Aine Cryts

MetroHealth Medical Center in Cleveland needed a better medical data management strategy than just holding on to everything indefinitely. But before it could move to enterprise content management, it needed buy-in from clinicians that some data could be removed from storage after a predefined period.

March 7, 2016

2015 12 04 17 57 49 609 Hands Shaking V3 200

3 things CIOs can learn from radiology analytics

There are three important things that healthcare chief information officers (CIOs) can learn from radiology data analytics -- and one important thing they can't, according to radiology informatics experts.

February 24, 2016

2016 02 24 15 09 37 954 Data Analytics 200

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Artificial Intelligence Ai Global Communication Network 400

Most FDA-cleared AI medical devices not tested on patient outcomes

Of the 1,059 FDA-cleared devices used in radiology, three had a registered trial.

Low-quality axial T2-weighted MRI scans with gradient-weighted class activation mapping. The low-quality MRI scans (top row) show blurred structures and artifacts making zonal and capsule boundaries indistinguishable. On the same scans with gradient-weighted class activation map overlays (bottom row), red indicates regions that were most heavily weighted by the deep learning model toward the low-quality prediction.

AI framework could improve MRI quality control in prostate cancer

(A) Ga-68 NK224 PET/CT maximum intensity projection image (left), axial PET/CT images (center), and axial CT images (right) in a 43-year-old female participant with lung adenocarcinoma show lesions in the right cerebellum (blue arrow), right frontal lobe (red arrow), and sternum (green arrow) with low, intermediate, and high tracer uptake, respectively. Photomicrographs (scale bar, 100 μm) with hematoxylin-eosin (H&E) staining (center bottom) and programmed death ligand 1 (PD-L1) immunohistochemical (IHC) staining (right bottom) show high PD-L1 expression in the sternal lesion (tumor proportion score ≥ 50%).

New immuno-PET tracer shows promise in lung cancer

$Artificial intelligence (AI) assistance facilitates correct fracture identification in cases initially missed by human readers. A representative case is shown using anteroposterior radiographs in a 29-year-old male patient with wrist trauma (suspected nondisplaced transverse scaphoid fracture). The reader’s initial unaided assessment (top) resulted in a missed fracture, whereas with AI assistance (bottom), the fracture was correctly identified. The green box represents the fracture location as predicted by the AI tool, the blue box represents the reference standard annotation, and the red dots represent individual reader annotations.$