Digital pathology systems enabling quality patient care

Abstract

Pathology laboratories are undergoing digital transformations, adopting innovative technologies to enhance patient care. Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using glass slides and a microscope. Pathology professional societies have established clinical validation guidelines, and the US Food and Drug Administration have also authorized digital pathology systems for primary diagnosis, including image analysis and machine learning systems. Whole slide images, or digital slides, can be viewed and navigated similar to glass slides on a microscope. These modern tools not only enable pathologists to practice their routine clinical activities, but can potentially enable digital computational discovery. Assimilation of whole slide images in pathology clinical workflow can further empower machine learning systems to support computer assisted diagnostics. The potential enrichment these systems can provide is unprecedented in the field of pathology. With appropriate integration, these clinical decision support systems will allow pathologists to increase the delivery of quality patient care. This review describes the digital pathology transformation process, applicable clinical use cases, incorporation of image analysis and machine learning systems in the clinical workflow, as well as future technologies that may further disrupt pathology modalities to deliver quality patient care.

Using digital pathology to analyze the murine cerebrovasculature

“…Digital pathology has been used extensively in both clinical and research settings to facilitate high throughput screening and analysis of human samples. [22][23][24][25] Among the advantages of digital pathology are the algorithm-driven analysis of digitized images, reducing bias and improving quantitation of various parameters. [26][27][28] In addition to measurements of staining intensity and coverage, some software packages can be used to analyze microvessels via algorithms that analyze metrics of blood vessel geometry (which may track with healthy or disease states), such as blood vessel lumen area and wall thickness.…”

Artifact Augmentation for Enhanced Tissue Detection in Microscope Scanner Systems

“…The digital revolution first began in the research domain [3] with the gradual spread of whole slide image (WSI) technology due to the ease of sharing, remote visual inspection, seamless integration, image processing and analysis capabilities, efficient storage, and reliable archiving of digital slides. Various scanning devices have been developed to capture detailed tissue structures, ensuring high color fidelity and resolution through the optimal configuration of scanning parameters [4,5]. However, the need for automating and expediting the scanning processes arose as these solutions entered daily routine diagnostics where the substantial volume of samples hinders individualized fine-tuning of scanning parameters per slide, necessitating intelligent tissue detection algorithms [6].…”