SmartChest

K232410

Milvue · cleared 2024-05-10 · product code QFM · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“SmartChest is a radiological computer assisted triage and notification software that analyzes frontal chest X-ray images (Postero-Anterior (PA) or Antero-Posterior (AP)) of transitional adolescents (18-21 yo but treated like adults) and adults (≥22 yo) for the presence of suspected pleural effusion and/or pneumothorax. SmartChest uses an artificial intelligence algorithm to analyze the images for features suggestive of critical findings and provides case-level output available to a PACS (or other DICOM storage platforms) for worklist prioritization.”
Algorithmartificial intelligence algorithm, CNN
source quote (p.3)
“SmartChest uses an artificial intelligence algorithm to analyze the images for features suggestive of critical findings and provides case-level output available to a PACS (or other DICOM storage platforms) for worklist prioritization.A structure of the CNN is then defined, which consists of different types of layers.”
Adaptive (vs locked)No
source quote (p.5)
“These images are then processed to fit the model's requirements, which can involve resizing and normalizing the images. A structure of the CNN is then defined, which consists of different types of layers. The collected data are used to train the model, adjusting its weights based on the errors it makes in predictions. To fine-tune the model and prevent it from overly specializing in the training data, a separate validation set is used. Finally, the model's performance is assessed with a testing set to see how well it can handle unseen data. Depending on the results, it is possible to go back and adjust the data, model's structure, or fine-tuning parameters, then repeat the training process until the model performs satisfactorily”
PCCPNo
Cybersecurity addressedYes
source quote (p.7)
“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" and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices."”

Validation studies (2)

Retrospective clinical

n=300 cases

endpoints: ROC AUC; Sensitivity; Specificity; Mean execution time

standards: IEC 62304:2006/A1:2016

Retrospective clinical

n=300 cases

endpoints: ROC AUC; Sensitivity; Specificity; Mean execution time

standards: IEC 62304:2006/A1:2016

Reported performance (6 observations)

sensitivity92.7CI 95% CI: 87.4-96.2
source quote (p.7)
“For pneumothorax, the results are as follows: ROC AUC 0.989 [0.978; 0.997], Sensitivity 92.7% [95% CI: 87.4-96.2], and Specificity 97.3% [95% CI:93.4-99.1]”
specificity97.3CI 95% CI:93.4-99.1
source quote (p.7)
“For pneumothorax, the results are as follows: ROC AUC 0.989 [0.978; 0.997], Sensitivity 92.7% [95% CI: 87.4-96.2], and Specificity 97.3% [95% CI:93.4-99.1]”
aurocas written: “auc”0.989CI 0.978; 0.997
source quote (p.7)
“For pneumothorax, the results are as follows: ROC AUC 0.989 [0.978; 0.997], Sensitivity 92.7% [95% CI: 87.4-96.2], and Specificity 97.3% [95% CI:93.4-99.1]”
aurocas written: “Pleural Effusion ROC AUC”0.975CI 0.960; 0.987
source quote (p.7)
“For pleural effusion, the results were as follows: ROC AUC 0.975 [0.960; 0.987], Sensitivity 93.3% [95% CI: 88.1-96.4], Specificity 90.0% [95% CI: 84.1-94.1]”
sensitivityas written: “Pleural Effusion Sensitivity”93.3CI 95% CI: 88.1-96.4
source quote (p.7)
“For pleural effusion, the results were as follows: ROC AUC 0.975 [0.960; 0.987], Sensitivity 93.3% [95% CI: 88.1-96.4], Specificity 90.0% [95% CI: 84.1-94.1]”
specificityas written: “Pleural Effusion Specificity”90CI 95% CI: 84.1-94.1
source quote (p.7)
“For pleural effusion, the results were as follows: ROC AUC 0.975 [0.960; 0.987], Sensitivity 93.3% [95% CI: 88.1-96.4], Specificity 90.0% [95% CI: 84.1-94.1]”

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/K232410