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Computational Pathology and Artificial Intelligence are reshaping the Pathology field: an interview with Keen Eye’s Application Specialist

By Charlotte Plestant (Scientific Content Marketing Manager), Eunice Stennett (Former CMO), David Guet (Digital pathology Specialist) - 26 May 2021

Over the last years, the world of Pathology has considerably evolved and brought out new challenges. Development of whole-slide scanners alongside Artificial Intelligence (AI) has empowered pathological analysis by digitizing immunohistochemistry, immunocytochemistry and H&E slides, providing better possibilities in patient selection and treatment. These changes have come with new hurdles, related to a sharp increase of the complexity of the images, higher expectations for image analysis and a shift in the pathologist's daily practices.

In this post we caught up with David Guet, Digital Pathology Application Specialist at Keen Eye. We asked him what his vision was with regards to Computational Pathology and why Artificial Intelligence driven solutions could support every expert in the field of Pathology.


The slide digitization opened the path to Computational Pathology 


Two decades ago, hospitals, pharmaceutical industries and CROs (Contract Research Organizations) started their own journey on the path of innovation. Thanks to slide scanners, they moved from the slide review with a microscope to whole-slide imaging, introducing Digital Pathology. “We can break down Digital Pathology into two major steps”, explains David, “the first one is the process of digitization of histopathology, IHC or cytology slides using whole slide scanners. It is then followed by the management, the analysis and the interpretation of these whole-slide images (WSIs) using computational approaches”.


Computational Pathology incorporates multiple sources of data through AI developed tools (or mathematical models), to generate inferences. It requires managing biological and clinical information from large and high-throughput data sets. Computational Pathology relying on Deep Learning tools gives access to better patient selection and drug treatment therapies.



AI-driven solutions have become mandatory to face the increasing complexity of image analysis


Originally in research, pre-clinical, clinical and diagnostic areas, analysis of images to support scientific experts was not systematically required. As the data to process became more and more complex, AI-automated image analysis has turned out to be vital for robust interpretations and to extract relevant information, ranging from biomarkers to anatomo-pathological features within each image. 


This inevitably has had an impact on the workflow of the different experts. David emphasizes that “there were more slides to screen, more biomarkers to analyze, and within shorter time frames”. This increase in the number of digital slides requiring review from the pathologists could also lead to an increase in the variability of the results, inter and intra-operator. 


Alongside these parameters, “we have to take into account a shortage in experienced pathologists, while the network between companies and labs working together all over the world is getting more and more complex”, adds David. WSIs are heavy data to handle: it is crucial to be able to access and to share these images through a responsive and scalable viewer. Being able to answer such needs in all the collaborative research approaches is a cornerstone in Computational Pathology.



Positive impact of AI-driven image analysis on the work of Digital Pathology experts


Pathologists and researchers are facing an increase in their workload. It is crucial  to create the right analysis solutions that will provide them with the relevant information on a study, allowing them to cope with the high volumes of data:  around 75% of pathologists throughout  59 countries worldwide have declared to be interested in using AI as a diagnostic tool¹.


AI-driven algorithms have to be designed under pathologist guidance and validation, in order to accompany and help them all along their work. Our Application Specialist highlights that “these solutions are made in collaboration with pathologists, for pathologists”. Indeed, with proper training and development, an AI image analysis can bring outstanding outcomes, thereby supporting the work of these experts. Moreover, the use of batch processing with a validated algorithm will allow faster and continuous analysis, with a quality assurance control and a reduced number of errors.


Having a reliable and productive AI-driven tool frees more time for researchers and pathologists to focus on the essential and make breakthrough discoveries.

Meet our Digital Pathology Application Specialist


David Guet has a PhD in Biological and Physical Sciences from Institut Curie, a world class cancer center.He has always been passionate about images, capturing what  cells and  tissues had to reveal. As a biologist, he particularly enjoys all the multidisciplinary collaborations he has set up with data scientists and physics labs. “My PhD gave me the opportunity to start my transdisciplinary journey, where biology meets microscopy and image processing! This has allowed me to develop a strong expertise in image acquisition and analysis”, declares David. To the question about what a typical day of a Digital Pathology Application Specialist (DPAS) looks like, he answers “there is no typical day and that’s what I love! A DPAS is taking part in the development and improvement of the features of the product, is always in relationship with the user's expectations”. “He has a key role, he can translate customer requirements into technical specifications”, explains Shazia Osman, Head of Business Development. “Thanks to his technical and scientific skills, he can be proactive and is a real asset”.




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More information about Deep Learning developed at Keen Eye: check here, and about our scientific articles: check here




¹ Physician perspectives on integration of artificial intelligence into diagnostic pathology
Shihab Sarwar, Anglin Dent, Kevin Faust, Maxime Richer, Ugljesa Djuric, Randy Van Ommeren & Phedias Diamandis
npj Digital Medicine volume 2, Article number: 28 (2019)