SmartChest
K232410Milvue · cleared 2024-05-10 · product code QFM · Radiology
Premarket evidence — what FDA accepted
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.”
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.”
source quote (p.5)
“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”
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 (1)
Retrospective clinical
n=300 cases
endpoints: ROC AUC; Sensitivity; Specificity; mean execution time
standards: IEC 62304:2006/A1:2016, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices, Content of Premarket Submission for Management of Cybersecurity in Medical Devices
Reported performance (6 observations)
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]”
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]”
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]”
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]”
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]”
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
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).