Appropriately identifying patient specimens is of critical importance to pathology labs. The College of Pathologists previously evaluated the average cost of labeling errors at approximately $280,000 per million specimens, adding up to over $1 million dollars a year for some of the larger institutes.1 Specimen labeling errors also result in the failure to provide proper and immediate care for patients, which can severely harm the patient, resulting in unnecessary morbidity and mortality.2
Artificial intelligence (AI) is one of the fastest growing new technologies of the last couple of years. As computing power has increased, so has the potential for using AI in the clinic, with researchers focusing on its role in solving complex biological problems and making healthcare more effective and efficient. AI isn’t an entirely new technology in histology and pathology; dating back to 1992, attempts have been made at introducing some basic form of AI, such as the PATHFINDER system for hematopathology diagnosis.1
Histology has evolved considerably since its beginnings in the 17th century, with advances in both specimen processing and analysis. Consequently, histology departments now face increasingly larger workloads. To adapt, they have integrated automated systems, which save time and allow histology professionals to work on other skill-based tasks, while maintaining enough flexibility to process and stain according to the needs of the medical or research lab. Here, we’ll explore how automation has been integrated into histology to speed up the workflow of both medical technicians and researchers.
Histology, the study of the anatomy of cells and tissues, is an important field of research used by researchers and physicians. While researchers seek to understand how each individual cell affects the function of tissues and organs, physicians study the histopathology of tissues, to see how they change in those affected by disease. Proper labeling of tissue samples at each step of the tissue preparation process is critical to the interpretation of histopathologic results, which are relied upon to correctly diagnose patients. However, histological techniques present unique obstacles for proper labeling that will often require innovative identification solutions to overcome.
Histology is one of the most varied fields of research, with a host of practical applications. Scientists have used the histological staining of tissues to understand how our bodies work, to discover novel therapeutic targets for disease, and to help diagnose patients suffering from illness. The term histology was coined in 1819 by Karl Mayer, who combined the two Greek words histos (tissues) and logos (study).1 However, the origins of histology date back even further with the advent of microscopy and the initial investigations into how tissues and organs work inside the body.