Strickland on film: Part 2 -- Reporting networks & the future

Feb 9, 2016
Latest in Imaging Informatics
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
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Nanox AI signs UK distribution deal for HealthOST bone health software
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Review highlights opportunities and risks of AI in pediatric imaging
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AI framework could improve MRI quality control in prostate cancer
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The framework uses a DL model trained on low- and high-quality MR images and helped improve radiologist accuracy.
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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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HealthOST analyzes routine CT scans for spine assessment.
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Review highlights opportunities and risks of AI in pediatric imaging
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Adult-trained AI tools may underperform in children, requiring pediatric-specific datasets, age-stratified validation, and stronger clinical oversight for safe deployment, researchers say.
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AI performs well in breast screening, but density complicates the picture
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While AI can support breast cancer risk assessment, its performance declined as breast density increased, while variations related to equipment vendor and measurement level may complicate personalized screening.
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Radiologists can identify three out of four AI-generated images
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Radiologists found it particularly difficult to distinguish between real and AI-generated CT and MR images.
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Top eight incorrectly classified synthetic images (Most convincing fake images). Images A, B, C, D, E, F, G and H were the most infrequently correctly classified images at 24%, 43%, 59%, 64%, 69%, 71%, 71% and 73% respectively. All images are AI-generated. Images A, B and C are head CTs. Images D and E are chest CTs. Image F is a cardiac CT. Image G is a musculoskeletal MRI. Image H is a head MRI. Images are republished under a Creative Commons license (CC BY 4.0).
Incorrect AI suggestions influence reader performance on mammography
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The biggest impact on reader performance stemmed from false-negative AI suggestions.
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A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.
The cost of AI, and finding its breakeven point
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Dr. Rowland Illing, chief medical officer at Amazon Web Services (AWS) and a former interventional radiologist.
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