AI Pathology Products Market Analysis


The objective of research aims to analyze the AI pathology or histopathology market, regarding patents and market participants. Specific goals are the assessment of technology and market development stages, identification of growth opportunities, and evaluating the sophistication of existing products available to pathologists and labs.

Early Findings

  • Artificial intelligence (AI) has been applied in the space of pathologies, such as histopathology, which assists pathologists with the detection and counting of cells as well as the detection of tissues' segments and their classification. These are crucial tasks in determining whether patients have diseases, such as cancer. More importantly, AI's application in imaging reduces the amount of repetitive and tedious tasks, such as the reading and analysis of medical images, to identify patterns in whole slides. In the future, AI is expected to improve operating efficiency and the accuracy of the diagnosis work. Moreover, with the increasing use of AI in histopathology, new problems are expected to arise, such as the magnification of tissue analyses, annotation of data used for training machines, and suitability of training dataset.
  • In the past five years, AI and its sub-set techniques, such as machine and deep learning, were making inroads into diagnostic pathology. By replacing conventional glass slides, the whole slide imaging technology could generate and scan digital images that reduce both the time and cost of reading and analysis by humans, resulting in timely treatment to patients. However, pathology labs require more digitization, as without digitized and standardized workflows and the digital processing of tissues, the AI pathology solutions are not expected to be feasible, regarding "automated image analysis and computer-assisted diagnosis."
  • AI in pathology, such as tissue analytics, is still in the early stage of development. Although there has been significant growth in this space with the potential to transform the work of pathologists by integrating AI applications into pathology workflows, the gap between research and practical solutions remains wide, regarding the accuracy of diagnosis and the clinical outcome of patient care. With continual innovation in AI-based pathologies, such as deep learning solutions, the gap is expected to narrow and AI could achieve a similar success rate as pathologists, for example, AI could detect 0.2mm tumours or tumours made up of less 200 cells. The research found that a deep learning system has achieved an accuracy rate of 0.7 in comparison to 0.61 by pathologists.
  • In the space of digital pathology, there has been an increasing number of patents for at least the past two decades. In January 2014, there were 588 patents granted in the US, in which 59.2% were specifically focusing on pathology. There has been a trend of patent application in "computer-aided diagnosis and digital consultation networks."
  • In November 2019, ContextVision, a medical software vendor in image analysis and AI, received the US patent on "method and system for detecting pathological anomalies in a digital pathology image and method for annotating a tissue slide". Its new software product, INIF Prostate Screening, could learn based on the patented method.
  • In October 2019, Proscia, a digital pathology software vendor, was granted a patent relating to dermatopathology techniques, which are based on the deep learning of human skin biopsies. The analysis of digital images could be used for diagnosis. Its recently released DermAI software embeds these patented techniques (U.S. Patent No. 10,460,150) to deliver functions, such as intelligent workflow balancing. Moreover, Proscia received another patent (U.S. Patent No. 10,346,980) in September 2019, regarding "techniques for processing and analyzing medical images critical to the advancement of digital pathology."
  • In 2019, the University of Waterloo's Kimia Labs has partnered with an industry consortium to invest in research on image-guided therapy. The main objective is on AI-driven auto-reporting through analyzing and learning from past histopathology images and diagnosed cases.
  • Market research on AI in-vitro diagnostics has seen the noticeable application of machine learning in "digital pathology, diabetes management, microbiology, disease genetics, and cancer precision medicine." These applications aim to improve data analysis and "standardize and aid the interpretation of test data." In 2019, 60 primary participants were in the market, such as Adaptive Biotechnologies , ARUP Laboratories , Philips, Google, IBM Watson Health and Siemens Healthineers.
  • Market research on digital pathology revealed that revenue is expected to grow from $613 million to $1,139 million, representing a CAGR of 13.2% between 2020 and 2025. Globally, the North American region is the largest market with the highest growth potential, followed by Europe and Asia. Scanners, drug discovery and pharmaceutical and biotechnology companies are the leading revenue contributors.
  • Three market leaders in digital pathology products and solutions are: "Leica Biosystems (US), Koninklijke Philips N.V. (Netherlands), and Hamamatsu Photonics (Japan). 17 other major market participants are: Roche (Switzerland), 3DHISTECH (Hungary), Apollo Enterprise Imaging (US), XIFIN (US), Huron Digital Pathology (Canada), Visiopharm A/S (Denmark), Corista (US), Indica Labs (US), Objective Pathology Services (Canada), Sectra AB (Sweden), OptraSCAN (India), Glencoe Software (US), KONFOONG BIOTECH INTERNATIONAL CO., LTD (China), Inspirata, Inc. (US), Mikroscan Technologies (US), Proscia Inc. (US), Kanteron Systems (Spain)."

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