The technique of digital pathology involves scanning glass slides using a whole slide image scanner, then employing an image viewer—typically found on a computer display or mobile device—to analyze the digital pictures. With an image viewer, pathologists can manipulate slides in the same manner they would with a conventional light microscope. While there haven’t been many significant changes to the fundamental viewing capabilities, digital pathology has significantly improved workflow, pathology lab efficiency, and profitability.
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It has taken over 20 years for whole slide imaging, or WSI, to evolve in the field of digital pathology. From mounting a camera on a microscope lens to creating the first scanners, technology has advanced to the point where it is now, rapidly turning into a necessary tool in anatomical laboratories. The original scanners were big, bulky devices with expensive storage, a processing time of up to six minutes, and a restricted range of applications. These days, a scanner can take up to 1,000 slides at once, be set to several magnifications, and scan slides in as little as 30 seconds. These days, cloud storage is accessible, safe, and reasonably priced. Artificial intelligence (AI)-powered computational systems provide a multitude of methods for picture analysis and presentation to pathologists.
Digital pathology has evolved in a manner akin to that of mobile phones. The initial iterations were too large, peculiar, and constrained in terms of necessity, expense, and coverage. In the early 1980s, no one could have predicted how widely used mobile phones would become. Today, they are as common as watches. Similar to how mobile phone usage has increased dramatically with more powerful mobile devices, digital pathology as a discipline has soared with the development of whole-slide image scanners.
How is pathology in digital form employed today?
Nowadays, digital pathology is applied in three basic methods. First, research organizations that maintain and want to utilize millions of specimens, like pharmaceutical corporations, CROs, and university medical facilities, use digital pathology in their rigorous study design, data gathering, and database maintenance.
Second, digital pathology is used in some clinical lab cases for remote consultations, teaching, or quantitative analysis.
The third use case, which is expanding the quickest, is about clinical laboratories switching to an all digital process. These labs use artificial intelligence (AI)-powered computationally-enabled digital pathology to assist in case assignment to pathologists, organize workflow into worklists, provide quantitative image analysis for particular case types, integrate data into their current Lab Information System (LIS), and increase case accessibility outside of the lab’s physical boundaries. Digital pathology eliminates the requirement for tissue sample transportation in person or the resulting wait periods when pathologists are not in the office, therefore speeding up diagnosis.
How can whole slide imaging benefit from computational pathology?
AI programs have the ability to “read” a whole slide picture and utilize specific algorithms to carry out a variety of helpful clinical activities that support the pathologist’s function. It is commonly recognized that pathology pictures include basic prognostic information. These days, software programs can measure features of tissue that are frequently undetectable to the human eye, not even under a microscope, in order to forecast tumor aggressiveness, prognosis, and ultimately, patient outcomes. The reason laboratories are embracing digital pathology so rapidly is being redefined by these AI technologies that fall under the computational pathology umbrella. By providing the equivalent of a second opinion, identifying patterns that the human eye is blind to, or warning pathologists of any differences, the same applications may be used to decrease malpractice.
Other use cases that computational technologies may assist with in the background to improve laboratory workflow efficiency and maximize pathologists’ time include sorting and workload balancing. An AI program may, for instance, automatically classify tissue samples according to the stage of the illness and then send them to particular pathologists for examination. The simpler cases might be evaluated by a more experienced pathologist first, delaying the more intricate biopsies until later in the day. Find out more about applications of AI.
On the other hand, a pathologist with less expertise may delegate simple cases to his colleagues while taking on more difficult cases directly away.
What is the future of digital pathology?
AI-enabled digital pathology will probably follow most technological trends as it develops, with costs continuing to decline and an expanding range of applications. More effective laboratories are required as a result of the growing volume of biopsies and the decreasing number of pathologists. Additionally, possibilities to apply digital pathology tools as part of integrated diagnostics and prognostic decision making will arise from overlap with other AI applications, such as those in radiology and MRI.
Upon case signout, some labs have already configured their digital pathology system to automatically load specific pictures from their radiology PACS (Picture Archiving Communication System). This enables treating physicians to examine and explore the exact slide that serves as the basis for the patient’s diagnosis. Radiologists can study radiographs, MRI and CT scans, and the accompanying histology slides in a single view at the same time.
The challenges of expanding server storage and reorganizing infrastructure suddenly appear less intimidating than before, with so many laboratories doing amazing things by implementing digital pathology. A growing number of laboratories will be putting their scanners back in service and returning to the discussion of transitioning to digital pathology. Labs will benefit from a new era of collaboration and even more significant advances in workflow efficiency as the use of digital and computational pathology rises. The final result? quicker, more precise diagnosis for patients and access to a larger amount of data for their treating providers.
