Detects malignant skin growth without a biopsy
Softeq developed firmware components for the device and helped the client test the viability of their idea.
Softeq participated in the development of a complex Wi-Fi-enabled system equipped with powerful functionality.
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 a 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 the vascular refilling rate and proceeds with a custom algorithmic data analysis complete with a plain report output on the UI.
The resulting solution was expected to participate in rigorous clinical testing across US dermatological institutions. The system showed the capability of detecting malignant skin growth without extra biopsies or examinations. The device was meant to cut healthcare-associated costs by providing a low-cost and prompt cancer diagnostics method.