Mammography Tools: Positioning, Pathology, and Professionalism

Oct 18th, 2013
Gaithersburg, MD
US
https://store2.aheconline.com/
This mammography class will bring you a wealth of information on detection and treatment of breast cancer.
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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.
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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.
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Of the 1,059 FDA-cleared devices used in radiology, three had a registered trial.
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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.
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(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%).
AI fracture detection device validated across three European hospitals
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No differences in AI performance were observed across centers, and AI assistance was associated with higher reader sensitivity.
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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.
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