EFAI NeuroSuite CT ICH Assessment System
K231025Ever Fortune.AI Co., Ltd. · cleared 2023-10-04 · product code QAS · Radiology
Premarket evidence — what FDA accepted
source quote (p.3)
“EFAI ICHCT is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage (ICH). EFAI ICHCT analyzes cases using deep learning algorithms to identify suspected ICH findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.”
source quote (p.3)
“EFAI ICHCT analyzes cases using deep learning algorithms to identify suspected ICH findings.”
source quote (p.8)
“Additionally, the software validation activities were performed in accordance with IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, in addition to the FDA Guidance documents, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices”(2005), and “Content of Premarket Submission for Management of Cybersecurity in Medical Devices."”
Validation studies (1)
Retrospective clinical
n=288 cases · 23 site(s)
endpoints: identifying intracranial hemorrhage (ICH) findings; sensitivity; specificity
standards: IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices”(2005), Content of Premarket Submission for Management of Cybersecurity in Medical Devices.
Reported performance (3 observations)
source quote (p.8)
“The EFAI ICHCT was able to demonstrate sensitivity and specificity of 0.947 (95%CI=0.895-0.974) and 0.949 (95% CI=0.902-0.974) respectively”
source quote (p.8)
“The EFAI ICHCT was able to demonstrate sensitivity and specificity of 0.947 (95%CI=0.895-0.974) and 0.949 (95% CI=0.902-0.974) respectively”
source quote (p.8)
“as well as an AUROC of 0.983 (95% CI=0.969-0.997)”
Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.
Predicate network
Postmarket — what happened after clearance
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.
Reimbursement — how devices like this got paid
Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).