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Digital Pathology: Roadmap to the Future of Medical Diagnosis

  • Report

  • 52 Pages
  • December 2018
  • Region: Global
  • Frost & Sullivan
  • ID: 4733466

Digital Pathology Solutions and Artificial Intelligence Tools are Destined to Transform Efficiency and Workflow of Pathology Services

Digital Pathology encompasses the use of optmized pathology workstations, whole slide imaging, image analysis, image management, laboratory information management system, use of machine learning, data handling and storage. Pathology is one of the most important fields in medicine, as the service provides investigation and validation platform in lieu of a medical condition of a patient. Traditional methods of pathology involves slide preparation, traditional microscopy, result interpretation from microscopy by pathologist onto an information management system followed by physical storage of slides for a prescribed timeline. According to numerous research and surveys conducted across the world, the request for pathology services are increasing by a healthy percentage year-on-year which can be attributed to the increase in cancer prevalence across the globe, ageing population amongst a number of other factors. Factoring in to the above the number of experienced pathologists available at the moment and ones about to retire, the scenario for pathology services may start to look grim. Therefore, an urgent solution is needed to tackle the challenges pertaining increase in diagnosis of patients and unreasonable timelines that pathologists are facing currently with the traditional pathology methods. Proper deployment of Digital Pathology solutions have the demonstrated ability to streamline the workflow of pathology services, and can potentially increase the efficiency of the workflow and decrease the time for diagnosis.

Artificial Intelligence (AI), Machine learning and deep learning are terms that refer to the technology that possesses the ability to recognize patterns from the database that it is initially provided with and matching to it to similar data set thus mapping the results based on past occurrences. The use of AI in digital pathology has gained momentum over the last few years. Notably, the FDA approved an AI-tool as a primary diagnosis tool as recently as in 2017 for wrist fractures. The application of AI for pathology services are innumerable considering the fact the pathological services give rise to a lot of information. Information or data sets that can serve as the basis of creating deep learning digital pathology tools that can act as a diagnosis supplement tool for pathologists, thus increasing the speed of diagnosis, maybe accuracy and certainly providing the potential to prioritize diagnosis cases based on severity for the pathologists to review. The next decade of the medical field is set to witness the transformation of digital pathology services by AI tools.

Table of Contents

1. Executive Summary
1.1 Research Objectives
1.2 Research Methodology
1.3 Overview of Business and Market Segmentation of Digital Pathology Industry
1.4 Key Findings: Digital Pathology and Artificial Intelligence (AI)
2. Workflow and Commercial Landscape of Digital Pathology
2.1 Digital Pathology: Current Workflow and Future Possibilities
2.2 Technology Segmentations of Digital Pathology
2.3 Fields of Application
2.4 Business Models
2.5 Strategic Design Needs to Improve Commercialization
3. Segment Assessment – Cloud Technology
3.1 Impact of Cloud Technology on Digital Pathology
4. Segment Assessment – Whole Slide Imaging
4.1 Whole Slide Imaging (WSI) is Paramount to Digital Pathology
4.2 Efficient WSI Integration for Improved workflow
5. Segment Assessment – Image Analysis
5.1 Image Analysis Software
6. Segment Assessment – Artificial Intelligence
6.1 Artificial Intelligence- Introduction
6.2 Focus Areas of AI companies in Digital Pathology
6.3 Important Advances in AI Capabilities in Digital pathology
7. Case Study - Takeaways from Digital Pathology Adoption in a Medical Institute
7.1 Smooth Transitioning Innate to Successful Adoption of Technology
7.2 Digital Pathology Adoption needs a Phased Approach
7.3 Strategic Technology Integration and Personnel Training pays Dividends
7.4 Phased workflow implementation provides numerous benefits
8. Drivers and Challenges
8.1 Factors Affecting Adoption of AI in Digital Pathology
9. Industry Landscape of Selected Companies
9.1 Aiforia and ContextVision- Company Profile
9.2 Deciphex and Indica Labs- Company Profile
9.3 Barco & Inspirata- Company Profile
9.4 Leica Biosystems & Flagship Biosciences- Company Profile
10. Patent Landscape Assessment
10.1 Patent Research Scope and Concepts
10.2 Top 10 Patent Holding Companies in Digital Pathology Technologies
10.3 Top 10 Patent Holding Educational Institutes in Digital Pathology Technologies
10.4 Office-wise Distribution of Digital Pathology Patent Portfolio, 2008-2018*
10.5 Year-wise Distribution of Digital Pathology Portfolio, 2008–2018*
11. Growth Opportunities
11.1 Growth Opportunity 1: Optimal use of Big Data Key for AI Advancement in Digital Pathology
11.2 Growth Opportunity 2: Generation of Datasets for Validation Paramount to Overcome Regulatory Hurdles
11.3 Growth Opportunity 3: Transitioning from Pharmaceutical Industry Application to Mainstream Clinical Applications
12. Appendix
12.1 Key Contacts
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