Esplora il futuro del Digital Healthcare: intelligenza artificiale, big data e tecnologie innovative per la salute e il benessere.
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.
To evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm, previously trained using both adult and pediatric patients, for the detection of acute appendicular fractures in the pediatric population on conventional X-ray radiography (CXR).
Most real-world systems are characterised by dynamics and correlations emerging at multiple time scales, and are therefore referred to as complex systems. In this work, the complexity of time series produced by complex systems was investigated in the frame of information theory computing the entropy rate via the conditional entropy (CE) measure.
Survival of pediatric patients with brain tumor has increased over the past 20 years, and increasing evidence of iatrogenic toxicities has been reported.
Considering the large number of patients with pulmonary symptoms admitted to the emergency department daily, it is essential to diagnose them correctly.
SenticLab ha vinto l'hackathon di MEDIMAG-IA con un sistema di intelligenza artificiale per riconoscere metastasi ossee e calcolarne l'attività metabolica.
Parkinson’s disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD.
SenticLab è nella Top 5 nella competizione KiTS 2023, avendo sviluppato un algoritmo per la segmentazione di immagini dei reni, per identificare cisti e tumori.