Digital pathology is an image-based information environment which is enabled by computer technology
that allows for the management of information generated from a digital slide. Digital pathology is enabled in part by virtual microscopy,
which is the practice of converting glass slides into digital slides that can be viewed, managed, shared and analyzed on a computer monitor.
With the advent of Whole-Slide Imaging, the field of digital pathology has exploded and is currently regarded as one of the most promising
avenues of diagnostic medicine in order to achieve even better,faster and cheaper diagnosis, prognosis and prediction of cancer and other important diseases.
In pathology, trained pathologists look at tissue slides under a microscope. The tissue on those slides may be subjected to staining to highlight structures.
When those slides are digitized, they then have the potential to be shared (tele-pathology) and numerically analyzed using computer algorithms. Algorithms can
be used to automate the manual counting of structures, or for classifying the condition of tissue such as is used in grading tumors. This could reduce human
error and improve accuracy of diagnoses. Digital slides are also, by nature, easier to share than physical slides.
This increases potential for using data for education and consultations between two or more experts.
Digital slides are created from glass slides using a scanning device. Digital pathology requires high quality scans free of dust, scratches, and other obstructions.
Digital pathology are accessible for viewing via a computer monitor and viewing software either locally or remotely via the Internet and data input the report Value automatically
by scan the QR Code On Tube.
Example: digital pathology tissue slide stained with Her2/neu biomarker used for diagnosis of breast cancer.
Digital pathology are maintained in an information management system that allows for archival and intelligent retrieval.
Digital pathology are often stored and delivered over the Internet or private networks, for viewing and consultation.
Image analysis tools are used to derive objective quantification measures from digital slides. Image segmentation and classification algorithms are used to identify medically significant regions and objects on digital slides. Recent developments in machine learning using deep learning methods are very promising and allow to make information hidden in integrated pathological data (images, patient history and *omics data) in arbitrarily high-dimensional spaces accessible and quantifiable, thereby generating a novel source of information which is not yet available to the expert and not exploited in current Digital Pathology settings.
Digital pathology work flow is integrated into the institution's overall Medical Devices.
Digital pathology also allows Internet information sharing for education, diagnostics, publication and research.