Deep Capsule® (Deep Capsule US)

K250655

Digestaid - Artificial Intelligence Development SA · cleared 2026-03-12 · product code QZF · Gastroenterology-Urology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
Deep Capsule® is an artificial intelligence (AI) assisted reading tool designed to aid small bowel capsule endoscopy reviewers in decreasing the time to review capsule endoscopy images for adult patients in whom the capsule endoscopy images were obtained for suspected small bowel bleeding.
Algorithmconvolutional neural networks using different deep learning models
source quote (p.7)
The Deep Capsule detection algorithm is based on convolutional neural networks using different deep learning models.
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.8)
Regarding the cybersecurity, documentation included recommended information from the FDA guidance document "FDA Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions" This includes threat identification, vulnerability assessment, likelihood and impact assessment, cybersecurity mitigation information, security policies and controls, continuous monitoring and review activities, regular auditing and cybersecurity testing.

Validation studies (3)

Standalone

n=272 patients

endpoints: Sensitivity; Specificity; AUC

Standalone

n=96,082 images

endpoints: Sensitivity; Specificity; AUC

Retrospective clinical

n=330 patients · 7 site(s)

endpoints: Diagnostic yield; sensitivity; specificity; positive predictive value; negative predictive value; mean reading time

Reported performance (5 observations)

sensitivity0.972CI (0.947 - 0.986)
source quote (p.26)
0.972 (0.947 - 0.986)
specificity0.125CI (0.055 - 0.261)
source quote (p.26)
0.125 (0.055 - 0.261)
aurocas written: “auc0.816CI (0.690 - 0.942)
source quote (p.26)
AUC = 0.816 95% CI: 0.690-0.942
ppvas written: “PPV0.89CI (0.850 - 0.920)
source quote (p.26)
0.890 (0.850 - 0.920)
npvas written: “NPV0.385CI (0.177 - 0.645)
source quote (p.26)
0.385 (0.177 - 0.645)

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). 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/K250655