Explore the future of Digital Healthcare: artificial intelligence, big data, and innovative technologies for health and well-being.
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…
We propose a camera-based system to provide an objective, contactless assessment of pain in children aged less than 3 years.
Automating cephalometric analysis would have huge potential for enhance orthodontic treatment workflow, supporting diagnosis and decision.
Objectives This study aimed to develop and evaluate an artificial intelligence (AI) framework for detecting dental restorations and prosthesis devices on panoramic radiographs (PRs). Detecting these elements is essential for enhancing automated reporting, improving the accuracy of dental assessments, and reducing manual examination…
Early and differential diagnosis of dementia is essential for timely and targeted care. This study investigated the feasibility of using an artificial intelligence (AI)-based system to discriminate between different stages and etiologies of dementia by analyzing facial emotions. We collected video recordings of…
A video stabilization pipeline for oral capillaroscopy imaging. The approach offers reliable, efficient stabilization, enhancing the quality of oral videocapillaroscopy.
Altered facial expressivity is frequently recognized in cognitively impaired individuals, we proposea study based on facial emotion analysis.
We aimed to assess LLM-based chatbots performance in realistic PED scenarios, and to explore their use as diagnosis-making assistants in pediatric emergency.
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.
