Digital Pathology – From Slides to Screen

Abstract

The term ‘Digital Pathology’ (DP) is used to denote digitisation efforts in pathology. DP can be defined as the digitalisation of gross and microscopic tissue specimens. Digital slides are created by scanning glass slides with a scanning device to provide high-resolution digital images that can then be managed, analysed, distributed and stored as digital images. Whole slide imaging helps to get high-resolution digital slides, the pathologist can scan the slides rapidly and focus by zooming in and out on the monitor using the keyboard, mouse, or his/her finger and gather information to make the diagnosis. These images are an accurate representation of the scanned glass slide and in some applications; they may be more valuable than the actual glass slides in terms of image resolution and ease of identification of specific diagnostic features. Digital imaging can be subdivided into two classes, that is, the digital microscopes to create a digital image and diagnosis-aided systems to detect the region of interest and give a presumptive diagnosis. The various benefits of using DP are similar in concordance in diagnosis as with glass slides with rapid access to second opinion, archiving and retrieval of slide images are much easier, and case histories and diagnostic information can be easily shared and retrieved. Another important field is the medical education, for graduates and postgraduate students, where difficult and rare cases can be shared and stored. The integration of clinical, laboratory and radiology data with pathology images, applying artificial intelligence (AI) for correlation is called computational pathology, which is the future of diagnostics. However, DP still has to deal with issues such as large data storage, high initial investment, confidentiality and lack of standardisation. These issues are being dealt with and newer solutions are being discussed. DP has started to expand and there are many well-established DP companies working towards the advanced diagnostic skills for pathologists and building the required business framework to support the development of precision medicine. Few biopharmaceutical companies and top clinical research organisations have adopted the concept of DP to streamline their drug development processes. DP can be relevant with the advent of assays such as markers or multiplex, which are difficult to discern with the human eye. With the increased use of exponential technologies such as AI and machine learning, enhanced translational research, computer-aided diagnosis and personalised medicine is expected to grow in the near future. After a DP system has been successfully deployed and integrated, the possibilities are immense. It is assumed that DP is not meant for taking pathologists out of the picture, infact with the emerging data analytics tools, DP will undoubtedly allow pathologists to make a more accurate and consistent diagnosis in the near future.