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Latest in Home
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CT 5-star quality rating system supports optimization
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CT 5-star quality rating system supports optimization
By Will Morton
The rating framework demonstrated robust reproducibility, with three-reader concordance reaching up to 93% in highly standardized centers.
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The AI model accurately identified the presence or absence of deep vein thrombosis on unstructured 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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Avoiding the ED cuts urgent CT turnaround time by half
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This is the first reported pilot of an expedited outpatient direct-to-CT imaging program designed to promote ED avoidance.
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VLMs miss most lung nodules on chest x-rays
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Findings indicate poor performance of six VLMs for lung nodule detection on chest x-rays.
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Patient-friendly radiology reports improve family comprehension
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Terminology in radiology reports can be difficult for patients and families to comprehend.
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Early CTA is linked to reduced stroke, decreased complications, and reduced intensive care unit length of stay.
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DBT-based foundational model classifies breast density
By Amerigo Allegretto
DBT-DINO showed strong performance in classifying breast density from DBT images.
August 4, 2026
(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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