How does one define digital pathology?
The technique of scanning glass slides using a whole slide image scanner and evaluating the digital pictures with an image viewer—usually on a computer display or mobile device—is known as digital pathology. Pathologists can move slides around in an image viewer in a manner like to that of a conventional light microscope. Digital pathology has led to remarkable improvements in pathology lab efficiency, workflow, and revenue advantages, even if the fundamental viewing functionality has not changed significantly.
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It has taken nearly 20 years for digital pathology, or whole slide imaging (WSI), to advance. From the early days of developing scanners to the present day, when technology is fast becoming essential in anatomic laboratories, it has evolved from the simple act of mounting a camera on a microscope’s lens. The initial scanning devices were cumbersome, large-footprint devices with processing times as long as six minutes, pricey storage, and a narrow range of applications. These days, scanners can take up to 1,000 slides at once, scan slides in as little as 30 seconds, and be adjusted at different magnifications. These days, storage is accessible, safe, and reasonably priced thanks to the cloud. Artificial intelligence (AI)-based computational systems provide the pathologist with several options for picture analysis and presentation.
The development of digital pathology bears a striking resemblance to the mobile phone. The first iterations had excessive size, eccentricity, and coverage, expense, and necessity limitations. The cell phone has become as commonplace as the watch and is being utilized in ways that were unimaginable in the early 1980s. The field of digital pathology has flourished with the development of whole-slide image scanners, just as the use of mobile phones has increased dramatically due to increasingly potent gadgets.
In what ways is digital pathology applied today?
Nowadays, there are three primary applications for digital pathology. First, digital pathology is used by research organizations—pharmacies, CROs, and university medical centers—in their rigorous study design, data gathering, and database administration for the millions of specimens they now maintain and want to utilize.
Second, for remote consultations, instruction, or quantitative analysis, clinical laboratories employ digital pathology in a limited number of cases.
Clinical labs switching to an all digital process is the subject of the third and fastest-growing use case. In order to assist pathologists in assigning cases, organize workflow into worklists, provide quantitative image analysis for particular case types, integrate data into their current Lab Information System (LIS), and increase access to cases outside of the lab’s physical walls, these labs employ computationally enabled digital pathology powered by AI. With digital pathology, waiting periods caused by pathologists’ absence from the office or the actual transportation of tissue samples no longer need to cause delays in diagnosis.
How might entire slide imaging be made more valuable by computational pathology?
Applications using artificial intelligence (AI) may “read” a whole slide picture and use specific algorithms that can carry out a variety of helpful clinical activities to support the pathologist’s job. Fundamental prognostic information is known to be included in pathology pictures. In order to forecast the likelihood of a diagnosis, the aggressiveness of tumors, and ultimately the outcomes for patients, software tools can now measure features of tissue that are frequently unseen to humans, not even under a microscope. Under the general heading of computational pathology, these AI applications are changing the reasons behind laboratories’ rapid adoption of digital pathology. The aforementioned applications have the potential to mitigate malpractice by providing an akin to second opinion, identifying patterns imperceptible to the human eye, or promptly informing pathologists of any disparities.
Other scenarios include workload balance and sorting, which computational technologies may assist with in the background to improve productivity in the laboratory and maximize pathologists’ time. For instance, an AI program may automatically classify tissue samples based on the stage of the illness and then forward them to particular pathologists for examination. Preserving the more intricate samples until later in the day, a pathologist with greater experience may evaluate the simpler ones first. Study up on applications of artificial intelligence.
On the other hand, a pathologist can delegate simple cases to his less seasoned colleagues while taking on more difficult cases directly away.
Where does digital pathology intend to go?
As AI-enabled digital pathology develops, it is anticipated to follow most technological trends, meaning that prices will continue to decline and the range of applications will rise. More effective laboratories are required as a result of the falling number of pathologists and the increasing volume of biopsies. Furthermore, integration with other AI applications, such as MRI and radiology, will open up new possibilities for the use of digital pathology tools in integrated diagnostics and prognostic decision making.
Treating physicians may now examine and explore the exact slide that serves as the basis for the patient’s diagnosis since some laboratories have already configured their digital pathology system to automatically load a selection of pictures from their radiology PACS (Picture Archiving Communication System) upon case signout. Radiologists have simultaneous access to radiographs, MRI, CT scans, and the relevant histology slides in a single view.
Increasing server capacity and reorganizing infrastructure seems less intimidating than it always was, especially with the abundance of examples of laboratories using digital pathology to great effect. As digital pathology becomes more prevalent, more and more laboratories will be putting their scanners back in service and returning to the discussion. Labs will benefit from increasingly significant improvements in workflow efficiency and a new era of collaboration as the use of digital and computational pathology rises. the final result? Patients will receive faster, more precise diagnoses and their treating physicians will have access to a greater amount of information.
