Detects malignant skin growth without a biopsy in 96.7% of suspect cases
Veriskin decided to build a novel device that uses Machine Learning algorithms for non-invasive skin cancer detection. They turned to Softeq to test the viability of their idea through proof of concept (PoC) and develop firmware components powering the device.
The solution is a complex Wi-Fi-enabled system that incorporates:
Equipped with 2 photodetectors and a force sensor, the solution captures LED light signal variations and translates them into a visual diagram in real time.
To diagnose the suspect lesion’s condition, the system monitors vascular refilling rate and proceeds to the custom algorithmic data analysis complete with a plain report output on the UI.
The solution’s highlights include:
Currently, the system is under rigorous clinical testing across the US dermatological institutions. The studies demonstrate that the device proves efficient in determining cancerous lesions. The solution is capable of detecting malignant skin growth in 96.7% of suspect cases with no extra biopsies or examinations. Allowing for a low-cost and prompt cancer diagnostics method, the device is meant to cut healthcare-associated spendings.