Digitization in pathology
Digitization in Pathology has enabled increase in efficiency, speed and enhanced quality of diagnosis. Recent technological advances have accelerated the adoption of digitization in pathology, similar to the digital transformation that radiology departments have experienced over the last decade. Digital Pathology has enabled conversion of the traditional glass slide to a digital image, which can then be viewed on a monitor, annotated, archived and shared digitally across the globe, for consultation based on organ sub-specialization. With digitization, a vast data set has become available, supporting new insights to pathologists, researchers, and pharmaceutical development teams.
The promise of AI
The availability of vast data is enabling use of Artificial Intelligence methods, to further transform the diagnosis and treatment of diseases at an unprecedented pace. Human intelligence assisted with artiﬁcial intelligence, can provide a well-balanced view of what neither of them could do on their own. The evolution of Deep Learning neural networks and the improvement in accuracy for image pattern recognition has been staggering in the last few years. Similar to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time improving it a little to achieve a more accurate outcomes.
The approach to diagnosis that incorporates multiple sources of data (e.g., pathology, radiology, clinical, molecular and lab operations) and using mathematical models to generate diagnostic inferences and presenting with clinically actionable knowledge to customers is Computational Pathology. Computational Pathology systems are able to correlate patterns across multiple inputs from the medical record, including genomics, enhancing a pathologists’ diagnostic capabilities, to make a more precise diagnosis. This allows Pathologists to eliminate tedious and time-consuming tasks, while focusing more on interpreting data and detailing the implications for a patient’s diagnosis.
AI applications that can easily augment a Pathologist’s cognitive ability and save time, are for example, identifying the sections of greatest interest in biopsies, finding metastases in the lymph nodes of breast cancer patients, counting mitoses for cancer grading or measuring tumors point-to-point. The ultimate goal going forward is the integration of all these tools & algorithms into the existing workflow and make it seamless and more efficient.