Francesco Cipolletta, Ruggiero Bollino, Domenico Parmeggiani, Michela Gravina, Stefano Marrone, Carlo Sansone, Ludovico Docimo, Giampaolo Bovenzi, Springer, Bollino, R., Bovenzi, G., Cipolletta, F., Docimo, L., Gravina, M., Marrone, S., Parmeggiani, D., Sansone, C., Chee-Peng Lim, Ashlesha Vaidya, Kiran Jain, Virag U. Mahorkar, Lakhmi C. Jain, Bollino, Ruggiero, Bovenzi, Giampaolo, Cipolletta, Francesco, Docimo, Ludovico, Gravina, Michela, Marrone, Stefano, Parmeggiani, Domenico, and Sansone, Carlo
In recent years the constant development of diagnostic techniques has contributed to improving the prognosis of many diseases. Among all, oncological diseases remain those in which a correct and early diagnosis can not only significantly improve the patient’s quality of life but also impact the effectiveness of the therapy itself. Artificial Intelligence can provide valuable aid to this need through the development of predictive models to support the physicians in the diagnosis of the disease. The project “Synergy-Net: Research and Digital Solutions in the Fight Against Oncological Diseases”, born from the collaboration between the Department of Medical and Advanced Surgical Sciences of the University of Campania “L. Vanvitelli”, the National Informatics Inter-University Consortium (National Informatics Inter-University Consortium), Lab ITEM “C. Savy” and Bollino IT S.p.A., aims at the realisation of a technological platform to support the early oncological diagnosis based on the integration of an interoperable communication and clinical data management system leveraging AI. The project has a deeply interdisciplinary nature (lung cancer, breast cancer, colorectal cancer, gastrointestinal carcinomas, prostate cancer, thyroid cancer and malignant skin tumours), which requires the collaboration of very different professionals, including general practitioners, specialist doctors, radiologists, surgeons, pathologists, molecular biologists and oncologists, as well as the support of a team of researchers for aspects related to machine learning and expert system development in health care. The core of the project consists in the creation of a Computer-Aided Detection/Diagnosis (Computer-Aided Detection/Diagnosis) system that, based on Machine Learning and Deep Learning techniques, assists the operator in the analysis of screening data such as anamnestic information, blood tests, instrumental and diagnostic images. The assistance to the operator is achieved by suggesting the portions of information (e.g. regions in an X-ray image) on which to focus more attention. The use of the system will help the physician in the development of increasingly personalised diagnostic and therapeutic strategies, meeting the criteria of tailored therapy/surgery, a desirable objective of any cancer prevention program.