A5055126952- Radiomics and Machine Learning in Medical Imaging
- Glioma Diagnosis and Treatment
- Brain Tumor Detection and Classification
- Machine Learning in Healthcare
- COVID-19 diagnosis using AI
- Pancreatic and Hepatic Oncology Research
- Artificial Intelligence in Healthcare and Education
- Attention Deficit Hyperactivity Disorder
- Genetics and Neurodevelopmental Disorders
- Autism Spectrum Disorder Research
- Advanced X-ray and CT Imaging
- Dementia and Cognitive Impairment Research
- Health, Environment, Cognitive Aging
- Diabetes Treatment and Management
- Medical Imaging Techniques and Applications
- Photoacoustic and Ultrasonic Imaging
- Neurological Disease Mechanisms and Treatments
- Advanced Neuroimaging Techniques and Applications
- AI in cancer detection
- Bone and Joint Diseases
- Autopsy Techniques and Outcomes
- Osteomyelitis and Bone Disorders Research
- Orthopedic Infections and Treatments
- Global Cancer Incidence and Screening
- MRI in cancer diagnosis
Bambino Gesù Children's Hospital
2021-2024
Istituti di Ricovero e Cura a Carattere Scientifico
2021-2024
Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, GBM-specific model is still lacking the literature. Our aim was to develop GBM-tailored deep-learning prediction by applying convoluted neural networks (CNN) multiparametric MRI. We selected 100...
Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). However, lack of parameter standardization limits applications. Many machine learning (ML) radiomic employ single classifiers rather than ensemble learning, which is known to boost performance, and comparative analyses are lacking the literature. We aimed compare ML predict clinically relevant tasks HGG: overall survival (OS), isocitrate dehydrogenase (IDH) mutation,...
Biomarker-based differential diagnosis of the most common forms dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim study was develop and interpret a ML algorithm capable differentiating Alzheimer's dementia, frontotemporal with Lewy bodies cognitively normal control subjects based on sociodemographic, clinical, magnetic resonance imaging (MRI) variables. 506 from 5 databases were included. MRI images processed FreeSurfer, LPA,...
Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be to better allocate resources the midst of pandemic. Our aim was build deep-learning (DL) model COVID-19 outcome prediction inclusive 3D chest images acquired at hospital admission. This retrospective multicentric study included 1051 (mean age 69, SD = 15) who presented emergency department three different institutions between 20th March 2020...
Objective: The purpose of this study is to analyze the texture characteristics chronic non-bacterial osteomyelitis (CNO) bone lesions, identified as areas altered signal intensity on short tau inversion recovery (STIR) sequences, and distinguish them from marrow growth-related changes through Machine Learning (ML) Deep (DL) analysis. Materials methods: We included a group 66 patients with confirmed diagnosis CNO 28 suspected extra-skeletal systemic disease. All examinations were performed...
<sec> <title>BACKGROUND</title> Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). Many machine learning (ML) radiomic have been developed, mostly employing single classifiers with variable results. However, comparative analyses of different ML clinically-relevant tasks are lacking the literature. </sec> <title>OBJECTIVE</title> We aimed to compare well-established classifiers, including and ensemble learners, predict HGG: overall survival (OS),...
Background: Distinction of IDH mutant and wildtype GBMs is challenging on MRI, since conventional imaging shows considerable overlap. While few studies employed deep-learning in a mixed low/high grade glioma population, GBM-specific model still lacking the literature. Our objective was to develop for prediction GBM by using Convoluted Neural Networks (CNN) multiparametric MRI. Methods: We included 100 adult patients with pathologically proven testing. MRI data included: morphologic...
Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM). However, implementation is limited by lack of parameters standardization. We aimed compare nine machine learning classifiers, with different optimization parameters, predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression GBM...
Population Medicine considers the following types of articles:• Research Papers -reports data from original research or secondary dataset analyses.• Review -comprehensive, authoritative, reviews within journal's scope.These include both systematic and narrative reviews.• Short Reports -brief reports research.• Policy Case Studies articles on policy development at a regional national level.• Study Protocols -articles describing protocol study.• Methodology -papers that present different...