Critical Care Suite with Pneumothorax Detection AI Algorithm, Critical Care Suite 2.1, Critical Care Suite

K223491

GE Medical Systems, LLC · cleared 2023-05-25 · product code QBS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“Critical Care Suite with Pneumothorax Detection AI Algorithm is a computer-aided triage, notification, and diagnostic device that analyzes frontal chest X-ray images for the presence of a pneumothorax.”
Algorithmdeep learning locked Al algorithms
source quote (p.6)
“They are all deep learning locked Al algorithms that can be deployed on several computing platforms such as PACS, On Premise, On Cloud or X-ray Imaging Systems.”
Adaptive (vs locked)No
source quote (p.6)
“They are all deep learning locked Al algorithms”
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (3)

Bench

sample size not stated

Retrospective clinical

n=804 images · 2 site(s)

endpoints: detection of pneumothoraxes; detection of large pneumothoraxes; detection of small pneumothoraxes; localization of pneumothorax overlay

Reader study (MRMC)

n=400 images · 2 site(s)

endpoints: improved reader performance for detection of pneumothorax (mean AUC); reader sensitivity; reader specificity; improved reader performance for detection of large pneumothorax (mean AUC); improved reader performance for detection of small pneumothorax (mean AUC)

Reported performance (6 observations)

sensitivity84.3CI 80.6%, 88.0%
source quote (p.8)
“a sensitivity of 84.3% (80.6%, 88.0%)”
specificity93.2CI 90.8%, 95.6%
source quote (p.8)
“a specificity of 93.2% (90.8%, 95.6%)”
aurocas written: “auc”96.1CI 94.9%, 97.2%
source quote (p.8)
“an AUC of 96.1% (94.9%, 97.2%)”
sensitivityas written: “Sensitivity (large pneumothorax)”96.3CI 93.1%, 99.2%
source quote (p.8)
“high sensitivity for detecting large pneumothoraxes with a sensitivity of 96.3% (93.1%, 99.2%)”
sensitivityas written: “Sensitivity (small pneumothorax)”75CI 69.2%, 80.8%
source quote (p.8)
“small pneumothorax with a sensitivity of 75.0% (69.2%, 80.8%).”
diceas written: “DICE Similarity Coefficient”0.705CI 0.683, 0.724
source quote (p.8)
“It also performed with a DICE Similarity Coefficient of 0.705 (0.683, 0.724) indicating that on average 70.5% of the Pneumothorax Overlay area and the true area of a pneumothorax within an image overlap.”

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); a recall is shown as device-attributed only when the recall record itself lists this clearance number. 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).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Radiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K223491