Using Artificial Intelligence For Cytology Screening
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Using Artificial Intelligence Visual Recognition and Machine Learning can improve speed and accuracy of screening cytology slides
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As an example, PathXL have developed TissueMark for the automated identification and analysis of tumour in lung, colon, breast and prostate cancer digital H&E slides. The conventional pathological estimation of % tumour nuclei in H&E samples shows gross variation between pathologists, undermining the quality of next generation sequencing, molecular testing and patient therapy and potential of false negative diagnoses. TissueMark uses a combination of pattern recognition, glandular analysis and nuclear segmentation to identify premaligant and invasive cancer patterns in H&E stained tissues and use this to assess tumour cell numbers and annotate samples for nucleic acid extraction and molecular profiling. Benchmark data was generated to validate TissueMark technology and showed concordance of automated data with manual counts, accelerating tumour markup and improving sample quality assessment. This represents an example of how automated imaging of tissue samples can be of immense value in quantitative tumour analysis for molecular diagnostics, thereby improving reliability in discovery and diagnostics.
This together with other examples in pathology research and practice will demonstrate that next generation tissue imaging technology in digital pathology could radically change how pathology is practiced.
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Presented in the AMCHAM Healthcare Innovation Seminar, held in Seoul, Korea on July 11, 2012.
Title: Toward Better Health Policy Supporting Innovation in Korean Medical Device Sector
Date: July 11, 2012
Speaker, Sung Yoon Bae, Professor of Healthcare Management, Inje University, Busan, Korea
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This document summarizes a study that used quantitative image analysis of HER2 expression in esophagogastric adenocarcinoma tissue samples to identify prognostic factors. Automated image analysis was performed using the Definiens platform to extract over 50 image features from segmented cells and subcellular compartments. Multivariate regression analysis was able to predict disease-free and overall survival times from these quantitative image features, which were then shown to have prognostic value when analyzed with Kaplan-Meier survival curves. This demonstrated that automated quantitative image analysis can provide statistically significant prognostic factors not accessible to visual analysis alone.
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Finnish biotech year, Tero Piispanen
Finnish biotech year 2016 saw several new startups created and funding rounds completed. Highlights included Faron Pharmaceuticals completing an IPO and funding round, Labmaster raising funds for diagnostic technology, and Aurealis Pharma completing a funding round for chronic inflammation and cancer treatments. The Finnish health technology industry saw growing exports, with the medical equipment sector being the largest. Challenges remain in increasing venture financing amounts in Finland compared to other European countries.
Big Pharma Outlook 2025 - Sample Pages
To purchase the full report follow this link https://goo.gl/kKrtGt
Using in-house sales forecasts, this analysis explores and visualizes market dynamics in the Big Pharma peer set out to 2025. PharmaVitae casts its eye out to 2025 as crystallizing trends in healthcare management will influence Big Pharma to further position itself towards providing value. Big Pharma will add $39bn in revenues out to 2025, generating $464bn in prescription pharmaceuticals at a low single-digit compound annual growth rate of 0.9%. A healthy launch portfolio will bolster growth as Big Pharma reaps the reward of breakthrough products as the result of recent industry innovation. However, the low single-digit CAGR is representative of the overall drag that Big Pharma is facing. Three themes will shape the outlook: evolving business models, market access, and productivity.
Key questions answered
Revenue analysis
Which will be the best performing companies out to 2025?
Which companies will propel revenue growth over the forecast period?
How will Big Pharma perform across the US, 5EU, Japan and RoW regions?
Which companies will have leading market share gains and market share losses?
Therapy area analysis
How are late-stage pipelines positioned and what are the most-coveted launch products?
How many new blockbuster positions will Big Pharma carve out to 2025?
Which therapy areas will experience the largest growth and decline?
Where are companies building leadership positions in specific therapy areas?
What are the detailed competitive dynamics at play in the oncology, metabolic and, infectious diseases markets?
Strategy analysis
Will there be drastic shifts in the proportion of prescription drug revenue that is attributed to R&D?
How is Big Pharma using M&A to propel strategic goals?
What is the current status of biosimilar filings and how will they affect the performance of Big Pharma?
What strategies have been key for the commercial success of products launched in the past few years?
Highlights
Big Pharma prescription drug sales are forecast to grow to $464bn by 2025 at a compound annual growth rate of 0.9%
Number one company in 2025: Pfizer sustains top ranking in prescription drug sales out to 2025
Most valuable product: Humira will continue to be the highest selling product in 2025 with global sales forecast at $11bn
Most lucrative therapy area: Oncology will supplant metabolic in 2023 to become the most valuable therapy for Big Pharma
Most valuable class: PD-1/PD-L1 inhibitors will continue to build momentum out to 2025
Pipeline launch analysis: Big Pharma’s launch portfolio is set to add $134bn in revenues out to 2025
Most valuable company pipeline: Gilead will add pipeline revenues of $19bn out to 2025.
To purchase the full report follow this link https://goo.gl/kKrtGt
NeoGenomics Company Overview 03/16/2015
This company presentation provides an overview of a cancer genetic testing services company. Key points include:
- The company has experienced strong and consistent growth in revenue and test volume over the past 10 years through both organic growth and acquisitions.
- It provides a broad menu of genetic and molecular testing services including next generation sequencing and is developing liquid biopsy tests.
- The company has an experienced management team with experience in large reference laboratories. It is focused on innovation, cost management, and expanding its commercial footprint.
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Roche Digital Pathology Vision
The Roche Digital Pathology vision, value offered and future focus are visually represented in this infographic.
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A look at the in vitro diagnostics market's major trends of 2015. Molecular, Infectious Disease, Veterinary and other categories included.
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The document discusses several emerging healthcare technology markets including healthcare IT integration, healthcare analytics, health information exchange, and telehealth services. It provides an overview of each market including key factors driving and inhibiting growth, market segmentation, prominent players, and market forecasts. The largest market discussed is healthcare analytics, which is valued at $4.43 billion in 2013 and expected to reach $21.35 billion by 2020 growing at a 25.2% CAGR.
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The document is a letter of transmittal from a team of students to their professors submitting a report analyzing the global pharmaceutical companies Merck & Co. and Sanofi. The team's analysis identified three key success factors for companies in the industry and concluded that based on these factors, Sanofi is currently in a better position than Merck & Co. to succeed. The letter requests feedback from the professors and offers to further discuss the report and its analyses and recommendations.
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Peter Hamilton on Next generation Imaging and Computer Vision in Pathology: p...
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Health Policy Supporting Innovation in Korean Medical Device Sector (July 11,...
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https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-zeller
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LEAD Innovation Launch_WHO Innovation Initiative.pptx
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Using Artificial Intelligence For Cytology Screening
- 1. SMART IMAGING TECHNOLOGIES web-pathology.net Smart Cytology Screening Gyn and Non-Gyn Appliations
- 2. Smarter Cytology Screening Robust • System processes cells on entire slide not just a number of selected fields of view • Processing is done automatically on server within 2-5 minutes for 5,000 – 50,000 cells Informative • Top 500 most abnormal cells are presented to reviewer in the Instant Review Grid™ • Single click navigation from field to field • Whole digital slide is always available In US this IVD application is labeled "For Research Use Only. Not for use in diagnostic procedures“ by FDA regulations.
- 3. Smarter Cytology Screening Convenient • Screening can be done remotely via any web browser • System works with any digital slide scanner Versatile • System can process Gyn and Non-Gyn Cytology samples • Works with any Liquid-Based Cytology methods including the economical ClearPrep™ process
- 4. How it works New slides are processed automatically on server. 2-5 minutes per slide depending on cell density HIPPA-compliant notification is routed to a user when analysis is complete and slide is ready for review User quickly reviews abnormal cells with Instant View Grid™ and completes diagnosis in a few clicks Smart Cytology™ screening application processes new slides automatically in the background and sends notification to the user when analysis is complete 1 2 3
- 5. Application Setup Validation and training of Smart IHC™ scoring application is quick and easy. System is trained automatically from slides with known diagnosis • Upload slides with known diagnosis • 10 slides per biomarker is usually sufficient 1 • Annotate patterns with known score on some slides for training • Keep other slides without annotations (for validation) 2 3 • Validate that application scoring is correct for annotated areas • Validate application scoring for non-annotated slides • Keep validation sides, records and reports In US Smart Cytology IVD application is labeled for "For Research Use Only. Not for use in diagnostic procedures“ by FDA regulation.
- 6. Analyzing Cytology Slides with Machine Learning Methods Machine Learning algorithms learn to recognize images and patterns the same way humans do – by example, rather than by human-derived “handcrafted features” such as shape, size, brightness etc. Adding Machine Learning methods to image analysis adds a number of benefits: • No need to formalize complex “handcrafted features”, cytologist can just point to patterns they need to recognize • No dependency on image analysis engineers (almost). • System can be trained on variety of samples to achieve robust recognition • New data samples can easily be added to a model to increase accuracy The Science © 2015 Smart Imaging Technologies. Co. Web-pathology.net sales@simagis.us