Esplora il futuro del Digital Healthcare: intelligenza artificiale, big data e tecnologie innovative per la salute e il benessere.
Glioma is a type of heterogeneous tumor originating in the brain, characterized by the coexistence of multiple subregions with different phenotypic characteristics, which further determine heterogeneous profiles, likely to respond variably to treatment. Identifying spatial variations of gliomas is necessary for targeted therapy.
SenticLab, partner di ricerca di synbrAIn, ha implementato una soluzione in grado di riconoscere il tipo di tubercolosi a partire da immagini radiografiche.
Grazie alla collaborazione con synbrAIn, Emme Esse ha rilasciato MS HUMANAID, software in grado di sfruttare l’intelligenza artificiale per supportare i medici nella diagnosi di polmonite da COVID-19.
The actual role of landmarks labeling before three-dimensional (3D) facial acquisition is still debated. In this study, several measurements were compared among textured labeled (TL), unlabeled (NL), and untextured (NTL) 3D facial models.
Il team di Senticlab ha sviluppato una nuova soluzione che sfrutta l’intelligenza artificiale per il riconoscimento di noduli tumorali a partire da immagini radiografiche del torace, ottenendo uno dei migliori risultati nella competizione NODE21.
The use of Optical Coherence Tomography (OCT) in acute coronary syndromes (ACS) allows recognizing ruptured fibrous cap (RFC) and intact fibrous cap (IFC) culprit lesions. The biological differences between them, as recently pointed out in translation studies, highlight different mechanisms for a similar clinical manifestation that might deserve different therapeutic approaches. The relationship between endothelial wall shear stress (WSS) and ACS has been demonstrated, however the differences in WSS features between RFC and IFC have not been elucidated.
Despite advances in treatments, 30% to 50% of stage III-IV head and neck squamous cell carcinoma (HNSCC) patients relapse within 2 years after treatment. The Big Data to Decide (BD2Decide) project aimed to build a database for prognostic prediction modeling.
The three-dimensional (3D) representation of the bifurcation anatomy and disease burden is essential for better understanding of the anatomical complexity of bifurcation disease and planning of stenting strategies.
Il progetto AI4NT mira a supportare terapia e diagnosi di pazienti affetti da patologie cognitive, grazie all'intelligenza artificiale.
The paper presents and comparatively analyses several deep learning approaches to automatically detect tuberculosis related lesions in lung CTs, in the context of the ImageClef 2020 Tuberculosis task.