New AI Pathology Microscopy Study with Augmentiqs Published in the Journal of Toxicologic Pathology
Authored by pathology AI developer AIRA Matrix, researchers from the Hebrew University of Jerusalem and toxicologic pathologist Dr. Abraham Nyska, the first of its kind study compared the manual semi-quantitative microscope-based assessment with the quantitative output of AIRA Matrix’s deep learning algorithm within the microscope of Dr. Nyska.
Accepted and published in the June 2020 publication of the Journal of Toxicologic Pathology, the non-sponsored and peer-reviewed study explores the use of AI algorithms for the quantification of fatty liver in mice, while maintaining the microscope-based pathology workflow.
The First AI Pathology Microscopy Study
To the best of the author’s knowledge, this is the first study to compare the quantification results of an AI application for fatty vacuole accumulation and the semi-quantitative evaluation performed by a board-certified toxicologic pathologist using a fully microscope-based approach.
- Abraham Nyska, DVM,
- Yuval Ramot, PhD,
- Gil Zandani, PhD,
- Zecharia Mader, BsC,
- Sanket Deshmukh,
Quantification of fatty vacuoles in the liver, with differentiation from lumina of liver blood vessels and bile ducts, is an example where the traditional semiquantitative pathology assessment can be enhanced with artificial intelligence (AI) algorithms. Using glass slides of mice liver as a model for nonalcoholic fatty liver disease, a deep learning AI algorithm was developed. This algorithm uses a segmentation framework for vacuole quantification and can be deployed to analyze live histopathology fields during the microscope-based pathology assessment. We compared the manual semiquantitative microscope-based assessment with the quantitative output of the deep learning algorithm. The deep learning algorithm was able to recognize and quantify the percent of fatty vacuoles, exhibiting a strong and significant correlation (r ¼ 0.87, P < .001) between the semiquantitative and quantitative assessment methods. The use of deep learning algorithms for difficult quantifications within the microscope-based pathology assessment can help improve outputs of toxicologic pathology workflows.
The results using the AI application developed by AIRA Matrix within the microscope with Augmentiqs are in strong agreement with a recent study that used a different image analysis software to quantify liver steatosis in a mouse model. Together, there is good evidence to support the use of AI for assessing the different components of NAFLD and NASH.
AI Pathology Microscopy Study – Utilizing Augmentiqs for Microscope-Based Digital Pathology
“The application and utility of microscope-based AI for pathology has been further proven with this pivotal study. The ability to integrate 3rd party AI algorithms by AIRA Matrix clearly demonstrates how pathologists across the globe can create their own pathology algorithms for use within their existing microscope, ” said Gabe Siegel, Augmentiqs CEO.
The ground-breaking study compared Augmentiqs as a platform for microscope-based AI & digital pathology, versus the semi-quantitative eyeball approach for hard to quantify fatty liver.
Augmentiqs is a microscope-centric approach to digital pathology, providing pathologists a cost-efficient and low-data method for realizing clinical and workflow enhancements. By connecting the analog microscope to the computer, Augmentiqs maintains the advantages of the microscope for workflow and primary diagnosis, while improving efficiency with the introduction of pathology software applications directly from the microscope.
By functioning as a platform for real-time software deployment within the microscope, Augmentiqs allows pathologists immediate access to imaging, analytical software, telepathology, LIMS integration and other digital pathology applications.
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