Peter Mildenberger

2011
How IT supports radiology: An overview of current developments
The introduction of digital imaging and workflow using RIS and PACS has led to fundamental changes in many radiologists' daily work. The European Society of Radiology has prepared informative posters on various topics for this year's congress so that radiologists can understand the facts about IT concepts.
March 4, 2011
2011 02 23 13 09 28 789 Ecr Today Logo
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The rating framework demonstrated robust reproducibility, with three-reader concordance reaching up to 93% in highly standardized centers.
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READ-DVT model performs well in finding DVT from ultrasound reports
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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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Bone scan testing has fallen 35% among Medicare beneficiaries
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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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SDMS President Aubrey Rybyinski discusses how clinics can address sonographer vacancies.
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Avoiding the ED cuts urgent CT turnaround time by half
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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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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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