Augmentiqs Facilitates the First Microscope Based Study of AR Microscopy for AI Developer via Open API

Real Time ‘Glass Slide’ Analysis of Non-Alcoholic Fatty Liver Disease on Mouse Samples Demonstrates High Concordance of AI Algorithm with Expert Analysis – Opening up Stage for Future Studies of AR Microscopy

Microscope-based digital pathology enabler Augmentiqs congratulates AI Developer AIRA Matrix on the publication of a pivotal study in the Journal of Toxicologic Pathology demonstrating the deployment of novel AI algorithms for the quantitative analysis of Non Alcoholic Fatty Liver Disease. Authored by Abraham Nyska, Yuval Ramot, Gil Zandani, Zecharia Madar, and Sanket Seshmukh, the study is titled Utilization of a deep learning algorithm for microscope based fatty vacuole quantification in a fatty liver model in mice“.

AR Microscopy Study Abstract

Quantification of fatty vacuoles in the liver, with differentiation from lumina of liver blood vessels and bile ducts, is an example where the traditional semi-quantitative pathology assessment can be enhanced with artificial intelligence (AI) algorithms. Using glass slides of mice liver as a model for non-alcoholic 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 semi-quantitative 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 = 0.87, p < 0.001) between the semi-quantitative 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.

Histology section of the perilobular region of the liver from a mouse fed high fat diet with 1% cholesterol and 0.5% cholic acid + 15% broccoli stalks for 7 weeks. The image shows the correlated fatty vacuolation (scored grade 3 by semi-quantitative evaluation) as seen with the fatty liver algorithm. The cytoplasmic fatty vacuoles appear clear with H&E staining, and green highlighted by the artificial intelligence application.

The Timely AR Microscopy Study with AI Developer AIRA Matrix Demonstrates a Cost Effective Approach to Digital Pathology 

“The philosophy of Augmentiqs has always been to offer an open platform through which pathologists will be able to access any digital pathology software. The combination of an AR microscop based workflow with AI an and digital pathology software will bring cost savings and enhance clinical outcomes,” outlines Gabe Siegel, CEO and co-founder of Augmentiqs. “We congratulate AIRA Matrix on their forward-thinking approach to integrating their powerful algorithms within our system and congratulate them on the publication of this pivotal study.”

AR Microscopy with AI – Study Conclusions

The authors finish the AR/AI Microscopy Based Study with the following thoughts:  “To the best of our knowledge, this is the first study to compare between the quantification results of an AI application for fatty vacuole accumulation and the semi-quantitative evaluation performed by a Board-certified toxicologic pathologist utilizing a fully microscope-based approach. This method of deploying digital pathology applications such as AI and deep learning within the existing microscope workflow will likely be studied by other scientists in the future. This is particularly beneficial as the in the field of toxicologic pathology, the use of image analysis in histopathological tissue assessment is expected to continue developing, especially for quantification purposes, to further support the integrity of assessments and diagnoses. We believe that reporting new AI applications and their correlation with microscope-based evaluation will be of great benefit to toxicologist pathologists for future utilization in daily practice.”

About Augmentiqs

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.

For more information about Augmentiqs, please visit or contact

AIRA Matrix

AIRA Matrix provides image analysis and management solutions for preclinical toxicology and pathology applications. The company’s deep-learning based platform helps pathologists analyze large volumes of image data and quickly helps focus on relevant study findings. Their solutions address the more complex workflow processes and image analysis issues faced in pathology reporting.

Connect to learn more