User:SaLaPi22/sandbox

Notes to the reader(s) of this sandbox: I added in much needed links to other wikipedia pages for reference. I also added in shorthands for these more specialized words (acronyms). I additionally edited the text that was already written for a few of the sections which originally came across as a bit disorganized and not proofread. The original text did not have any mention of the role of AI in particular, and many ideas were not expanded upon. There was no photos associated with this page originally, and although it was hard finding a non-copy written or public domain images for use on this public access encyclopedia, I was finally able to find one (details added, below). Finally, I decided to add in a few more pieces of information to the sections listed in this sandbox - and decided to go through a select number of sections and generally edit the wording and continued adding hyperlinks.

The following image that was found through WikiCommons, but has yet to be used on any Wikipedia article. :

= Digital pathology = From Wikipedia, the free encyclopedia

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 within the medical practice of pathology. Digital pathology utilizes 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.

Edited: (below)

Digital pathology is a sub-field of pathology that focuses on data management based on information generated from digitized specimen slides. Through the use of computer-based technology, digital pathology utilizes virtual microscopy. Glass slides are converted into digital slides that can be viewed, managed, shared and analyzed on a computer monitor. With the practice of Whole-Slide Imaging (WSI), which is another name for virtual microscopy, the field of digital pathology is growing and has applications in diagnostic medicine, with the goal of achieving efficient and cheaper diagnoses, prognosis, and prediction of diseases.

Potential[edit | edit source]
Trained pathologists traditionally view tissue slides under a microscope. These tissue slides may be stained to highlight cellular structures. When those slides are digitized, they are able to be shared through tele-pathology and are 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. They can additionally be used for feature detection of mitotic figures, epithelial cells, or tissue specific structures such as lung cancer nodules, glomeruli, or vessels. This could reduce human error and improve accuracy of diagnoses. Digital slides can be easily shared, increases the potential for data usage in education as well as in consultations between experts.

Analyze[edit | edit source]
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 (ML) using deep learning (DL) methods show promise and allow to make information hidden in integrated pathological data (images, patient history and -omics data) in arbitrarily high-dimensional spaces both accessible and quantifiable. Thus, generating a novel source of information which is not yet available to the expert and not exploited in current Digital Pathology settings.

Integrate[edit | edit source]
Digital pathology workflow is integrated into the institution's overall operational environment. Slide digitization is expected to reduce the number of routine manually reviewed slides, maximizing workload efficiency.

Sharing[edit | edit source]
Digital pathology also allows internet information sharing for education, diagnostics, publication and research. This may take the form of publicly available datasets or open source access to ML algorithms.