BlineSlide

K252007

Deep 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).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K252007