The landscape of
disease diagnosis
Disease diagnosis is traditionally determined by pathologists who examine histology slides under a microscope. In 2019, the European Union approved the usage of whole slide scans for primary diagnosis, wherein routine glass histopathology slides are digitized and presented to pathologists on computer monitors. This DP approach represents a major inflection point for both research and clinical workflows, since once slides are digitized, they become amenable to digital transfer and computational analysis.
Innovating
with digital pathology
The standardization and sharing of DP data are crucial for the development, testing, and validation of machine learning-enabled tools. As the Swiss Digital Pathology Consortium (SDiPath), industry, and academic institutions have shown in a multitude of research studies, DP images can be analyzed by computer algorithms to enable discovery of clinically relevant biomarkers and more precise characterization of disease presentation features. Through the analysis of continually growing retrospective cohorts, DP can provide enhanced diagnosis, prognosis, and therapy response predictions. This significantly contributes to the advancement of precision medicine, enabling providers to tailor treatment plans for each patient, optimizing care.
Benefits
of digital pathology
Speed and Efficiency
DP & AI help to create more efficient pathology workflows
Advancing the Biomedical Industry
DP can advance precision medicine by enhancing diagnosis, prognosis, and predictions of therapy responses.
Tissue Preservation
DP techniques are tissue non-destructive, allowing for further analysis many years later.
Enhanced Analysis
DP images can be analyzed using computer algorithms, leading to more precise characterization of disease presentation features.
Real-Time Diagnostic Support
DP enables real-time diagnostic support in clinical practice, improving patient care outcomes.
Longitudinal Studies
DP facilitates longitudinal studies, allowing for continuous improvement of algorithms as understanding of diseases evolves over time.