Skinopathy AI

Skinopathy AI is a suite of machine learning pipelines that perform preliminary assessments for a variety of skin conditions and adds Artificial Intelligence workflow to the Skinopathy OS platform.

About Skinopathy AI™

The pandemic has placed an incredible burden on the healthcare system, and, unfortunately, skin cancer screenings have plummeted resulting in an alarming trend where initial late-stage skin cancer diagnoses are increasing at a rapid pace.

Skinopathy AI™ technology was created so that people who fear the threat of skin disease or skin cancer could get a preliminary analysis as to the urgency of their lesions, without the need to wait months for an appointment or risk close contact with someone with Covid-19.

Since the technology is geographically agnostic, we are also able to deploy it to under-serviced regions and provide people with unparalleled access to healthcare.

Re-Captcha for Skin

What we have created is a Convolutional Neural Network (CNN)-based technology geared for skin abnormalities.

CNN’s are mostly used for analyzing images. The most famous application being the “Re-Captcha” security feature found on many websites, which is a process that trains such CNNs.

However, instead of using our AI to determine the difference between a fire hydrant and a bus, we are using this technology to determine the difference between a mole and a cancerous lesion.

Skinopathy AI™ is a light-load AI that features an integrated feedback loop process that continuously refines our precision and accuracy.

AI Training

We began training our AI using the HAM10000 database, a large and thoroughly curated open-source database of skin abnormality images taken using dermatoscopes.

However, since our technology is geared for the public and not trained physicians with dermatoscopes, we created a secondary database that more accurately reflected the type of real-world images patients are likely to submit.

The substantial variance in both focus and field of view allowed us to do real-world testing of our model and compare those results with the pristine HAM10000 database.

Eventually, Skinopathy AI™ will be trained for a variety of skin concerns that include burns, wounds, and other ailments.


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