Initial validation of mobile Whole Slide Imaging (mWSI) with Northwell Health
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 and our
dedicated team at NYU Digital Future Labs.
The methods for creating mWSIs was first published in the British Medical Journal. Then we started our first partnership
with Northwell Health’s pathology department led by Dr. Michael Esposito. Dr. Esposito helped us create our first
validation study. 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 and the images 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 establishing 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 hi-tech daignosis, which is where AiDA 2 fits in.
They have chosen the AiDA microscope as the tool to provide tele-pathology, helping them become the leaders in
this collaborative approach.
Cervical cancer pre-screening on mobile devices with Qualcomm India
Roughly 1 in 5 Pap smears screenings will be abnormal, but for the best efficiency every woman between the ages of 21-65
recommended to be screened every three years. In low resource areas these screening programs can be 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
Our team innovated a workflow to automate the prescreening and increase throughput of smears called AutoPap.
Developed with the engineers at Qualcomm’s Innovation Lab, AutoPap is a CNN capable of taking images captured by the
and AiDA eye systems as inputs and determining with 98% accuracy if there are abnormal cells present.
AutoPap was designed using Tensor-Flow which is an interface for expressing machine learning algorithms as well as an
implementation for executing such algorithms. 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.
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
Catalina Maya Rendon
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 - they depended on loading the slides
into cartridges first which were then fed into the scanners. Those designs just didn’t hold water, literally! With AiDA
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
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 preclassification algorithm in order to verify that illuminance, contrast and blurriness met the
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 testing of livestock stool samples. In many regions of sub-Saharan Africa where we are
conducting our first field trials. 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
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 is confidential, and all information contained on the heat map is anonymous.