
Prepare for digital health success
It all comes down to matching the digital health tool strategy to your business case and market opportunity. For many companies, it will be a question of creating a complementary and strategic digital diagnosis of a therapeutic asset. But without doing your due diligence to determine your best option, you might miss opportunities to monetize digital health innovation, or at least ensure optionity throughout the product development cycle. To introduce valuable digital health technology, it is essential to consider and understand the needs of your target market and your therapeutic landscape. This includes understanding the physician’s point of view, the incentives and barriers to adoption. It also means considering creating a digital diagnostic that truly adds value for the patients and clinicians who rely on you for treatment options, even if it means a more intense development process and under regulatory approval.
Bill Woywod is associate director of health; Jim Williams is associate director in life sciences; and Jthatob Graham is partner of the Life Sciences practice, all at Guidehouse.
Automotive Diagnostic Tools Market Report Coverage: Key Growth Drivers and Challenges, Regional Segmentation and Outlook, Key Industry Trends and Opportunities, Competition Analysis, COVID-19 Impact Analysis and Forecasted Recovery, Market Sizing and Forecast
The report provides detailed competitive intelligence to inform users of all recent innovations and developments compared to their competitors. This Automotive Diagnostic Tools Market report reveals multiple growth opportunities that users can consider capitalizing on, along with insights into key industry trends in which to invest. This section aims to facilitate the critical decision-making process for users.
This report examines all the key factors influencing the growth of the global Automotive Diagnostic Tools market including demand and supply scenario, price structure, profit margins, production, and chain analysis. value. The regional assessment of the global Automotive Diagnostic Tools market opens up a plethora of untapped opportunities in the regional and country level markets. Detailed Company Profiling allows users to assess analysis of company stocks, emerging product lines, NPD reach in new markets, pricing strategies, innovation opportunities and much more. Moreover.
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Some points from the table of contents:
Global Automotive diagnostic tools Market Outlook by Major Company, Regions, Type, Application and Segment Forecast, 2016-2027
1. Scope of the research
2. Market overview
3. The main driver of the automotive diagnostic tools industry
4. Global and regional automotive diagnostic tools market
5. United States Automotive Diagnostic Tools Production, Demand (2017-2027)
6. Europe Automotive Diagnostic Tools Production, Demand (2017-2027)
7. China Automotive Diagnostic Tools Production, Demand (2017-2027)
8. Japan Automotive Diagnostic Tools Production, Demand (2017-2027)
9. India Automotive Diagnostic Tools Production, Demand (2017-2027)
10. Korea Automotive Diagnostic Tools Production, Demand (2017-2027)
11. Southeast Asia Automotive Diagnostic Tools Production, Demand (2017-2027)
12. Global Automotive Diagnostic Tools Average Price Trend
13. Industrial chain (Impact of COVID-19)
14. Competitive landscape of automotive diagnostic tools
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Lewisburg – The Imaging Services at Evangelical Community Hospital have been granted a three-year accreditation in ultrasound following a comprehensive review by the American College of Radiology (ACR).
Ultrasound imaging, also known as ultrasound, uses high frequency sound waves to produce images of internal parts of the body. At Evangelical, ultrasound imaging is used to help diagnose the causes of disease, injury, or other medical problems in the body and to examine the developing fetus during antenatal visits. Ultrasound is safe, non-invasive and does not require the use of ionizing radiation.
The ACR Gold Seal of Accreditation represents the highest level of image quality and patient safety. It is awarded only to facilities that meet the ACR’s practice parameters and technical standards, following peer review by certified physicians and medical physicists and experts in the field.
Image quality, personnel qualifications, suitability of facility equipment, quality control procedures and quality assurance programs are assessed.
Yi “Edwin” Sun, Ph.D. Electrical and Computer Engineering candidate at the University of Illinois Urbana-Champaign and a member of the Beckman Institute Biophotonic Imaging Lab led by Stephen Boppart, explored how deep learning methods can render optical coherence tomography polarization sensitive, or PS-OCT, more expensive. efficient and better equipped to diagnose cancer in biological tissues.
The article, titled “Synthetic polarization-sensitive Optical Coherence Tomography by Deep Learning”, was published in npj Digital Medicine.
OCT systems are common in the clinic and are used to generate high resolution cross sectional images of areas of the human body. Sun and his team have developed a revolutionary method of software application to the OCT tool to provide polarization-sensitive capabilities, without the cost and complexity that come with hardware-based PS-OCT imaging systems.
“We are trying to replace the hardware associated with the PS-OCT,” Sun said. “Nevertheless, [it] is still in the development and research stage. By adding a deep learning model on top of an OCT system, we suddenly come to PS-OCT capabilities without the traditional hardware added.
OCT is a non-invasive imaging test that uses light waves to determine the properties of a biological sample. However, by allowing the tool to use polarization sensitivity, scientists can detect relevant information that OCT cannot capture on its own. For example: OCT can differentiate tissues in a precise way and when the broader characteristics are clear; PS-OCT can detect abnormalities at a deeper level, differentiating microstructural features such as the orientations of collagen fibers that change in a cancer-infected area compared to a normal area.
“We have proven that applying our method to other systems can generate PS-OCT contrast, and that this model can be used on many OCT systems to help us differentiate between cancerous tissue and other types of tissue. tissues much better than OCT systems alone, ”Sun said. . “It’s a huge improvement, which makes this system better for cancer diagnoses.”
Deep learning, a subset of machine learning, has enabled the Sun team to create software that can be combined with OCT systems to provide polarization sensitivity.
“Deep learning has enabled a more advanced method of detecting subtle features in images, which can be used for more precise segmentation and classification. It also allows the imaging tool to use multiple layers to capture spatial features in an image, ”Sun said.
By applying historical data, deep learning methods aid in accurate diagnoses and even medical predictions. Sun’s team tested their model by predicting what a photo of a lush summer forest in December might look like: barren, gray, maybe a bit of ice and snow in the trees. With this concept in mind, images from OCT systems, coupled with this deep learning approach, can even predict the PS-OCT images that would come from more complex and expensive PS-OCT systems.
“Edwin’s study really highlights the power and potential of AI and deep learning approaches to predict and generate synthetic PS-OCT images from standard OCT images, a type of translation from image to image. With the increasing use of OCT in medical fields, this advancement is likely to have a broad impact and ultimately help improve disease detection and diagnosis, ”said Boppart, Ph.D. of Sun. thesis director who is both a doctor and a professor of engineering at the UIUC.
This research was funded in part by grants from the National Cancer Institute and the National Institutes of Health.
Reference: Sun Y, Wang J, Shi J, Boppart SA. Deep learning polarization sensitive synthetic optical coherence tomography. npj Average number. 2021; 4 (1): 1-7. doi: 10.1038 / s41746-021-00475-8
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