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FDA Clears AI-Powered Qualitative Perfusion Mapping for Cone-Beam CT
Reportedly validated in more than 10 clinical trials, the AngioFlow perfusion imaging software enables timely identification of brain regions with cerebral blood flow reduction and those with significant hypoperfusion.
The Food and Drug Administration (FDA) has granted 510(k) clearance for AngioFlow™, an artificial intelligence (AI)-enabled perfusion imaging software that may facilitate more timely assessments of perfusion status prior to endovascular procedures.
Offering color-coded perfusion mapping that identifies regions with reduced cerebral blood flow and blood volume, AngioFlow provides results within minutes of a cone-beam computed tomography (CT) scan, according to RapidAI, the manufacturer of AngioFlow.
The newly FDA-cleared AngioFlow reportedly identifies brain regions with reduced cerebral blood flow within minutes of a cone-beam computed tomography (CT) scan, according to RapidAI, the manufacturer of AngioFlow. (Image courtesy of RapidAI.)
In addition to potential cost savings by reducing redundant imaging, RaidAI emphasized that AngioFlow’s timely assessments may reduce long-term disability risks and facilitate life-saving interventions for patients in need of acute stroke care.
“AngioFlow by RapidAI will allow physicians to assess the need for additional imaging immediately in the interventional suite. By avoiding unnecessary scans, stroke patients can receive the timely care that can be the difference between being able to walk out of a hospital to their homes versus being discharged to a skilled nursing facility,” said Abhishek Singh, MD, DABPN, DUCNS, who is affiliated with the Creighton University School of Medicine in Omaha, Nebraska.
The company added that AngioFlow has been validated in more than 10 clinical trials.
CT Study: AI Algorithm Comparable to Radiologists in Differentiating Small Renal Masses
May 14th 2024An emerging deep learning algorithm had a lower AUC and sensitivity than urological radiologists for differentiating between small renal masses on computed tomography (CT) scans but had a 21 percent higher sensitivity rate than non-urological radiologists, according to new research.
What a New Meta-Analysis Reveals About Fractional Flow Reserve Assessment with Computed Tomography
May 13th 2024While acknowledging variable accuracy overall with CT-derived fractional flow reserve (FFR-CT) values, researchers found that the accuracy rate increased to 90 percent for FFR-CT values greater > 0.90 and < 0.49.
MRI-Based Deep Learning Algorithm Shows Comparable Detection of csPCa to Radiologists
May 8th 2024In a study involving over 1,000 visible prostate lesions on biparametric MRI, a deep learning algorithm detected 96 percent of clinically significant prostate cancer (csPCa) in comparison to a 98 percent detection rate for an expert genitourinary radiologist.
