Advances in artificial intelligence are slowly transforming the specialty, much the way radiology is being transformed by similar advances in digital technology.
John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.
Any patient who faces a potential cancer diagnosis knows how important an accurate, timely pathology report is. Similarly, surgeons often require fast pathology results when they are performing a delicate procedure to determine their course of action during an operation. New technological developments are poised to meet the needs of patients and clinicians alike.
AI can improve pathology practice in numerous ways. The right digital tools can automate several repetitive tasks, including the detection of small foci. It can also help improve the staging of many malignancies, make the workflow process more efficient, and help classify images, which in turn gives pathologists a “second set of eyes”. And those “eyes” do not grow tired at the end of a long day or feel stressed out from too much work.
Such capabilities have far-reaching implications. With the right scanning hardware and the proper viewer software, pathologists and technicians can easily view and store whole slide images (WSIs). That view is markedly different from what they see through a microscope, which only allows a narrow field of view. In addition, digitization allows pathologists to mark up WSIs with non-destructive annotations, use the slides as teaching tools, search a laboratory’s archives to make comparisons with images that depict similar cases, give colleagues and patients access to the images, and create predictive models. And if the facility has cloud storage capabilities, it allows clinicians, patients, and pathologists around the world to access the data.
A 2020 prospective trial conducted by University of Michigan and Columbia University investigators illustrates just how profound the impact of AI and ML can be when applied to pathology. Todd Hollon and colleagues point out that interoperative diagnosis of cancer relies on a “contracting, unevenly distributed pathology workforce.”1 The process can be quite inefficient, requiring a tissue specimen travel from the OR to a lab, followed by specimen processing, slide preparation by a technician, and a pathologist’s review. At University of Michigan, they are now using Stimulated Raman histology, an advanced optical imaging method, along with a convolutional neural network (CNN) to help interpret the images. The machine learning tools were trained to detect 13 histologic categories and includes an inference algorithm to help make a diagnosis of brain cancer. Hollon et al conducted a 2-arm, prospective multicenter, non-inferiority trial to compare the CNN results to those of human pathologists. The trial, which evaluated 278 specimens, demonstrated that the machine learning system was as accurate as pathologists’ interpretation (94.6% vs 93.9%). Equally important was the fact that it took under 15 seconds for surgeons to get their results with the AI system, compared to 20-30 minutes with conventional techniques. And that latter estimate does not represent the national average. In some community settings, slides have to be shipped by special courier to labs that are hours away.
Mayo Clinic is among several forward-thinking health systems that are in the process of implementing a variety of digital pathology services. Mayo Clinic has partnered with Google and is leveraging their technology in two ways. The program will extend Mayo Clinic’s comprehensive Longitudinal Patient Record profile with digitized pathology images to better serve and care for patients. And we are exploring new search capabilities to improve digital pathology analytics and AI. The Mayo/Google project is being conducted with the help of Sectra, a digital slide review and image storage and management system. Once proof of concept, system testing, and configuration activities are complete, the digital pathology solution will be introduced gradually to Mayo Clinic departments throughout Rochester, Florida, and Arizona, as well as the Mayo Clinic Health System.
The new digital capabilities taking hold in several pathology labs around the globe are likely to solve several vexing problems facing the specialty. Currently there is a shortage of pathologists worldwide, and in some countries, that shortage is severe. One estimate found there is one pathologist per 1.5 million people in parts of Africa. And China has one fourth the number of pathologists practicing in the U.S., on a per capita basis. Studies predict that the steady decline of the number of pathologists in the U.S. will continue over the next two decades. A lack of subspecialists is likewise a problem. Similarly, there are reports of poor accuracy and reproducibility, with many practitioners making subjective judgements based on a manual estimate of the percentage of positive cells for a biomarker. Finally, there is reason to believe that implementing digital pathology systems will likely improve a health system’s financial return on investment. One study has suggested that it can “improve the efficiency of pathology workloads by 13%.” 2
As we have said several times in these columns, AI and ML are certainly not a panacea, and they will never replace an experienced clinician or pathologist. But taking advantage of the tools generated by AI/ML will have a profound impact of diagnosis and treatment for the next several decades.
References
1. Hollon T, Pandian B, Adapa A et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat. Med. 2020. 26:52-58.
2. Ho J, Ahlers SM, Stratman C, et al. Can digital pathology result in cost savings? a financial projection for digital pathology implementation at a large integrated health care organization. J Pathol Inform. 2014;5(1):33; doi: 10.4103/2153-3539.139714.
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