Digitization in Pathology and the promise of Artificial Intelligence

The COVID-19 pandemic has had a profound impact across industries and healthcare in particular - every aspect of it is undergoing change- from diagnosis to treatment and through the entire continuum of care. This has also created an urgency in the healthcare industry, to look for innovative solutions and a boost to faster, efficient application of technologies like Artificial Intelligence (AI) and Deep Learning. Pathology is one area which stands to greatly benefit from these applications.

Pathologists today spend significant amount of time observing tissue samples under a microscope and they are facing resource shortages, growing complexity of requests, and workflow inefficiencies with the growing burden of diseases. Their work underpins every aspect of patient care, from diagnostic testing and treatment advice, to the use of cutting-edge genetic technologies. They also have to work together in a multidisciplinary team of doctors, scientists and healthcare professionals to diagnose, treat and prevent illness. With increasing emphasis on sub-specialization, taking a second opinion from specialists, means shipping several glass slides across laboratories, sometimes to another country. This means reduced efficiency and delayed diagnosis and treatment. The current situation has disrupted this workflow.

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.

The Challenge

However, Artificial Intelligence in Pathology is quite complex. The IT infrastructure required in terms of data storage, network bandwidth and computing power are significantly higher as compared to Radiology. Digitization of Whole Slide Images (WSI) in pathology generate large amounts of gigapixel sized images and processing them needs high performance computing. Training a deep learning network on a whole slide image at full resolution can be very challenging. With the increase in the processing power with the use of GPUs, there is a  promise to train deep learning networks successfully, starting with training smaller regions of interest.

Another key aspect for training deep learning algorithms is the need for large amounts of labeled data. For super-vised learning, a ground truth must first be included in the dataset to provide appropriate diagnostic context and this will be time consuming. Obtaining adequately labeled data by experts is the key.

Digitization in pathology supported by appropriate IT infrastructure is enabling Pathologists to work remotely without the need to wait for glass slides to be delivered and maintaining social distancing norms. The promise of Artificial Intelligence will only further accelerate seamless integration of algorithms into the existing workflow. These unprecedented times have raised many challenges, but are also providing us a chance to accelerate the application of AI and in turn to achieve the quadruple aim: enhancing the patient experience, improving health outcomes, lowering the cost of care, and improving the work life of care providers.

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