Abstract
Breast Cancer is currently the most commonly diagnosed type of cancer worldwide. While the impacts of this disease can be mitigated through early diagnosis, generalized screening programs based on full-field digital mammography present several limitations regarding lesion obscurity and false positive diagnosis. Given that, women could benefit from a risk analysis for the development of BC that would allow their healthcare professionals to adapt screening in a personalized fashion, not only in terms of frequency but also regarding the imaging modality used. This study aims to develop a medical application, based on Artificial Intelligence (AI), that receives images from different modalities as input and outputs a personalized risk prediction for BC development. The final goal is to have an AI model that allocates each analyzed case to a risk group (1/2-year risk, 3/4-year risk, 5/more-year risk) based on characteristics present in the medical images. A solution like the one proposed would allow not only the previously mentioned screening adaptation but also some preventive measures taken both by the healthcare professional and by the patient. Finally, the development of a computerized medical application allows its use in any type of medical facility, despite the socio-economical characteristics of the patients.
Published Version
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