The engineering tasks of the iToBoS project can be divided into two main groups. On the one hand, the design and construction of the total body scanner, that is the hardware. On the other hand, the design and implementation of the data processing and artificial intelligence software that will analyse all the information (skin images and clinical data) and generate a comprehensive output for the clinicians.
The scientific work "Registration of polarimetric images for in vivo skin diagnostics", with the support of the iToBoS project, has been published.
The high performance of machine learning algorithms for the task of skin lesion classification has been shown over the past few years. However, real-world implementations are still scarce.
In the last two years, the scarcity of semiconductors has become a global problem. The shortage of microchips is a consequence of the restrictions imposed by the pandemic situation and the closure of some factories and companies. This situation has affected many sectors, demonstrating the importance of these components nowadays.
As the use of AI becomes increasingly pervasive in business, machine learning models are one of the ways industries can best make the most of existing data to improve business outcomes.
The total body scan intends to image the full body of the patient with high-resolution images. However, the human body is not a flat surface, so we will need to take many pictures of the same region, each one focused at a different distance, and compute a super-resolved image combining the regions resolved in each image.