Our ViewMD app enables users to capture image, Each image that is captured will be securely stored in the cloud, making use of all the best security standards and practices that comes along with AWS cloud.

Images can be live streamed to remote devices for instant sharing and collaboration.

Images are organized by the field of pathology, patients and sub cases, this way users can easily retrieve images, anytime, anywhere, whether its from a pc , laptop or mobile device.

Users can also choose to make use of our overlays for measuring in microns(μm), zoom in on areas of interest and alter colors and brightness as needed.


Each annotation project can be setup with its own set of images captured in the AppSuite. All on request 3rd party images can also be uploaded.

Once a project is setup one or more users can be assigned to add annotation information to the images. Users can simply add an annotation onto an image by click on the image, annotations can also be removed in a similar way by right clicking the image.

Once an annotation is added to the image, users can then go ahead and classify the annotation by selecting from the pre configured list of annotation labels created in the project setup step.

Multiple users can annotate the same image and results can later be layered on top of each other for comparison purposes. Annotated images can be used for research, train machine learning models as well as student education.


We currently have analysis modules in the following areas:

Sperm head morphology

By taking exact measurements of the head of the sperm as well as measuring the size of the acrosomal region, we can accurately identify whether a sperm head and the acrosomal region is normal or abnormal

Skin normal/abnormal detection

This module scans whole slide images of skin, breaking it up into smaller sections and analyses each section for abnormalities. All the results gets aggregated into an overall score of normal vs abnormal. This can be used to prioritize workload in terms of what needs to be looked at first.

Malaria detection

By analyzing blood slides we can detect the malaria parasite, the AI can further diagnose the stage of life of the parasite as well as the percentage of infestation. This will allow for accurate diagnosis and thus treatment of the infection.