iToBoS has been designed to fully address all of the challenges in the scope of the European H2020 call SC1-BHC-06-2020: “Digital diagnostics – developing tools for supporting clinical decisions by integrating various diagnostic data”.
The iToBoS project is based on the development and validation of an integrative diagnostic platform for early detection of melanoma. This platform consists of a full-body-skin-imaging acquisition system with dermatoscopic image quality, which is the most advanced technique to detect skin cancer, and the advanced analysis of those images in combination with other relevant patient data, such as clinical history and genomics, will lead to a highly personalized diagnostic of the patient condition.
iToBoS aims at increasing the precision and clinical decisions in diagnosis of skin cancer in several ways.
First, the use of the proposed automatic acquisition system will permit the screening of the whole skin surface in about 6 minutes and in a standardized way. This ensures that the whole patient’s body is captured, reducing the risk of missing important information of the patient’s condition that may be relevant for the diagnostic, in opposition to the current gold-standard technique, which consists in the recording of the body areas that the dermatologist considers most relevant, leaving most of the patient's skin surface without a visual record of his/her condition in the moment of exploration. This is especially relevant in the emergence of new naevi, since the state-of-the-art technique does not provide records of clinically normal skin with no associated naevus. Given the fact that most melanomas arise de novo, with iToBoS, they will be detected and compared to the previous exploration, raising a warning at an early stage, and hence contributing to improved diagnosis and clinical decisions.
Second, the integration of various data sources, including medical records and genomics data with the current state-of-the-art diagnostic technique, i.e. in vivo dermoscopy images which iToBoS will acquire in a standardized way with the proposed full-body-image scanner, will lead to more informed decisions and consequently, more encompassing diagnosis.
Finally, the use of state-of-the-art advanced analytical tools including Deep Learning and other Artificial Intelligence solutions, to integrate the gold-standard-procedure data with various other data obtained from different sources, will improve the accuracy of current diagnostic support systems, leading to better clinical decisions.