BlineSlide
K252007Deep Breathe, Inc. · cleared 2025-10-06 · product code QIH · Radiology
Premarket evidence — what FDA accepted
Device typesamd
source quote (p.7)
“Blineslide is a cloud service application that helps qualified users with image-based assessment of lung ultrasound (LUS) cines acquired from the anterior or anterolateral chest regions during a physician-led LUS examination of patients aged 18 years or older. It does not directly interface with ultrasound systems.”
AlgorithmAI-assisted tool for detecting the presence or absence of B line artifacts in LUS cines, implementing artificial intelligence including non-adaptive machine learning algorithms trained with clinical data, and using deep convolutional neural networks for segmentation or landmark detection.
source quote (p.7)
“B Line Artifact Module: an AI-assisted tool for detecting the presence or absence of B line artifacts in LUS cines. Ultrasound image processing software implementing artificial intelligence including non-adaptive machine learning algorithms trained with clinical data intended for non-invasive analysis of ultrasound data. Deep convolutional neural networks for segmentation or landmark detection”
Adaptive (vs locked)No
source quote (p.9)
“Ultrasound image processing software implementing artificial intelligence including non-adaptive machine learning algorithms trained with clinical data intended for non-invasive analysis of ultrasound data”
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.12)
“A comprehensive cybersecurity assessment was performed in accordance with FDA's premarket cybersecurity guidance and industry best practices. The device underwent penetration testing, vulnerability scanning, and a Common Vulnerabilities and Exposures (CVE) analysis. Identified threats were reviewed and mitigated using accepted controls, and no unmitigated high-severity vulnerabilities remained at the time of release.”
Validation studies (1)
Retrospective clinical
n=1,005 cases
endpoints: sensitivity; specificity
Reported performance (2 observations)
sensitivity0.91CI 0.88 – 0.94
source quote (p.12)
“Sensitivity 0.91 (0.88 – 0.94)”
specificity0.84CI 0.81 – 0.86
source quote (p.12)
“Specificity 0.84 (0.81 – 0.86)”
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
3
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).