4 weeks ago
1 month ago
Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs1 month ago
Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status2 months ago
Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models2 months ago
Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer3 months ago
A pilot study on the performance of machine learning appled to texture-analysis-derived features for breast lesion characterisation at ABUS4 months ago
Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation4 months ago
Welcome to the blog on Artificial Intelligence of the European Society of Radiology
This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.
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- Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis
- Looking outside the box: AI in the fight against COVID-19, how our society is being transformed by tech, and sensors analyzing your football skills
- Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer
- AI in radiology: What patients really want – the best possible diagnosis with the highest possible precision
- Can radiomics improve the prediction of metastatic relapse of myxoid/round cell liposarcomas?