Introduction
The pathophysiology is changing in comparison to prior times. Digital pathology is one of the most significant advancements in this profession. Affordability of digital and telepathology products, ease of virtual slide transmission, and improved efficacy of pathology are all reasons driving demand.
On a computer, digital pathology technologies made it easy to examine and interpret the collected pictures. This enables high-resolution sample scanning as well as online storage of digital slides. As a result, even without physical evidence, pathologists can cross-examine slides.
Because of the cost and improved efficacy of the goods, the digital and telepathology market has seen tremendous expansion. Hospitals, biotech and pharmaceutical businesses, diagnostic labs, and academic and research organizations all utilize it.
The information provided by a digital slide can be better managed using digital pathology. Pathologists are eager to use such technology since they believe it to be really advantageous.
Evolution of Digital Pathology in terms of Large-Scale Management, Analysis, and Visualisation
Digital pathology has shown great promise in terms of improving cancer characterization, predicting outcomes, and guiding therapy options. Advances in image processing and machine learning, as well as an enormous increase in available CPU power, have assisted this. However, technological issues remain a key roadblock to clinical use.
These technical issues may be divided into three categories:
(1) data administration,
(2) analysis and visualization systems and algorithms, and
(3) visual analytics for interpretation.
Image processing, machine learning, and, more recently, deep learning algorithms have all contributed significantly to the advancement of digital pathology’s potential. All of these algorithms rely on vast amounts of high-quality labeled data. Moving data to algorithms for feature computing becomes difficult due to the increasing volume of data. Instead, systems must be built to allow algorithms to be moved to data and, where possible, to collocate algorithms and data.
Containerization and the growing popularity of technologies like Docker have enabled algorithms to be encapsulated, deployed at scale on a public/private cloud, and computed with little image migration. This is an active research topic in which the imaging community may benefit from the genomics community’s knowledge and tools.
Digital pathology relies heavily on visualization and visual analytics. Image visualization, or the ability to use a pan-and-zoom tool to quickly study and explore a digitized WSI, is critical. The capacity to overlay pictures with features to help in the interpretation of pathogenic feature sets is one necessity. Users require visual analytic tools in addition to overlaying features on photos for population-wide feature space exploration. This might be used to investigate the correlations between imaging features for a specific cohort, or to investigate the interaction between imaging features and related mutation information and/or survival outcomes [Figure 8]. Pathology quantitative imaging informatics is a full technology stack that provides such sophisticated visualization and visual analysis capabilities.
Significant achievements in the field of WSI have occurred over the previous 20 years. Users may now choose from a variety of commercial WSI scanners for both clinical and non-clinical applications. Hardware (e.g., z-scanning capability, hybrid WSI/robotic instruments) and software solutions have seen significant advancements in technology (e.g., image analysis). While FDA clearance of the WSI system for primary diagnosis in surgical pathology is expected to encourage further acceptance of WSI for clinical usage in the United States, a number of issues remain.
A good VNA implementation will assist pathology, as well as many other medical specialties. Although pathology images will benefit an enterprise VNA, the lack of standardization and universal DICOM compliance of WSIs is expected to slow the integration of these images into these new applications. Planning ahead, doing a comprehensive inventory evaluation, and selecting the right VNA and image capturing equipment will make this integration easier.
Conclusion
The usage of digital pathology is expected to increase dramatically over the next 20 years, boosting pathologists’ capacity to provide patient care. A crucial pathology informatics contribution to precision medicine will be the adoption of relevant approaches to validate, categorise, and study pathology imaging biomarkers integrated into the clinical decision-making process.
Understanding how morphology and molecular processes interact is crucial to the success of research into virtually every major illness. Much of this research is possible because of digital pathology. Indeed, a number of research groups have developed and shown a diverse range of approaches for performing quantitative microscopy analysis, including deep learning.
To make this vision a reality, infrastructure will need to be developed and deployed to scan, organize, and store incredibly huge quantities of WSI, both for therapeutic and research purposes. To summarise, despite the fact that the virtual microscope idea has been around for two decades, the use of digital pathology informatics tools in clinical practice is clearly still a work in progress.
