Digital Pathology: From Technological Advances to Routine Clinical Application
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 9049
Special Issue Editor
Interests: digital pathology; artificial intelligence; tumor microenvironment; immunohistochemistry; melanoma; diagnostic and prognostic biomarkers in oncology
Special Issue Information
Dear Colleagues,
Integration of diagnostic and prognostic tools based on artificial intelligence (AI) will possibly represent a milestone for the healthcare system within the next decade. Histopathology is, however, only at the very beginning of this digital revolution.
Even though image analysis of histochemical stains has been a reality for many years, most automated procedures remain unadopted to the pathologists’ daily practice. Possible explanations include hurdles in computer technology, tissue complexity, costs, and hands-on time of digital quantification of immunohistochemistry, and difficulties related to a shift from microscope to monitor, which heavily alters the workflow of the department.
Automated procedures in pathology are, nonetheless, highly desired to reduce the pathologists’ workload and to increase the accuracy and precision of their assessments, which often are deemed subjective with low reproducibility.
Very recently, deep learning, an AI subfield, has created rapid advances in the performance of image analysis. This has, for instance, made automated analysis of conventional, low-cost hematoxylin-eosin stains feasible. Thus, AI possibly has the potential to simplify and standardize automated procedures within and across pathology departments, which, in time, will lead to better, faster, and cheaper patientcare. Nonetheless, considerable development and validation work lies ahead before AI-based methods are ready for integration at the departments of pathology.
This Special Issue welcomes research papers within all fields of digital pathology that may accelerate the implementation of automated procedures, ranging from methodology to clinical validation studies.
Dr. Patricia Switten Nielsen
Guest Editor
Manuscript Submission Information
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Keywords
- digital pathology
- histopathology
- artificial intelligence
- machine learning
- deep learning
- biomarkers
- biomedical imaging