- AI
- Molecular Imaging
- CT
- X-Ray
- Ultrasound
- MRI
- Facility Management
- Mammography
CMS Approves NTAP Reimbursement for CT-Based AI Triage Software for Obstructive Hydrocephalus
The first radiology triage modality to garner a Breakthrough Device Designation from the FDA, Annalise-Obstructive Hydrocephalus has reported sensitivity and specificity rates of 97.5 percent and 95.3 percent respectively.
The Centers for Medicare and Medicaid Services (CMS) will provide a new technology add-on payment (NTAP) in 2025 for the use of the artificial intelligence (AI) software Annalise-Obstructive Hydrocephalus (OH), which facilitates triage of suspected OH cases based on an analysis of brain computed tomography (CT) cans.
While the clinical presentation of OH has similar characteristics to other neurological conditions, the potentially life-threatening OH requires urgent detection and treatment. Annalise-Obstructive Hydrocephalus identifies suspicious findings of OH within minutes of assessing non-contrast brain CT scans, according to Annalise.ai, the manufacturer of the AI software.
In 2025, the Centers for Medicare and Medicaid Services (CMS) will issue a new technology add-on payment for use of the AI software Annalise-Obstructive Hydrocephalus, which reportedly has a 98.7 percent AUC for detecting obstructive hydrocephalus on non-contrast brain CT scans. (Image courtesy of Annalise.ai )
The company emphasized that Annalise-Obstructive Hydrocephalus, which garnered the FDA’s Breakthrough Device Designation in 2023, has an area under the curve (AUC) of 98.7 percent, a sensitivity rate of 97.5 percent and a specificity rate of 95.3 percent for OH detection.
“Patients with obstructive hydrocephalus can deteriorate rapidly if not promptly identified, evaluated, and treated. Annalise-OH will help care teams identify these patients faster, consult with their neurosurgeon, and reduce the chance that they suffer from severe complications,” noted Aaron Yengo-Kahn, M.D., a neurosurgeon based in Orange, Calif.”
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.
