NaviCam ProScan

DEN230027

Ankon Technologies co., ltd · granted 2023-12-12 · product code QZF · Gastroenterology-Urology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.1)
“Gastrointestinal capsule endoscopy analysis software device. A gastrointestinal capsule endoscopy analysis software device is used to analyze pre-recorded capsule endoscopy videos of the gastrointestinal tract that are suspected of containing lesions. This device uses software algorithms to identify images and areas of interest as outputs to aid the clinician in analyzing suspected lesions, for clinician review of device outputs. The device may include hardware to support interfacing with a capsule imaging system.”
Algorithmconvolutional neural networks using different deep learning models
source quote (p.3)
“Both software algorithms are based on convolutional neural networks using different deep learning models.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedYes
source quote (p.4)
“Regarding the cybersecurity, documentation included all the recommended information from the FDA guidance document “Content of Premarket Submissions for Management of Cybersecurity in Medical Devices.” This includes a threat model, cybersecurity mitigation information, a malware-free shipping plan, an upgrade plan, and other information for safeguarding the algorithms.”

Validation studies (3)

Bench

n=2,642 patients · 8 site(s)

endpoints: lesion detection sensitivity; lesion detection specificity; lesion detection AUC; tract site recognition sensitivity; tract site recognition specificity

Retrospective clinical

n=87 patients

endpoints: image review time

Prospective clinical

n=133 patients · 7 site(s)

endpoints: diagnostic yield for detection of significant small bowel pathology; mean reading time

Reported performance (14 observations)

sensitivity0.98CI 93.95%-99.71%
source quote (p.7)
“Patient-level sensitivity and specificity were determined to be 98% (95% CI: 93.95%-99.71%) and 37% (95%CI: 27.27%-48.02%), respectively.”
specificity0.37CI 27.27%-48.02%
source quote (p.7)
“Patient-level sensitivity and specificity were determined to be 98% (95% CI: 93.95%-99.71%) and 37% (95%CI: 27.27%-48.02%), respectively.”
aurocas written: “auc”0.911CI 0.872-0.945
source quote (p.7)
“AUC(95%CI)=0.911(0.872-0.945))”
sensitivityas written: “Image-level Lesion Detection Sensitivity”0.9505CI 94.28%-95.72%
source quote (p.8)
“Image level sensitivity and specificity were determined to be 95.05% (95% CI: 94.28%-95.72%) and 97.54% (95% CI: 97.28%-97.78%), respectively.”
specificityas written: “Image-level Lesion Detection Specificity”0.9754CI 97.28%-97.78%
source quote (p.8)
“Image level sensitivity and specificity were determined to be 95.05% (95% CI: 94.28%-95.72%) and 97.54% (95% CI: 97.28%-97.78%), respectively.”
aurocas written: “Image-level Lesion Detection AUC”0.993CI 0.981-1.000
source quote (p.8)
“AUC(95%CI)=0.993(0.981-1.000))”
sensitivityas written: “Tract Site Recognition Sensitivity (Oral cavity and beyond)”0.9947CI 99.14%-99.68%
source quote (p.9)
“Oral cavity and beyond 99.47% (99.14%-99.68%)”
specificityas written: “Tract Site Recognition Specificity (Oral cavity and beyond)”0.995CI 99.39%-99.58%
source quote (p.9)
“Specificity (95% CI) 99.50% (99.39%-99.58%)”
sensitivityas written: “Tract Site Recognition Sensitivity (Esophagus)”0.9892CI 97.79%-99.50%
source quote (p.9)
“Esophagus 98.92% (97.79%-99.50%)”
specificityas written: “Tract Site Recognition Specificity (Esophagus)”0.991CI 98.98%-99.22%
source quote (p.9)
“99.10% (98.98%-99.22%)”
sensitivityas written: “Tract Site Recognition Sensitivity (Stomach)”0.996CI 99.49%-98.69%
source quote (p.9)
“Stomach 99.60% (99.49%-98.69%)”
specificityas written: “Tract Site Recognition Specificity (Stomach)”0.9906CI 98.80%-99.26%
source quote (p.9)
“99.06% (98.80%-99.26%)”
sensitivityas written: “Tract Site Recognition Sensitivity (Small Bowel)”0.9926CI 98.89%-99.51%
source quote (p.9)
“Small Bowel 99.26% (98.89%-99.51%)”
specificityas written: “Tract Site Recognition Specificity (Small Bowel)”0.9836CI 98.18%-98.52%
source quote (p.9)
“98.36% (98.18%-98.52%)”

Each value carries its own analysis unit and task — never compare or pool across devices. Source: De Novo decision summary PDF.

Predicate network

Postmarket — what happened after clearance

Not yet tracked — the weekly postmarket refresh hasn't snapshotted this device.

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 De Novo AI/ML devices in the Gastroenterology-Urology 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/DEN230027