Brian Casey and Abraham Kim

IS
Expansion of AI tools highlights GE at RSNA
CHICAGO - New advancements in its Edison artificial intelligence (AI) program are a major focus in the RSNA 2019 booth of GE Healthcare. The company is rolling out Edison-based tools across multiple modalities, while also highlighting new developments in CT, MRI, interventional, and x-ray.
December 1, 2019
2019 12 02 22 26 6865 Ge Air Recon Dl 20191202222624
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CT
CT 5-star quality rating system supports optimization
The rating framework demonstrated robust reproducibility, with three-reader concordance reaching up to 93% in highly standardized centers.
Radiological Report 400
Ultrasound
READ-DVT model performs well in finding DVT from ultrasound reports
Dexa
Digital X-Ray
Bone scan testing has fallen 35% among Medicare beneficiaries
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MRI
MRI-based models may predict atypical brain development in children
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READ-DVT model performs well in finding DVT from ultrasound reports
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Declines in testing were greatest among certain underserved groups.
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The models could provide a framework for identifying which adolescents are at higher risk of developing psychiatric conditions.
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Ultrasound MinnieCast, Episode 14: Weathering the sonographer shortage
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Pediatric Imaging
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DBT-based foundational model classifies breast density
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DBT-DINO showed strong performance in classifying breast density from DBT images.
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(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.
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