How Cognition’s AI platform can help identify nail diseases and healthy nails
The dermatology department of a leading hospital wanted a tool that was able to differentiate between healthy and unhealthy nails to automatically and quickly assess whether a patient needed to make an appointment with the dermatologist. The tool also needed to identify three nail diseases: melanonychia, dystrophy and onychomycosis.
Cognition’s computer vision platform, Sigma Vision, can recognize patterns with extremely high accuracy, making it ideal for this use case. The dermatology clinic had a large database of annotated nail images, healthy and unhealthy, giving Cognition the data necessary to develop a solution customized to the task: detecting and identifying nail diseases quickly and easily.
Cognition’s team preprocessed the images to reduce variability and optimize the result of the machine learning process, fine tuning the technology parameters for this type of images. They also developed a user interface to visualize the results in a way that the dermatologist could rapidly confirm the diagnosis of the disease.
The resulting solution was able to identify healthy nails with an accuracy of 95%, and to detect and identify the three nail diseases with the following accuracies: