Viz.ai has announced new clinical data validating the accuracy and clinical utility of its artificial intelligence (AI)-powered hyperdensity measurement solution for intracranial haemorrhage (ICH) management. According to a recent press release from the company, two independent studies have highlighted the ability of Viz ICH Plus to accurately and efficiently measure haemorrhage volume, quantify lateral ventricular volume, and estimate midline shift.
“Patients with ICH often require multiple scans in a short timeframe to guide treatment decisions,” said Peter Kan (University of Texas Medical Branch, Galveston, USA). “Manual measurements can be time-consuming and vary between providers. Viz ICH Plus enables us to assess patients more quickly, identify the most urgent cases, and determine whether intervention is needed—with greater consistency and efficiency.”
The first study, published in Neurosurgery, assessed the performance of Viz ICH Plus in quantifying ICH volume, bilateral lateral ventricle volume, and midline shift. Viz.ai states that the algorithm performed well with low median absolute error in segmenting and quantifying ICH volume, bilateral lateral ventricle volume and midline shift. Viz ICH Plus achieved up to 92.3% accuracy in identifying clinically meaningful findings on scans.
The second study, published in the Journal of NeuroInterventional Surgery, evaluated 139 patients presenting with spontaneous ICH and compared Viz.ai’s automated measurements with traditional mABC/2 estimation. The study demonstrated that Viz ICH Plus was significantly more accurate than mABC/2—with a mean volume difference of 4.77mL vs 8.36mL (p<0.01)—and delivered results nearly three times faster than manual methods.
“Accurate and timely volume estimation is critical for managing patients with ICH, especially in acute and intensive care settings,” said Molly Madziva Taitt, vice president of global clinical affairs at Viz.ai. “These studies reinforce the precision, speed and reliability of Viz ICH Plus, empowering clinicians with real-time information that can enhance decision-making and contribute to improved patient outcomes.”
