FDA Clears AI Software for CT Detection and Triage of Obstructive Hydrocephalus
Large Mammography Study Suggests AI is Equivalent to Radiologists for Double Reading of Exams
In separate test sets that included challenging mammography cases, researchers found that artificial intelligence (AI) demonstrated similar sensitivity and specificity for detecting breast cancer in comparison to assessments from over 500 clinicians.
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In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists.
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The study demonstrated that the combined model yielded improved risk assessment for both interval and long-term breast cancers.
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In comparison to clinical-radiologic assessment, a deep learning CT radiomics nomogram had a 10 percent higher AUC and a 27 percent higher specificity for predicting the macrotrabecular massive subtype of hepatocellular carcinoma in external data testing.
The Echelon Synergy MRI system reportedly uses deep learning technology to accelerate image acquisition and enhance image quality.
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The artificial intelligence (AI)-enabled VisiRad XR reportedly demonstrated an 83 percent sensitivity rate for detecting lung nodules and masses on chest X-rays in one retrospective study.
The De Novo approval for Viz HCM, which assesses electrocardiograms with artificial intelligence (AI) to identify possible cases of hypertrophic cardiomyopathy (HCM), is the 12th FDA clearance of algorithms on the Viz.ai Platform.
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With reported performance validation on over 17,000 ultrasound images, Sonio Detect employs artificial intelligence (AI) to help ensure quality criteria for fetal ultrasound imaging of the brain and heart.
Reportedly the first randomized trial to examine the impact of artificial intelligence (AI) on screening mammography, researchers found AI-aided screening led to a 20 percent increase in breast cancer detection and a 44.3 percent decrease in mammography screening workload.
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Through automated processing of diffusion-weighted magnetic resonance imaging (MRI) scans, the Advanced Neuro Diagnostic Imaging (ANDI) software performs detailed analysis of white matter microstructure.
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In a study of over 1.300 women with dense breasts, the combination of mammography and ultrasound had a recall rate of 11.7 percent, a specificity rate of 89.1 percent and an accuracy rate of 89.2 percent in comparison to a 21.4 percent recall rate, 79.4 percent specificity and 79.5 percent accuracy for the combination of mammography, ultrasound, and artificial intelligence (AI).
Indicated for use in two-dimensional transthoracic echocardiography (2D-TTE) in adults, the AI Guidance software reportedly enables novice ultrasound users to obtain real-time cardiac imaging at the point of care.
In a dataset enriched for African American women, BRCA mutation carriers and those with benign breast disease, a mammography-based deep learning model demonstrated a five-year AUC of 63 percent for predicting breast cancer in comparison to 54 percent for BI-RADS assessment.
In a study of over 20,700 people, researchers found that artificial intelligence (AI) analysis of body composition measurements via lung cancer screening computed tomography (CT) exams improves the prediction of mortality risks for lung cancer, cardiovascular disease, and all-cause mortality.
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An artificial intelligence model, trained on MRI and FLAIR imaging from over 900 patients with multiple sclerosis, demonstrated a 96 percent accuracy rate and 99 percent specificity rate for contrast-enhancing lesions in this patient population.
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