EFAI Chestsuite XR Malpositioned ETT Assessment System (ETT-XR-100)
K242821Ever Fortune.AI, Co., Ltd. · cleared 2025-02-20 · product code QAS · Radiology
Premarket evidence — what FDA accepted
Device typesamd
source quote (p.6)
“EFAI CHESTSUITE XR MALPOSITIONED ETT ASSESSMENT SYSTEM (EFAI ETTXR) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze chest radiographs and alerts the PACS/RIS workstation once images with features suggestive of malpositioned ETT are identified.”
Algorithmdeep learning algorithms
source quote (p.4)
“EFAI ETTXR analyzes cases using deep learning algorithms to identify suspected malpositioned ETT findings.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.9)
“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, “Content of Premarket Submissions for Device Software Functions” and “Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions.””
Validation studies (1)
Retrospective clinical
n=940 cases
endpoints: sensitivity; specificity
standards: IEC 62304:2006/A1:2016, ISO 14971:2019
Reported performance (2 observations)
sensitivity0.89CI 95% CI=0.846-0.923
source quote (p.9)
“The performance evaluation conducted exclusively on AP view showed EFAI ETTXR demonstrated a sensitivity and specificity of 0.890 (95% CI=0.846-0.923)”
specificity0.935CI 95% CI=0.909-0.954
source quote (p.9)
“and 0.935 (95% CI=0.909-0.954)”
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
0
recalls in product code, 24mo
0
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
0
drift signals on this device
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).