A5078341846- Radiomics and Machine Learning in Medical Imaging
- Artificial Intelligence in Healthcare and Education
- Hip disorders and treatments
- Advanced X-ray and CT Imaging
- Shoulder Injury and Treatment
- Shoulder and Clavicle Injuries
- Orthopaedic implants and arthroplasty
- Bone and Joint Diseases
- Mesenchymal stem cell research
- Orthopedic Surgery and Rehabilitation
- Musculoskeletal synovial abnormalities and treatments
- MRI in cancer diagnosis
- AI in cancer detection
- Orthopedic Infections and Treatments
- Knee injuries and reconstruction techniques
- 3D Printing in Biomedical Research
- Radiology practices and education
- Total Knee Arthroplasty Outcomes
- Tissue Engineering and Regenerative Medicine
- Sports injuries and prevention
- Renal cell carcinoma treatment
- COVID-19 diagnosis using AI
- Lower Extremity Biomechanics and Pathologies
- Radiation Dose and Imaging
- Lung Cancer Diagnosis and Treatment
Foundation for Research and Technology Hellas
2020-2025
University of Crete
2015-2025
Karolinska Institutet
2023-2025
University Hospital of Heraklion
2015-2025
FORTH Institute of Computer Science
2024-2025
Berlin Institute of Health at Charité - Universitätsmedizin Berlin
2024
Centre Hospitalier Universitaire de Reims
2024
Technologies pour la Santé
2024
Hôpital Maison Blanche
2024
Humboldt-Universität zu Berlin
2024
Abstract Purpose To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research of radiomics studies. Methods We conducted an online modified Delphi study with group international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members identify the items be voted; Stage#3, four rounds exercise by panelists determine eligible for METRICS their weights. The...
adiology is one of the most technology-driven medical specialties and has always been closely linked to computer science.In particular, ever since picture archiving communication system (PACS) revolution, there have many examples emerging new technology that shaped reshaped day-to-day practice radiologists. 1 More recently, scientific community witnessed remarkable progress artificial intelligence (AI), advances in image-recognition tasks are likely herald another significant leap forward...
To investigate the intra- and inter-rater reliability of total radiomics quality score (RQS) reproducibility individual RQS items' in a large multireader study.
Abstract This statement has been produced within the European Society of Radiology AI Working Group and identifies key policies EU Act as they pertain to medical imaging. It offers specific recommendations policymakers professional community for effective implementation legislation, addressing potential gaps uncertainties. Key areas include literacy, classification rules high-risk systems, data governance, transparency, human oversight, quality management, deployer obligations, regulatory...
Oxidized alginate hydrogels are appealing alternatives to natural due their favourable biodegradability profiles and capacity self-crosslink with amine containing molecules facilitating functionalization extracellular matrix cues, which enable modulation of stem cell fate, achieve highly viable 3-D cultures, promote growth. Stem metabolism is at the core cellular fate (proliferation, differentiation, death) metabolomics provides global metabolic signatures representative status, being able...
To use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT images of a multi-institutional, multi-vendor, multicenter dataset. A total 264 histologically confirmed were included, from US Swedish centers. Images augmented divided randomly 70%:30% algorithm training testing. Three CNNs (InceptionV3, Inception-ResNetV2, VGG-16) pretrained with transfer learning fine-tuned our dataset to distinguish tumors. The...
Atlases of normal genomics, transcriptomics, proteomics, and metabolomics have been published in an attempt to understand the biological phenotype health disease set basis comprehensive comparative omics studies. No such atlas exists for radiomics data. The purpose this study was systematically create a dataset abdominal pelvic that can be used model development validation. Young adults without any previously known disease, aged > 17 ≤ 36 years old, were retrospectively included. All...
Abstract Radiomics, the extraction of quantitative features from medical images, has shown great promise in enhancing diagnostic and prognostic models, particularly CT MRI. However, its application ultrasound (US) imaging, especially musculoskeletal (MSK) remains underexplored. The inherent variability ultrasound, influenced by operator dependency various imaging settings, presents significant challenges to reproducibility radiomic features. This study aims identify whether commonly used...
Abstract Objectives To investigate the intra- and inter-rater reliability of total methodological radiomics score (METRICS) its items through a multi-reader analysis. Materials methods A 12 raters with different backgrounds experience levels were recruited for study. Based on their level expertise, randomly assigned to following groups: two groups, intra-rater where each group included one without preliminary training session use METRICS. Inter-rater groups assessed all 34 papers, while...
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pre-treatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations primary tumors and non-mass-enhanced regions. The integrates imaging data from four collections Cancer Archive (TCIA), where only 163 cases with segmentations were initially available. facilitate...
Differentiation between transient osteoporosis (TOH) and avascular necrosis (AVN) of the hip is a longstanding challenge in musculoskeletal radiology. The purpose this study was to utilize MRI-based radiomics machine learning (ML) for accurate differentiation two entities. A total 109 hips with TOH 104 AVN were retrospectively included. Femoral heads necks segmented features extracted. Three ML classifiers (XGboost, CatBoost SVM) using 38 relevant trained on 70% validated 30% dataset....
To systematically review current research applications of radiomics in patients with cholangiocarcinoma and to assess the quality CT MRI studies.A systematic search was conducted on PubMed/Medline, Web Science, Scopus databases identify original studies assessing and/or MRI. Three readers different experience levels independently assessed using score (RQS). Subgroup analyses were performed according journal type, year publication, quartile impact factor (from Journal Citation Report...
CT liver perfusion (CTLP) has been well validated for hepatocellular carcinoma (HCC) detection, characterization, and treatment response evaluation. However, its role in HCC management algorithms remains unclear. This study aims to assess the diagnostic performance of CTLP alone or as an adjunct MRI patients considered for- undergoing locoregional HCC. Thirty-nine under surveillance (36 male, 31 cirrhotic, 16 pretreatment, 19 post-transarterial chemoembolization, 2 post-ablation) underwent a...