UniCas for Medicine and Healthcare

With over twenty years of experience, our research group, affiliated with the Artificial Intelligence and Data Analysis Laboratory (AIDA), which belongs to the University of Cassino and Southern Lazio (UniCas), has been deeply engaged in artificial intelligence. The specific focus on Machine Learning, Pattern Recognition, and Deep Learning has evolved theoretically, with the development of specialized skills in model optimization, and practically, through application to real-world problems, particularly in the healthcare domain. In particular, attention is paid to designing and implementing Computer-Aided Diagnosis systems to support the prevention, diagnosis, and monitoring of Neurodegenerative diseases, Specific Learning Disorders, breast cancer, diabetic retinopathy, and movement-related disorders. Different data is utilized to reach the objectives of the AIDA Lab, and many approaches are implemented. Handwriting analysis is exploited to support the diagnosis of neurodegenerative diseases and specific learning disorders and to monitor them over time. Handwriting analysis encompasses two distinct approaches: examining dynamic features and scrutinizing handwriting sample images. This comprehensive approach allows a more thorough understanding of the individual’s writing characteristics. Additionally, in Neurodegenerative Diseases, advancements include the utilization of 3D image analysis of MRI scans to aid in the detection of Alzheimer’s disease, further enhancing diagnostic capabilities in this field. Mammograms are used for breast cancer prevention and diagnosis, while retinal images are used for diabetic retinopathy detection, particularly focusing on detecting small lesions. Detecting small lesions is a crucial step in diagnosis, as is identifying microcalcifications in digital mammograms and microaneurysms in digital fundus images. To address this challenge, we propose a novel architecture called GravityNet. Another field of study in the research conducted by the AIDA Lab is movement analysis, focusing on gait analysis, enabling precise evaluations in real-world settings and potential applications in Parkinson’s disease assessment. To this end, Machine and advanced Deep Learning techniques are employed, such as deep cascades of boosting classifiers and Deep Convolutional and Attentional Neural Networks.