Emerging breast ultrasound research showed the use of computer-aided diagnosis (CAD), powered by deep learning, led to 24 percent and 36.9 percent improvements in accuracy and specificity, respectively, in the use of BI-RADS classifications by radiologists without breast ultrasound expertise.
African American and Asian-Chinese women have a disproportionately higher prevalence of fibroids, according to newly published transvaginal ultrasound findings in a diverse population of nearly 1,000 women.
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In a recent video interview, David Ouyang, M.D., shared insights from two recent studies he co-authored on the use of artificial intelligence (AI) to improve initial assessment of left ventricular ejection fraction (LVEF) on echocardiography and ascertain cardiac risks associated with changes in the left ventricle sphericity index seen on magnetic resonance imaging (MRI).
Adjunctive use of an artificial intelligence (AI) software demonstrated nearly equivalent sensitivity and over 28 percent higher accuracy in comparison to radiologist assessment of breast ultrasound images for breast lesions, according to new research presented at the recent Society of Breast Imaging (SBI) conference.
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The artificial intelligence (AI)-powered EchoGo Amyloidosis can reportedly detect cardiac amyloidosis by assessing a single echocardiogram.
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Lamenting a lack of control over imaging requests from referring clinicians, this author suggests that a more collaborative approach between referrers and radiologists may facilitate more efficient use of imaging.
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The BD Prevue™ II Peripheral Vascular Access System reportedly offers real-time needle depth markers that may reduce multiple needlestick attempts and allow easier placement of intravenous (IV) access in high-risk patients.
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Based on a review of 3,495 echocardiographic studies to evaluate left ventricular ejection fraction (LVEF), researchers found that cardiologists changed initial artificial intelligence (AI) assessment 16.8 percent of the time and initial sonographer assessment 27.2 percent of the time.
The artificial intelligence (AI) powered Auto B-line Counter, which reportedly produces a B-line count from a six-second ultrasound clip, may facilitate more expedient and consistent assessment of abnormal lung function.
Emerging research shows the adjunctive use of automated breast ultrasound with mammography has similar sensitivity for breast cancer detection as adjunctive handheld ultrasound but may offer reduced false-positive rates in women with dense breasts.
Smart-CKD, an ultrasound-derived, computer-aided diagnosis (CAD) tool, demonstrated an area under the curve of 81 percent and an 83 percent sensitivity rate in a validation cohort for differentiating between mild and moderate to severe fibrosis in patients with chronic kidney disease.
Findings from three randomized trials of ultrasound renal denervation revealed a significant reduction in daytime ambulatory systolic blood pressure in patients with varying levels of hypertension.
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The TE Air reportedly combines enhanced point-of-care ultrasound imaging with an array of practical benefits for radiologists.
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The HERA W10 Elite ultrasound platform provides enhanced visualization features and artificial intelligence (AI)-aided measurement capabilities.
Employing an artificial intelligence (AI)-powered scoring system, LVivo IQS reportedly provides real-time assessment of the quality of cardiac ultrasound images.
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In what is reportedly the first Food and Drug Administration (FDA) 510(k) clearance for the use of artificial intelligence (AI) for musculoskeletal ultrasound, the model provides automated measurements of tendons in the knee, ankle, and foot.
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