Combining four CAD modules for valvular pathologies with a variety of automated measurements, the AI-enabled AISAP Cardio ultrasound system reportedly facilitates up to a 90 percent accuracy rate in detecting common cardiac conditions.
The Food and Drug Administration (FDA) has granted 510(k) clearance for the AISAP Cardio, a cloud-based, point-of-care ultrasound (POCUS) system that may enable early detection for a variety of cardiac conditions.1
Offering automated analyses and reports, the AISAP Cardio platform has four computer-assisted diagnosis (CAD) modules for valvular pathologies, according to AISAP, the developer of the POCUS system. Trained on over 24 million echocardiography video clips, the AISAP Cardio platform provides automated measurements for cardiac structural functional parameters, ranging from left ventricle ejection fraction (LVEF) and right ventricular fractional area change (RV FAC) to inferior vena cava (IVC) diameter.
AISAP notes that recently published prospective research showed that use of the AISAP Cardio platform led to management changes in one-third of patients.2 For clinicians with basic ultrasound skills, AISAP maintained that AISAP Cardio has up to a 90 percent accuracy rate in assessing common cardiac structural and functional parameters.1
Smadar Kort, M.D., said the AISAP Cardio platform, which will be commercially available on September 1, has “game-changer” potential.
“We know that structural heart disease and heart failure are the leading causes of hospitalization and morbidity in the U.S. Enabling a wide variety of qualified physicians to quickly and accurately diagnose these conditions at the bedside could lead to earlier detection and treatment, and better patient outcomes, as well as greater efficiencies and cost savings to health systems, while ultimately saving countless lives,” noted Dr. Kort, the system director of non-invasive cardiac imaging at Stony Brook Medicine in Stony Brook, N.Y.
References
1. AISAP. AISAP’s CARDIO AI-powered point-of-care ultrasound diagnostic assessment software receives FDA clearance. Business Wire. Available at: https://www.businesswire.com/news/home/20240822701346/en/AISAP%E2%80%99s-CARDIO-AI-powered-Point-of-Care-Ultrasound-Diagnostic-Assessment-Software-Receives-FDA-Clearance . Published August 22, 2024. Accessed August 22, 2024.
2. Fisher L, Yarkoni Y, Faierstein K, et al. Enhancing handheld point-of-care echocardiography with artificial intelligence: a prospective clinical trial. J Am Coll Cardio. 2024;83(13):2344.
Can AI Enhance CT Detection of Incidental Extrapulmonary Abnormalities and Prediction of Mortality?
September 18th 2024Emphasizing multi-structure segmentation and feature extraction from chest CT scans, an emerging AI model demonstrated an approximately 70 percent AUC for predicting significant incidental extrapulmonary findings as well as two-year and 10-year all-cause mortality.
MRI or Ultrasound for Evaluating Pelvic Endometriosis?: Seven Takeaways from a New Literature Review
September 13th 2024While noting the strength of MRI for complete staging of disease and ultrasound’s ability to provide local disease characterization, the authors of a new literature review suggest the two modalities offer comparable results for diagnosing pelvic endometriosis.
The Reading Room: Artificial Intelligence: What RSNA 2020 Offered, and What 2021 Could Bring
December 5th 2020Nina Kottler, M.D., chief medical officer of AI at Radiology Partners, discusses, during RSNA 2020, what new developments the annual meeting provided about these technologies, sessions to access, and what to expect in the coming year.
FDA Clears Controlled Contrast Delivery Method for Ultrasound Imaging of Fallopian Tubes
September 9th 2024Facilitating natural contrast delivery through an intrauterine catheter, FemChec can be utilized for ultrasound assessment of fallopian tubes and may provide diagnostic confirmation for an emerging non-surgical option for permanent birth control.
Study Assesses Lung CT-Based AI Models for Predicting Interstitial Lung Abnormality
September 6th 2024A machine-learning-based model demonstrated an 87 percent area under the curve and a 90 percent specificity rate for predicting interstitial lung abnormality on CT scans, according to new research.