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Deep learning comparable to radiologists for ultrasonic liver analysis
Deep learning is on par with radiologists when it comes to ultrasonic detection and grading of hepatic steatosis.
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Representative radiographs (top), acquired in anteroposterior projection in the supine position, and corresponding attention maps (bottom). (A) Images show main diagnostic findings of the internal data set in a 49-year-old male patient with congestion, pneumonic infiltrates, and effusion (left); a 64-year-old male patient with congestion, pneumonic infiltrates, and effusion (middle); and a 69-year-old female patient with effusion (right). Image courtesy of Radiology.
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Ultrasound models show high marks in liver steatosis detection
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Cases of the Week
Check out our Cases of the Week!
66-year-old man with difficulty voiding
A 66-year-old man with hypertension, hyperlipidemia, diabetes mellitus, and benign prostatic hyperplasia presented with voiding difficulty that started three weeks ago. The patient has since required a urinary catheter for voiding.
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Representative radiographs (top), acquired in anteroposterior projection in the supine position, and corresponding attention maps (bottom). (A) Images show main diagnostic findings of the internal data set in a 49-year-old male patient with congestion, pneumonic infiltrates, and effusion (left); a 64-year-old male patient with congestion, pneumonic infiltrates, and effusion (middle); and a 69-year-old female patient with effusion (right). Image courtesy of Radiology.
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Ultrasound models show high marks in liver steatosis detection
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Liver Digital Tablet 400
Artificial Intelligence
Deep learning comparable to radiologists for ultrasonic liver analysis
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Can transformer models improve AI image interpretations?
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Liver Digital Tablet 400
Deep learning comparable to radiologists for ultrasonic liver analysis
Deep learning is on par with radiologists when it comes to ultrasonic detection and grading of hepatic steatosis.
How effective is AI for detecting PE on CTPA studies?
Can transformer models improve AI image interpretations?
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Ga-68 ABY-025 PET/CT and F-18 FDG-PET/CT images at baseline with F-18 FDG PET/CT follow-up after two cycles of treatment in biopsy-confirmed HER2-positive disease. (A) Patient with high Ga-68 ABY-025 uptake (SUVmax, 21), who previously received three lines of treatment. F-18 FDG-PET/CT follow-up showed complete metabolic response. (B) Patient with low uptake (SUVmax, 5.4), who previously received three lines of treatment. F-18 FDG PET/CT follow-up showed disease progression (a 68% increase in tumor glycolysis) despite HER2-targeted treatment. (C) Patient with heterogeneous Ga-68 ABY-025 uptake, who previously received seven lines of treatment. F-18 FDG-PET/CT follow-up showed heterogeneous response, with lesions higher in Ga-68 ABY-025 uptake tending to have better response. Arrows indicate biopsy sites. Image courtesy of the Journal of Nuclear Medicine.