Are you looking to:
  • build a telepathology network
  • receive reliable cytology consultation
  • deploy AI in a digital pathology setting

Initial validation of mobile Whole Slide Imaging (mWSI) with Northwell Health
Dr. Esposito
Dr. Bhuiya

Mobile Whole Slide Imaging is our proprietary method of generating digital whole slides. The process was created by Lou Auguste, co-founder of Alexapath, and automated through the work of Shishir Malav, co-founder of Alexapath, along with our dedicated team of engineers and developers at NYU Digital Future Labs.

The methods for creating mWSIs was first published in the British Medical Journal (BMJ Innovations, 2015). Following the publication, we conducted our first clinical validation study with the staff of Northwell Health’s pathology department led by Dr. Michael Esposito. The goal of the study was to determine if images created by the AiDA device were adequate for diagnostic interpretation by the pathology team.

The results were presented as a poster at USCAP 2015. Images created through the mWSI technique were recognized as valid for diagnosis.
Using AiDA 2 to build a network for diagnosis of childhood cancers in Tanzania
Dr. Trisch Scanlan (Tumaini La Maisha)
Dr. Anna Schuh (Dept. of Oncology, Oxford University)

Tanzania, the most populated country in East Africa, is composed of a wide distribution of settlements making access to quality healthcare challenging. Like many low-income areas of the world, cure rates for non-communicable diseases like childhood cancers were close to 5% when the team from Tumaini La Maisha (TLM) started working in Tanzania almost 20 years ago. Their goal is to make sure that all children living in Tanzania who develop cancer are diagnosed in a timely fashion and have appropriate cancer care.

To achieve that goal, TLM is in the process of providing a centrally coordinated pediatric oncology care to other regions of the country. To build this national paediatric oncology network of 6 hospitals all using standard protocols with centralized access to specialist services- including timely and reliable diagnosis, which is where AiDA 2 fits in. They have chosen the AiDA microscope as the tool to provide telepathology, helping them become the leaders in this collaborative approach.
Cervical cancer pre-screening on mobile devices with Qualcomm India
Qualcomm (India)

Roughly 1 in 5 Pap smears screenings will be abnormal, but for every woman between the ages of 21-65, screening is recommended once every three years. In low resource areas, these screening programs are the difference between life and death, but to the lab professionals, it can mean hours of routine work viewing cases that are negative. When coupled with staffing shortages of diagnostic professionals, mistakes can be made and abnormal smears can be missed.

Our team innovated a workflow to automate the prescreening and increase the throughput of smears called AutoPap. Developed with the support of engineers from Qualcomm’s Innovation Lab, AutoPap is a CNN capable of determining with 98% accuracy if there are abnormal cells present in the digital whole slides created by AiDA.

AutoPap was designed using Tensor-Flow which is an interface for expressing machine learning algorithms and deploy the model on devices. The CNN needed a GPU heavy system for training, but we were able to successfully deploy a lightweight version of the trained algorithm on a mobile system using the SnapDragon 820.

The CNN and the dataset containing all 120,000 images now open source, and we are using it to teach high school students how to develop AI. Currently, we are looking for partners with funding to test AutoPap in the field.
Detecting water parasites using HEAD AI with UNAM
Prof. Catalina Maya Rendon
Gustavo Velasquez, MSc

Researchers from UNAM were looking for a way to create whole slide images of rafters (chambered slides for holding water specimens). All of our competitor whole slide scanners had a design flaw - slides had to be loaded into cartridges before they were images. Those designs just didn’t hold water! In AiDA 2 they discovered a scalable solution.

We’ve heard this story before, guidelines exist to limit the density of Helminth eggs in various environmental matrices, but there are not enough qualified technicians for the task of visual identification using conventional microscopy across all regions in need.

With support from the Bill and Melinda Gates Foundation, we donated an AiDA 2 system to the team at UNAM. AiDA was used to acquire mobile Whole Slide Images (mWSIs) of various concentrations of Helminth ova. The images were then analyzed by a pre-classification algorithm in order to verify that illuminance, contrast, and blurriness met the established threshold values. The testing validated that the AiDA system produced digital images that met the benchmarks needed for successful analysis by AI.
Expanding Access to parasite testing for farm animals with vHive and Zoetis
Dr. Abel Ekiri (vHive Surrey)

Building on previous research with Qualcomm and UNAM. Our team in partnership with vHive Surrey is developing an automated solution for the screening of stool samples. In many regions of sub-Saharan Africa, farmers share grazing lands for their cattle. When a herd with parasites grazes on a common green, they can put all the neighbors' livestock at risk.

Facing a similar lack of diagnostic professionals we have identified as a key challenge facing many of our customers. Together, vHive and Alexapath are set to trial a version of the HEAD algorithm capable of identifying parasite ova present in stool samples. To speed the uptake of this automated diagnostic, we are employing a team of drivers to travel from farm to farm to collect specimens. The farms have access to a mobile application that shows of heat map of grazing areas that parasites may be present at. The results of the testing are confidential and all information contained on the heat map is anonymous.