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Catalogo Ricerca: [82093] Home page macrotipologia

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1 Contributo su Rivista 42456
- 1.1 Articolo in rivista 40025
- 1.2 Recensione in rivista 1364
- 1.3 Scheda bibliografica 62
- 1.4 Nota a sentenza 170
- 1.5 Abstract in rivista 760
- 1.6 Traduzione in rivista 75

2 Contributo in Volume 13513
- 2.1 Contributo in volume (Capitolo o Saggio) 11661
- 2.2 Prefazione/Postfazione 600
- 2.3 Breve introduzione 194
- 2.4 Voce (in dizionario o enciclopedia) 586
- 2.5 Traduzione in volume 204
- 2.6 Recensione in volume 40
- 2.7 Schede di catalogo 228

3 Libro 2693
- 3.1 Monografia o trattato scientifico 2386
- 3.2 Concordanza 4
- 3.3 Indice 10
- 3.4 Bibliografia 3
- 3.5 Edizione critica 65
- 3.6 Pubblicazione di fonti inedite 11
- 3.7 Commento scientifico 21
- 3.8 Traduzione di libro 193

4 Contributo in Atti di Convegno (Proceeding) 19774
- 4.1 Contributo in Atti di convegno 16619
- 4.2 Abstract in Atti di convegno 2708
- 4.3 Poster 447

5 Altro 1855
- 5.01 Composizione 0
- 5.02 Disegno 2
- 5.03 Design 0
- 5.04 Performance 51
- 5.05 Esposizione 5
- 5.06 Mostra 72
- 5.07 Manufatto 3
- 5.08 Prototipo d'arte e relativi progetti 1
- 5.09 Cartografia 11
- 5.10 Banca dati 53
- 5.11 Software 20
- 5.12 Altro 1635
- 5.13 Progetto architettonico 0
- 5.14 Audiovisivi/Multimediali 2

6 Brevetti 270
- 6.1 Brevetto 270

7 Curatele 1535
- 7.1 Curatela 1535

Prodotti recenti

OC.03.10 AUTOMATED CARDIOVASCULAR RISK PREDICTION IN MASLD PATIENTS USING AI, BY CLINICAL AND CT IMAGING

**Background and Aim:** Accurate assessment of cardiovascular risk is essential for individuals who appear healthy. Metabolic-dysfunction associated steatotic liver disease (MASLD), formerly known as NAFLD, affects 38% of the global population. In patients with MASLD, cardiovascular diseases (CVD) are the leading cause of mortality, suggesting that MASLD should be considered an independent risk factor in developing new cardiovascular risk assessment scores. This study aims to develop a predic...

AI-based estimation of cardiovascular risk in MASLD patients using non-contrast CT imaging and clinical data

{Introduction Accurate cardiovascular risk (CVR) assessment is crucial for apparently healthy individuals. Metabolic-dysfunction associated steatotic liver disease (MASLD), formerly NAFLD, affects 38% of the population worldwide and the cardiovascular diseases (CVD) represent the primary cause of death in these patients, suggesting that MASLD could be considered as an independent risk factor for a novel CVR assessment score. Aim our study aims at modelling non-contrast Cardio-CT scans and cli...

LiverCARD: Opportunistic Cardiovascular Risk assessment in MASLD using a multimodal AI pipeline

Introduction: Metabolic dysfunction–associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), affects approximately 30% of the global population and represents a major public health challenge due to its strong association with cardiovascular disease (CVD), the leading cause of mortality in this group. Accurate cardiovascular risk (CVR) assessment in MASLD remains suboptimal, as traditional risk scores do not incorporate liver-related parameters o...

Monitoring and Scheduling of Semiconductor Failure Analysis Labs

Identifying and locating non-conformities, such as physical failures causing electrical malfunctioning of a device, in modern semiconductor devices is a challenging task. Typically, highly qualified employees in a failure analysis (FA) lab use sophisticated and expensive tools like scanning electron microscopes to identify and locate such non-conformities. Given the increasing complexity of investigated devices and very limited resources, labs may struggle to deliver analysis results in time....

Embedding vs Image-Based AI: A Comparative Fairness Study in Chest X-ray Analysis

AI has shown remarkable potential in healthcare, but faces accessibility challenges due to high computational and expertise demands, especially in medical image analysis. Vector embeddings, compact representations of medical images achieved from foundation models in zero-shot inference, offer a potential solution. Recently, an equivalent vector embeddings dataset of existing large publicly available medical images has been released, for which training an AI model requires significantly lower ...

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