applications and challenges of digital imaging technology in pathology

Traditional diagnostic pathology is based on the examination of histological slides by pathologists. although it is the simplest diagnostic method, it has problems such as a long process and different opinions between pathologists. digital imaging technology has revolutionized diagnostic pathology recently using whole-slide imaging (wsi) and static/live streaming images. this technology can be used clinically for diagnosis, analyzing, remote consultation intraoperatively, telepathology, and archiving documents. additionally, it can be used for nonclinical purposes like educating, research, and marketing. there are some approaches to digital pathology analysis. one is quantitative analysis which is about detecting, classifying, and quantifying special components in the image through wsi. some examples of the detections are tumors, infection, proportion of special immune cells, adipose tissue, and connective tissue. the important characteristic of this analysis is through detection of obvious components in the image. the other approach is artificial intelligence or ai-based assessments that represent information about diagnosis, treatments, and prognosis. this ai technology could give us information about mutations in some cancers or patient prognosis. although digital technology in pathology progressed noticeably, analysis remains complex and challenging practically. one of the challenges is that many pathologists do not design software and on the other hand, many software designers do not know about pathology. to face this challenge both pathologists and software designers should collaborate. with all the challenges, pathologists and software designers interested in ai technology should know about ai models, algorithms, and limitations. to improve the ai models, designing software that is available to everyone is recommended, so that many researchers could analyze more images which gives us more information about the performance of the software. the exchange of data and cooperation between pathologists and software designers will lead to improvement in ai technology.