Tag: AI in medical imaging

A systematic review of 26 studies finds AI models diagnosing multiple sclerosis with over 95 percent accuracy in some cases, ...

A machine learning model applied to routine CT urography scans can non-invasively predict HER2 status in bladder cancer, potentially guiding ...

A University of Texas at Arlington team is developing AI-based 4D reconstruction methods to substantially reduce radiation dose in multi-phase ...

A meta-analysis of 40 studies covering 12,769 patients shows that radiomics-based models combining CT image features with clinical data can ...

Deep learning models appeared to detect inguinal hernias in children from chest X-rays, but a rigorous new study shows the ...

Researchers have built a hybrid transformer-U-Net that segments diabetic foot ulcers with expert-level overlap and quantitatively explainable visualisations.

A machine learning model trained on CT texture features of metastatic neck lymph nodes predicted which patients with advanced nasopharyngeal ...

A fully automated deep learning system called CFAPS segments carotid plaques on multi-contrast MRI and classifies their vulnerability in under ...

A new multi-task AI framework called TAZD-Net segments thyroid nodules and predicts malignancy by explicitly reasoning over the lesion, its ...

Researchers have externally validated an automated pipeline that measures paraspinal muscle size and fat content at the L3-L4 level on ...