Esplora il futuro del Digital Healthcare: intelligenza artificiale, big data e tecnologie innovative per la salute e il benessere.
Central vision loss (CVL) is an increasingly prevalent condition that significantly affects essential daily activities such as reading and visual search. This study simulates a central scotoma in a controlled experimental setting using the Microsoft HoloLens 2 augmented reality (AR) headset, enabling the…
L'automazione dell'analisi cefalometrica avrebbe un enorme potenziale per migliorare il flusso di lavoro del trattamento ortodontico, supportando la diagnosi e il processo decisionale.
This study aimed to develop and evaluate an artificial intelligence framework for detecting dental restorations and prosthesis devices on panoramic radiographs. Detecting these elements is essential for enhancing automated reporting, improving the accuracy of dental assessments, and reducing manual examination time.
Questo studio ha esaminato la fattibilità dell'utilizzo di un sistema basato sull'intelligenza artificiale (IA) per distinguere tra i diversi stadi ed eziologie della demenza attraverso l'analisi delle emozioni facciali.
A video stabilization pipeline for oral capillaroscopy imaging. The approach offers reliable, efficient stabilization, enhancing the quality of oral videocapillaroscopy.
Nel deficit cognitivo si osserva un'alterazione dell'espressività, proponiamo un approccio basato sull'analisi delle emozioni facciali.
Valutare le prestazioni dei chatbot basati su modelli di linguaggio di grandi dimensioni in scenari realistici di pronto soccorso pediatrico.
The paper takes a practical approach to estimating the bone age of children and young adults based on hand radiography, by constructing an end-to-end pipeline that involves bone segmentation, deep regression algorithms and model explainability.
This paper presents a complete pipeline for automatic diagnosis of various liver masses, consisting of components based on deep learning models for both the automatic segmentation of liver and its lesions, as well as the classification of masses into 5 classes, namely simple cysts, hemangiomas, hepatocellular carcinoma, calcifications and metastases.
This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence.
