Paige.AI

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Paige is transforming the way pathologists work through a comprehensive digital pathology platform and generative AI applications.

How Paige.AI can help you:

Enhances pathology workflows for efficient and accurate diagnostics.
Provides actionable insights with cutting-edge AI applications.
Supports the full digitization of pathology labs for improved case review and analysis.
Facilitates the detection of molecular biomarkers not typically visible to the human eye.

Why choose Paige.AI: Key features

The first and only company with FDA approval for an AI application in pathology.
Exclusive access to the largest and most diverse dataset in cancer diagnostics.
Comprehensive digital pathology platform with robust AI integration.
Expert support for lab digitization and AI application implementation.

Who should choose Paige.AI:

Pathologists seeking to enhance diagnostic accuracy and efficiency.
Hospitals and labs aiming to integrate digital pathology solutions.
Medical professionals interested in advanced cancer detection and diagnostics.

About Paige.AI

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https://paige.ai/

Release Date

March 2024

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AI tool enhances cancer diagnosis by transforming standard tissue images

By Pooja Toshniwal PahariaReviewed by Danielle Ellis, B.Sc.Sep 12 2024 New AI-driven tool converts common histology images into detailed, multi-layered cancer markers, promising faster, more accurate diagnostics and improved patient outcomes. In a recent study published in Nature Machine Intelligence, researchers developed VirtualMultiplexer, a virtually multiplexed staining tool based on generative artificial intelligence (AI) that converts hematoxylin and eosin (H&E) pictures to immunohistochemistry (IHC) pictures for several antibody markers (one marker each time). Background Tissues are spatially structured ecosystems composed of various cells and non-cellular substances. H&E is an important staining technique used in histopathology processes to examine tissue morphology associated with disease. H&E detects aberrant cell proliferation, lymphovascular invasion, and immune cell infiltration in cancer. Similar to generative AI models that create pictures based on a photo, or a textual description of a motive, in the style of different artists, the VirtualMultiplexer produces virtual colorations for specific cellular markers within a tissue. A standard dye routinely performed in cancer pathology, termed hematoxylin/eosin (H/E) staining, serves as its input. Image Credit: Zhu et al. ICCV, 2017 / M. Rapsomaniki (UNIL-CHUV) Understanding tumor spatial heterogeneity is critical to cancer biology. Current processes rely on time-consuming and tissue-intensive procedures, resulting in misaligned images. Artificially staining tissue images with AI is a promising, cost-effective, and easily accessible alternative. About the study In the present study, researchers created the VirtualMultiplexer tool to provide virtually multiplexed immunohistochemistry images for various antibody markers based on an input H&E-stained image. The antibody markers include androgen receptor (AR), homeobox protein Nkx-3.1 (NKX3.1), cluster of differentiation 44 (CD44), CD146, p53, and erythroblast transformation-specific-related gene (ERG). The team trained VirtualMultiplexer on unpaired original H&E-stained (source) pictures and immunohistochemistry (target) ones. The model divided the images into patches and fed them into generator networks, which conditioned the input and output. The model translated the staining patterns to tissue shapes. The produced IHC patches were put together to form virtual IHC images. VirtualMultiplexer provides an architecture replicating human expert review at single-cell, cell-neighborhood, and full-image levels. It uses a neighborhood loss to ensure that produced IHC patches cannot be distinguished from original ones, in addition to adversarial and multilayered contrastive losses from contrastive unpaired translation (CUT). Global consistency losses ensured content and stylistic consistency between actual and virtual immunohistochemistry images. Local consistency losses captured the original representation and staining patterns. The researchers trained the AI tool using a tissue microarray (TMA) for prostate cancer. The TMA included unpaired pictures stained with H&E and IHC agents for six clinically significant membrane, cytoplasmic, and nuclear markers. They trained a separate one-to-one VirtualMultiplexer model for each IHC marker individually. To ensure staining dependability, they offer a multiscale technique that combines three separate loss functions. The researchers analyzed the created images by applying quantitative fidelity criteria, expert pathology evaluation, and visual Turing assessments before determining their therapeutic significance by estimating clinical outcomes. They compared the VirtualMultiplexer to four cutting-edge unpaired S2S translation algorithms and used the Fréchet inception distance (FID) to evaluate the quality of AI-generated pictures. The researchers encoded genuine H&E, real IHC, or virtual IHC pictures as tissue-graph representations and then used a graph transformer (GT) to transfer the representations to downstream class labels. The researchers used the obtained stainings in the European Multicenter Prostate Cancer Clinical and Translational Research (EMPaCT) dataset to predict patient survival and disease progression. They tested the model's ability to generalize data using the prostate cancer grade assessment (PANDA) and SICAP datasets, which included H&E-stained biopsies and associated Gleason scores. They applied the EMPaCT-pre-trained VirtualMultiplexer to a pancreatic ductal adenocarcinoma cohort (PDAC) TMA and generated virtual IHC images for CD44, CD146, and p53, as well as colorectal and breast cancer H&E-stained whole-slide images (WSIs) from The Cancer Genome Atlas. Results VirtualMultiplexer identified physiologically meaningful staining patterns at different tissue scales without needing sequential tissue slices, picture registration, or lengthy expert annotation. It generates nearly multiplexed imaging datasets with excellent staining quality that are indistinguishable from actual ones in a timely, robust, and exact manner. Researchers effectively translated the model across tissue sizes and patient cohorts, indicating its ability to transfer between tissue types. VirtualMultiplexer created virtual IHC pictures that retained the tissue shape and staining patterns of the original H&E image. The model had the lowest value for FID across all antibody markers (mean, 29), consistently less than CycleGAN, AI-FFPE CUT, and CUT with kernel instance normalization (KIN). In the Turing test, the model had 52% sensitivity and 54% specificity for antibody markers. On average, 71% of virtual pictures had acceptable staining quality, compared to 78% for genuine images. VirtualMultiplexer has restrictions, such as a raised backdrop and more pronounced tiling artifacts near the core boundary. It also does not adequately stain CD146+ vascular structures or glandular NKX3.1+ cells invading peri-glandular stroma. Despite these difficulties, the produced pictures allowed for training early fusion GT models, which improved clinical endpoint prediction in both the out-of-distribution prostate cancer and PDAC tumor, node, and metastasis (TNM) cohorts. Conclusion The study showed that VirtualMultiplexer improves clinical prediction in histopathology processes and cancer biology by generating high-quality, realistic multiplexed IHC pictures. The findings highlight the therapeutic use of AI-assisted multiplexed tumor imaging. VirtualMultiplexer is suitable for data inpainting, sample imputation, and pre-histopathological experimental design. Future studies should evaluate the approach in real-world contexts. Journal reference: Pati, P., Karkampouna, S., Bonollo, F. et al. Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling. Nat Mach Intell 2024, doi: https://doi.org/10.1038/s42256-024-00889-5 https://www.nature.com/articles/s42256-024-00889-5

News-Medical.net

Thu, 12 Sept, 12:03 PM UTC

Digital Pathology Market to Surpass USD 2067.36 Million by 2031, Witnessing 7.8% CAGR Growth | SkyQuest Technology

Westford, USA, Sept. 12, 2024 (GLOBE NEWSWIRE) -- SkyQuest projects that the Global Digital Pathology Market will reach a value of USD 2067.36 Billion by 2031, with a CAGR of 7.8% during the forecast period (2024-2031). Due to the increasing number of cancer cases, there is more focus on improving work efficiency while quick diagnostic tools are becoming a necessity. Additionally, the market is growing due to rising investments in healthcare, which are supported by major players in the industry that concentrate on new launches, growing telepathology adoption, and a growing emphasis on precision medicine and drug discovery. The market for digital pathology solutions is being driven by the increasing adoption of healthcare IT solutions. Get a Free Sample Research Report: https://www.skyquestt.com/sample-request/digital-pathology-market Digital Pathology Market Segmental Analysis Global Digital Pathology Market is segmented based on product, application, and region. Based on product, the market is segmented into Software {by type [integrated and standalone (information management system and image analysis software)], by deployment mode (on premises and cloud based), scanners (brightfield scanners and other scanners), device, and storage systems. By application, the market is segmented into drug discovery & development, academic research, and disease diagnosis. By type, the market is segmented into human pathology and veterinary pathology. By end use it is segmented into pharmaceutical and biotechnology, Hospitals & Reference Laboratories, Academic & Research Institutes. By region, the market is segmented into North America, Europe, Asia Pacific, Middle East and Africa, and Latin America. Digital Pathology Market Overview: Report CoverageDetailsMarket Revenue in 2023USD 1133.61 BillionEstimated Value by 2031USD 2067.36 BillionGrowth RatePoised to grow at a CAGR of 7.8%Forecast Period2024-2031Forecast UnitsValue (USD Billion)Report CoverageRevenue Forecast, Competitive Landscape, Growth Factors, and TrendsSegments CoveredProduct, Application, Type, End User and RegionGeographies CoveredNorth America, Europe, Asia-Pacific, Latin America, and Middle East and AfricaReport HighlightsTelepathology and remote consultationKey Market OpportunitiesAI and Machine learning applicationsKey Market DriversNeed for efficient workflow and collaboration Ask for Customization: https://www.skyquestt.com/speak-with-analyst/digital-pathology-market Prominent Players in Digital Pathology Market Philips Healthcare (Netherlands) Roche Diagnostics (Switzerland) Hamamatsu Photonics (Japan) 3DHISTECH (Hungary) GE Healthcare (US) Apollo Enterprise Imaging (US) Olympus Corporation (Japan) Mikroscan Technologies (US) Inspirata (US) PerkinElmer (US) Huron Digital Pathology (Canada) Sectra AB (Sweden) PathAI (US) Indica Labs (US) F. Hoffmann-La Roche (Switzerland) Nikon Corporation (Japan) Device Product Segment to Hold Significant Growth Due to Advancements in Imaging Systems Endorsed by progressions in AI-accommodating therapeutics instruments, imaging schemes and scanners, gadget offerings run the global digital pathology industry presently. Thus, pathology slides can be analyzed with swifter speeds and higher accuracies; this way enhancing both clinical procedures as well as diagnostic correctness. As a result, these innovations are widely adopted in patient care, education, and research, propelling market expansion. Academic Research to Lead Market Due to Cutting-Edge Studies on AI-Driven Diagnostics Cutting-edge studies on AI-driven diagnostics, machine learning algorithms, and data analytics are driving innovation in the global digital pathology market. These studies are conducted by academic institutions. This industry leads because of its contributions to the creation of new technologies, the advancement of precision medicine, and the creation of partnerships that push the frontiers of digital pathology for use in clinical and research settings. North America to Dominate the Market Due to Rising Adoption of Digital Imaging The global market for digital pathology was led by North America. This can be ascribed to a number of things, such as growing government initiatives to develop technologically complex pathologies, ongoing investments in R&D, the increasing use of digital imaging, and the existence of significant market players in the region. Take Action Now: Secure Your Digital Pathology Market Today - https://www.skyquestt.com/buy-now/digital-pathology-market Digital Pathology Market Insight Drivers: Rising Demand for Accurate and Early DiagnosisAdvancements in Digital Imaging TechnologiesGrowing Adoption of AI and Machine Learning in Pathology Restraints: High Maintenance CostsRegulatory and Standardization ChallengesLimited Integration with Existing Laboratory Systems Key Questions Answered in Global Digital Pathology Market Report By 2031, how much is the global digital pathology market expected to be worth?Which region is now leading the world market for digital pathology, and why?Which major factors are propelling the digital pathology market expansion? This report provides the following insights: Analysis of key drivers (Increasing cases of chronic diseases, high-resolution scanners, Automated analysis improves diagnostic accuracy), restraints (Shortage of pathologists trained to use digital pathology tools, raising issues related to cybersecurity, approval processes being time-consuming), opportunities (Expansion of AI-driven diagnostic tools, global push towards telehealth, Increased focus on oncology and precision medicine), and challenges (Privacy laws like GDPR and HIPAA pose challenges, Limited healthcare infrastructure in low- & middle-income countries) influencing the growth of digital pathology market.Market Penetration: Comprehensive information on the product portfolios offered by the top players in the digital pathology marketProduct Development/Innovation: Detailed insights on the upcoming trends, R&D activities, and product launches in the digital pathology marketMarket Development: Comprehensive information on lucrative emerging regionsMarket Diversification: Exhaustive information about new products, growing geographies, and recent developments in the marketCompetitive Assessment: In-depth assessment of market segments, growth strategies, revenue analysis, and products of the leading market players. Related Reports: Hemp Market is growing at a CAGR of 14.04% during the forecast period (2024-2031). Smart Inhalers Market is growing at a CAGR of 19.8% during the forecast period (2024-2031). Medical Waste Management Market is growing at a CAGR of 6.2% in the forecast period (2024-2031). Herbal Medicine Market is growing at a CAGR of 11.16% in the forecast period (2024-2031). Medical Supplies Market is growing at a CAGR of 3.8% during the forecast period (2024-2031). About Us: SkyQuest is an IP focused Research and Investment Bank and Accelerator of Technology and assets. We provide access to technologies, markets and finance across sectors viz. Life Sciences, CleanTech, AgriTech, NanoTech and Information & Communication Technology. We work closely with innovators, inventors, innovation seekers, entrepreneurs, companies and investors alike in leveraging external sources of R&D. Moreover, we help them in optimizing the economic potential of their intellectual assets. Our experiences with innovation management and commercialization have expanded our reach across North America, Europe, ASEAN and Asia Pacific. Mr. Jagraj Singh SkyQuest Technology 1 Apache Way, Westford, Massachusetts 01886 USA (+1) 351-333-4748 Email: sales@skyquestt.com Visit Our Website: https://www.skyquestt.com/ Market News and Data brought to you by Benzinga APIs

Benzinga

Thu, 12 Sept, 2:02 PM UTC

PathPresenter Raises $7.5 Million to Accelerate Adoption of Digital Pathology Workflows

AVANT BIO, Barco NV and Modi Ventures Back Pathologist-Built Platform MONTVILLE, N.J., September 12, 2024 (Newswire.com) - PathPresenter, the global image sharing platform for pathology, announced today that it has closed a Series A funding round of $7.5 million to accelerate the adoption of the company's enterprise pathology workflow solution. The funding was led by AVANT BIO, with participation from Barco NV and Modi Ventures. The funds will be used to advance the features and interoperability of the vendor-agnostic platform, invest in talent across engineering and commercial functions, and accelerate the achievement of regulatory milestones. Founded by dermatopathologist and digital pathology pioneer Dr. Rajendra Singh, PathPresenter began as a free online platform for pathology education and rapidly became the world's largest and fastest-growing digital pathology community with more than 50,000 users in 172 countries. Responding to the needs of institutions, the company began building a secure and scalable enterprise platform and extended the pathology use cases to include clinical care, remote consultations and research, partnering with visionary third-party vendors to create the most comprehensive and interoperable pathology workflow solution available. Today, PathPresenter's vendor-agnostic enterprise platform empowers more than 50 top-tier hospitals, labs and pharma companies to create seamless and powerful digital pathology workflows that connect their institution and pathologists to the vast expertise of their colleagues around the world. The platform leverages promising new artificial intelligence (AI) algorithms and foundational models to enhance patient outcomes and create new profit centers from digital pathology investments. Industry and Executive Insights About PathPresenter PathPresenter is the global image sharing platform for pathology. We are on a mission to help democratize access to the world's pathology knowledge by connecting pathologists to the vast expertise of their colleagues globally and providing a practical platform to access and use best-in-class AI models. The company was founded by dermatopathologist and digital pathology pioneer Dr. Rajendra Singh. PathPresenter's enterprise platform has been adopted by tier-one medical institutions for clinical care, education and research. Through its free pathology education platform, PathPresenter has built the world's largest digital pathology community with more than 50,000 users in 172 countries. For more information on PathPresenter, visit www.pathpresenter.com.

Newswire.com

Thu, 12 Sept, 4:09 PM UTC

Proscia Unveils Concentriq Embeddings and Developer Toolkit to Accelerate Pathology AI Development

Proscia, a leader in digital and computational pathology, has launched Concentriq Embeddings and a new Developer Toolkit. These innovations aim to streamline the development and deployment of AI applications in pathology, potentially revolutionizing disease diagnosis and treatment.

2 Sources

Thu, 3 Oct, 12:04 AM UTC

AI-Powered Diagnostic Tools in Oncology

AI-powered solutions are excellent at precisely organizing and evaluating big datasets. By spotting patterns and correlations in data that might not be immediately obvious to the human eye, they are intended to increase diagnostic precision and lower the possibility of error. Artificial intelligence (AI) systems used in oncology mostly rely on machine learning and deep learning, letting computers learn from massive datasets and classify or predict things. To create these systems, artificial intelligence (AI) models must be trained on enormous volumes of labeled data, such as patient records or medical images, for them to identify particular characteristics of cancer. a. Image Acquisition Optimization Frequent imaging procedures for cancer patients expose them to radiation, cumulative contrast doses, and sometimes extended tests (if an MRI is performed). Transferring images from one high-dimensional data space to another is an efficient task for deep neural networks. As a result, oncology patients may benefit from several innovative applications, including decreased contrast/radiotracer dosage, quicker MRI acquisition, and CT-dose reduction. b. CT-Dose Reduction A CT image can be transferred from the low-dose or high-noise space to the high-dose or low-noise representation using deep neural networks' ability to map data from one high-dimensional data space to another. To drastically lower radiation exposure, new methods based on deep learning reconstruction (DLR) are presently being developed. In 2019, two DLR solutions were approved by the FDA and made available for clinical use. Reconstruction techniques based on DLR have reduced radiation exposure and/or enhanced image quality while providing a respectably quick reconstruction time. A recent DLR pilot study described volumetric tomographic imaging that was produced using a patient-specific prior and ultrasparse data sampling, or single projection, which, if confirmed, can further minimize the radiation exposure. c. Optimization of MRI Acquisition In oncologic imaging, magnetic resonance imaging, or MRI, is essential. Since whole-body MRI has been available, it can also be used as a staging, therapy response assessment, and surveillance tool in addition to being a problem-solving tool for lesion characterization, local assessment, and tumor staging. Long scan durations can cause motion artifacts, increase costs, and cause pain for patients, making it one of the most difficult problems to deal with. These problems might be resolved by recent advancements in AI. For instance, under-sampling in deep learning-based techniques has been used to speed up MRI scan times. These techniques fall into numerous categories and include image-based reconstruction, k-space-based reconstruction, adversarial networks, and super-resolution.

Analytics Insight

Thu, 19 Sept, 10:02 PM UTC

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