Anatomic Pathology Track and Trace Solutions Market expected to reach $1,028.63 million by 2026 growing at a CAGR of 12.2% | Thermo Fisher Scientific Inc., Agilent Technologies, Inc., General Data Healthcare Inc., Primera Technology

Press Release

The report on “Computer Vision in Healthcare Market” will help Major Players and the new entrants to understand scrutinize the market in detail. This information will encourage the Major Players to decide their business strategy and achieve proposed business aims.

Global Anatomic Pathology Track and Trace Solutions market is accounted for $364.20 million in 2017 and is expected to reach $1,028.63 million by 2026 growing at a CAGR of 12.2%. Significant factors which are influencing the market growth are rising number of legal cases around cancer misdiagnosis and growing number of diagnostic tests performed in laboratories. However, high implementation cost of these solutions in laboratories is hampering the market growth.

Based on technology, barcode segment is anticipated to have a significant growth due to the increasing adoption of barcode systems among end users. Barcodes are optical machine-readable representations of data where each character is represented by patterns. There are 2 different barcode types namely 1D (linear) and 2D (eg, DataMatrix, MaxiCode, QR code). The 1D barcodes can be numeric or alphanumeric. The advantages of using 2D barcodes are that they contain higher data density, produce smaller barcodes, allow omnidirectional scanning, and result in fewer scan and printer failures.

Top Companies Covered in this Report:
Thermo Fisher Scientific Inc., Agilent Technologies, Inc., General Data Healthcare Inc., Primera Technology, Inc., AP Easy Software Solutions, Zebra Technologies Corporation, Leica Biosystems Nussloch and LigoLab.

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The report aims to provide an overview of Computer Vision in Healthcare market with detailed market segmentation by mounting location, vehicle type and geography. The global Computer Vision in Healthcare market is expected to witness high growth during the forecast period. The report provides key statistics on the market status of the leading Computer Vision in Healthcare market players and offers key trends and opportunities in the market.

The report provides a detailed overview of the industry including both qualitative and quantitative information. It provides an overview and forecast of the global Computer Vision in Healthcare market based on various segments. It also provides market size and forecast estimates from the year 2017 to 2026.

The report analyzes factors affecting Computer Vision in Healthcare market from both demand and supply side and further evaluates market dynamics effecting the market during the forecast period i.e., drivers, restraints, opportunities, and future trend. The report also provides exhaustive PEST analysis for all five regions namely; North America, Europe, APAC, MEA and South & Central America after evaluating political, economic, social and technological factors effecting the Computer Vision in Healthcare market in these regions.

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Fundamentals of Table of Content:

1 Report Overview
1.1 Study Scope
1.2 Key Market Segments
1.3 Players Covered
1.4 Market Analysis by Type
1.5 Market by Application
1.6 Study Objectives
1.7 Years Considered

2 Global Growth Trends
2.1 Computer Vision in Healthcare Market Size
2.2 Computer Vision in Healthcare Growth Trends by Regions
2.3 Industry Trends

3 Market Share by Key Players
3.1 Computer Vision in Healthcare Market Size by Manufacturers
3.2 Computer Vision in Healthcare Key Players Head office and Area Served
3.3 Key Players Computer Vision in Healthcare Product/Solution/Service
3.4 Date of Enter into Computer Vision in Healthcare Market
3.5 Mergers & Acquisitions, Expansion Plans

4 Breakdown Data by Product
4.1 Global Computer Vision in Healthcare Sales by Product
4.2 Global Computer Vision in Healthcare Revenue by Product
4.3 Computer Vision in Healthcare Price by Product

5 Breakdown Data by End User
5.1 Overview
5.2 Global Computer Vision in Healthcare Breakdown Data by End User

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This post was originally published on Analytics News