New Jersey, United States, – Verified Market Research presents an updated and most recent study on the AI-Based Medical Diagnostic Tools Market for 2021-2028. The report provides answers to the growth scenarios and the impact of the ongoing COVID-19 crisis on the AI-based medical diagnostic tools market. The report includes essential data related to market size, revenue, production and consumption, gross margin, price and factors influencing the market growth. The report places special emphasis on the major driving and restraining factors of the market, along with a comprehensive study of emerging trends and future developments in the market. The report further provides an in-depth study of the micro and macroeconomic aspects that are expected to influence the market demand. The report provides an in-depth study of the market for the forecast period 2021-2028. Comprehensive analysis of market players along with company overview, financial position, and SWOT analysis are included in the report.

The AI-based medical diagnostic tools market was valued at $ 1,035.7 million in 2019 and is expected to reach $ 6,651.2 million by 2027, with a CAGR of 26.04% from 2020 to 2027.

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The market for AI-based medical diagnostic tools industry is extremely competitive, with several major players and small-scale industries. The adoption of advanced technologies and the development of production are expected to play a vital role in the growth of the industry. The report also covers their mergers and acquisitions, collaborations, joint ventures, partnerships, product launches and deals entered into in order to acquire substantial market size and global position.

The report covers an in-depth analysis of the major market players in the market, along with their business overview, expansion plans, and strategies. The major players studied in the report include:

Viz.ai Aidoc, Arterys Inc., Icometrix, IDx Technologies Inc., Imagen Ltd., Others.

Market segmentation of AI-based medical diagnostic tools

AI-Based Medical Diagnostic Tools Market, By Application

• you care
• Oncology
• Radiology
• Cardiovascular
• Pathology
• Others

AI-Based Medical Diagnostic Tools Market, By Diagnostic Tool

• Medical imaging tools
• Automated detection systems
• Others

The report categorized AI-based medical diagnostic tools industry into segments comprising product type and application. Each segment is evaluated based on growth rate and share. Additionally, analysts investigated potential regions that could prove rewarding for manufacturers of AI-based medical diagnostic tools in the years to come. The regional analysis includes reliable predictions on value and volume, thereby helping the market players to acquire in-depth insight into the overall AI-based medical diagnostic tools industry.

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Scope of the AI-Based Medical Diagnostic Tools Market Report

Geographic segment covered in the report:

• North America (United States and Canada)
• Europe (UK, Germany, France and rest of Europe)
• Asia-Pacific (China, Japan, India and the rest of the Asia-Pacific region)
• Latin America (Brazil, Mexico and the rest of Latin America)
• Middle East and Africa (GCC and rest of Middle East and Africa)

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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

This article was republished from the following materials. Note: The material may have been modified for its length and content. For more information, please contact the cited source.

The porcine reproductive and respiratory syndrome virus (PRRSV) continues to challenge the US pork industry. The virus has become more difficult to control despite aggressive biosecurity methods, multiple commercial vaccines, live virus inoculation methods and the implementation of vaccination protocols, said Phillip Gauger, DVM, PhD, in a recent research article.¹

Fortunately, there are several molecular diagnostic tests available to detect, sequence, and characterize PRRSV, said Gauger, a diagnostic pathologist in the Iowa State University Veterinary Diagnostic Laboratory and head of the molecular diagnostics section.

Challenges with SDRPV

“A particular challenge facing the industry has evolved through the use of modified live PRRSV vaccines (MLV-PRRSV) and the inability of diagnostic tests to distinguish between detection of wild-type PRRSV (WT -PRRSV) and / or vaccine type. strains of the virus, ”Gauger said.

“Vaccine-like MLV-PRRSV can replicate in vaccinated animals for long periods of time; is capable of transmission to unvaccinated pigs; can evolve genetically thanks to point mutations in the genome; and can undergo recombination with WT-PRRSV.

These factors can complicate the overall epidemiology or genetic ecology of PRRS, and several tests or sequencing tests may be required to distinguish between the presence of MLV-PRRSV or WT-PRRSV when mixed infections are suspected, a Gauger said.

New test options available

New molecular test methods have been developed to not only detect PRRSV in a sample, but also characterize the presence of different strains of PRRSV in a production system. These methods attempt to distinguish between detection of an MLV-PRRSV, WT-PRRSV or multiple strains of the virus, Gauger explained.

PRRS CLAMP refers to a molecular diagnostic sequencing tool that can help distinguish the presence of wild-type and vaccine strains of PRRSV.

“CLAMP is a term – not an acronym – to suggest what the test will do while it is running,” Gauger said. “Thus, it blocks the homologous sequence of an MLV-PRRSV and prevents the amplification and sequencing of this gene. CLAMP is the bridged nucleic acid designed to bind tightly to the homologous target, when present, and ultimately enables preferential sequencing of a WT-PRRSV.

There are many molecular diagnostic tests currently available to detect PRRS in pig diagnostic samples, Gauger said, but each has certain advantages and limitations (see box).

Promising future

Next-generation sequencing offers a range of processes that can provide the entire genome sequence of a specific virus, Gauger explained. It can also detect the presence of new viruses or allow in-depth sequencing of the microbiome.

“For routine diagnostic purposes, whole genome sequencing of PRRSV may be an appropriate option to answer a diagnostic question or monitor the epidemiology of a PRRSV circulating in a production system,” he said. declared.

“While no molecular diagnostic test is perfect, these tests and combinations of different tests improve the likelihood of detecting and characterizing one or more strains of PRRSV present in a group of pigs,” Gauger added.

“They also help reduce the risks associated with moving pigs unknowingly infected with WT-PRRSV, especially when MLV-PRRSV is present in the same population. “

Existing diagnostic tools

Real-time polymerase chain reaction (PCR) assay for PRRSV screening: These tests are very sensitive and can detect low concentrations of PRRSV and all strains. They are highly specific and multiplexed for PRRSV type 1 or type 2 species, but are not designed for further characterization of the virus nucleic acid and are not able to differentiate between different genetic strains of the virus, a Gauger said. If a PCR test for PRRSV is positive, additional testing may be necessary.

Sequencing of the PRRSV ORF5 gene: This test is a molecular diagnostic test based on the amplification of the ORF5 gene followed by the selective determination of the nucleotide sequence within the gene. “Diagnostic reports with the ORF5 sequence data will also include a comparison of nucleotide identity with the MLV-PRRSV vaccine strains,” Gauger said.

Real-time PCR by reverse transcription of the PRRSV vaccine type: “This assay can detect the presence of MLV-PRRSV and is suggested for use in PRRSV monitoring protocols in combination with other diagnostic assays (PRRSV-screen PCR and / or sequencing),” Gauger said. “Or, it can be used when the history, clinical impressions and use of the vaccine are well known to justify the use of this test,” Gauger said.

PRRSV CLAMP Sequencing Tests: This test is used when MLV-PRRSV and WT-PRRSV are suspected in a pig population. The CLAMP method uses a bridged nucleic acid as described above to prevent amplification and sequencing of MLV-PRRSV, allowing preferential sequencing and detection of WT-PRRSV, if present. It can detect lower concentration WT-PRRSV sequences in vaccinated pigs and helps reduce the risk of transmitting WT-PRRSV that could go undetected without the most advanced molecular tools currently available, Gauger said.

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The latest recently published study, The forecast period of the global AI-based medical diagnostic tools market, 2021-2028. According to the study, the global AI-based medical diagnostic tools market was valued at XX USD and is projected to reach XX USD by 2027, exceeding only XX USD and an average of XX% per year during the period. forecast. This research report provides a comprehensive perspective of the market with detailed insights into drivers, restraints, opportunities, trends, and challenges, key factors that may affect targeted market results over the years.

The market reports provide a concise overview of the segments and sub-segments including product types, applications, players, and regions which provide key aspects of the market. The report focuses on the performance impact of the COVID-19 pandemic and provides the current market and takes an in-depth look at the market conditions. The report was able to conduct an in-depth study of market dynamics, changing consumer behavior, and flow of global supply chains affected by the market. This important information on reporting objectives provides clients with solid guidance in obtaining insight into their trading decisions from their investment market in order to assess factors that may affect current and future market conditions.

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Competition analysis

This report provides a comprehensive view of the competitive environment for the AI-based medical diagnostic tools market and includes a detailed description of the performance of the major global players made in the market. It provides the latest updated list of several business strategies including mergers, acquisitions, partnerships, product launches, manufacturing unit expansions and collaborations adopted by these major global players. The report provides a clear picture of large companies’ R&D investments and adoption of innovative technologies to broaden their consumer base and extend their existing competitiveness. Furthermore, the report provides detailed information about the position of new entrants or players in the market, the extent of growth, and opportunities.

The research focuses on the current market size of the AI-based medical diagnostic tools market and its file-based growth rates with company outline of key players / manufacturers:

The Major Players Covered By AI-Based Medical Diagnostic Tools Markets:

• Icometrix
• Alivecor
• Viz.ai
• Aidoc
• IDx Technologies Inc.
• Arteries inc.
• Bay Labs
• IA MaxQ
• Ltd.
• Zebra medical vision
• Imagen Ltd
• ScreenPoint Medical

AI-Based Medical Diagnostic Tools Market Segmentation:

The AI-based medical diagnostic tools market is split by type and by application. For the period 2021-2028, the cross-industry growth provides accurate calculations and sales forecast by type and application in terms of volume and value. This analysis can help you grow your business by targeting qualified niche markets.

AI-Based Medical Diagnostic Tools Market Split By Type:

• Medical imaging tools
• Automated detection systems
• Others

AI-Based Medical Diagnostic Tools Market Split By Application:

• Eye care
• Oncology
• Radiology
• Cardiovascular
• Pathology
• Others (Orthopedics
• Rare sickness)

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Scope of the AI-Based Medical Diagnostic Tools Market Report

Regional Market Analysis AI-Based Medical Diagnostic Tools can be represented as follows:

Each regional sector of AI-based medical diagnostic tools is carefully studied to understand its current and future growth scenarios. It helps the players to strengthen their position. Use market research to gain a better perspective and understanding of the market and target audience and ensure you stay ahead of the competition.

Based on geography, the global AI-based medical diagnostic tools market has segmented as follows:

• North America includes the United States, Canada and Mexico
• Europe includes Germany, France, UK, Italy, Spain
• South America includes Colombia, Argentina, Nigeria and Chile
• Asia-Pacific includes Japan, China, Korea, India, Saudi Arabia and Southeast Asia

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The study thoroughly explores the profiles of the major market players and their main financial aspects. This comprehensive business analysis report is useful for all new entrants and new entrants as they design their business strategies. This report covers the production, revenue, market share and growth rate of the AI-based medical diagnostic tools market for each key company, and covers the breakdown data (production, consumption, revenue, and market share). market) by regions, type and applications. Historical breakdown data of AI-based medical diagnostic tools from 2016 to 2020 and forecast to 2021-2029.

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We talk about solutions for logical research, personalized consulting and data severity analysis across a range of industries including energy, technology, manufacturing and construction, chemicals and materials, food. and drinks. Etc. Our research studies help our clients make more data-driven decisions, admit push predictions, grossly capitalize on opportunities, and maximize efficiency by acting as their belt in crime to adopt a mention precise and indispensable without compromise.

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