Digital Pathology

Digital pathology (DP) is an image-based environment that enables the management, acquisition and interpretation of pathology information generated from a digitized glass slide. It involves the technique of analyzing high-resolution images and taking the advantage of computational tools and algorithms. Digital pathology helps in the early diagnosis and treatment protocol optimization in Oncology and other chronic diseases.

Image analysis techniques are applied to the digitized images for Identification, to define the boundary of tumor, find proteins and cells of interest inside and adjacent to the tumor. The role of computer-based imaging plays a key role and helps pathologists in making an improved decision. Therefore, Intelligent Digital pathology has the potential to bring a paradigm shift from conventional microscopy to pathology powered by AI in the near future.

Digital pathology is a convergence of pathology and computers that has the potential to replace the conventional microscope-based diagnosis in the near future. DP serves as a tool for advanced pathology practice to improve productivity and efficiency, insure against damage to glass slides, reduce manual errors, surgeons, assist pathologists to identify the bottlenecks in laboratory workflow, improve timely treatment decisions to achieve enhanced patient care.

Market Size

The global digital pathology market was valued at $390.7 million in 2016, and growth projected to reach $1.33 billion by 2024, growing at a CAGR of 16.6% from 2017 to 2024.

The market is expanding at fast pace, driven by increase in installation of the systems. The high prevalence of cancer and accurate pathological assessment & digitalization are the factors that project to fuel the global digital pathology market during the forecast period.

Digital pathology is mostly used by pharma & biotechnology companies involved in toxicological studies, pathology labs and educational institutions etc. However, it’s bound to play a dominant role in the near future in the healthcare for primary diagnosis purpose.

The challenges faced in adopting digital pathology:

• High cost structure,
• Complexity in practical implementation of cloud-based digital pathology systems
• Increase in prevalence of chronic diseases
• Low awareness about digital pathology systems and pathologists globally
• High healthcare costs
• Difficulty in implementing digital pathology in low resource countries

Digital pathology creates an environment where several pathologists can access whole slide images, and zoom into various areas via a computer or a smart phone without physically moving the slide. Pathologists embrace the revolution, as digital pathology system is not limited to transfer of analog data from a glass slide to a monitor but augment the human eye with information/data to draw intelligent predictions.

AI and deep learning technologies mimic human cognitive capabilities using sophisticated imaging processing techniques, automated analysis and MI algorithms reduce the errors. These trained multilayered convolutional networks perform all intricate computational tasks and disclose more information/data answering queries related to a particular disease, and digital slides/ images can be stored forever without compromising on quality.

Benefits

• The accurate pathological assessment
• It enables tele pathology
• Increases the efficiency and productivity of pathologists
• Easy collaboration for pathologists to overview their work, and
• Allows pathologists to work from anywhere, anytime