Overjet Periapical Radiolucency Assist
K231678Overjet, Inc · cleared 2023-09-21 · product code MYN · Radiology
Premarket evidence — what FDA accepted
Device typesamd
source quote (p.3)
“Overjet Periapical Radiolucency (PARL) Assist is a radiological, automated, concurrent read computer-assisted detection software intended to aid in the detection of periapical radiolucencies on permanent teeth captured on periapical radiographs.”
AlgorithmMachine Learning model, operating in three layers: a Network Layer, a Presentation Layer, and a Decision Layer.
source quote (p.4)
“Images are pulled in from a clinic/dental office, and the Machine Learning model creates predictions in the Decision Layer and results are pushed to the dashboard, which are in the Presentation Layer.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this
Validation studies (2)
Reader study (MRMC)
n=379 images
endpoints: AUC of the receiver operating characteristic curve (ROC)
Standalone
n=763 images
Reported performance (12 observations)
sensitivity0.88CI 95% CI's (0.847, 0.914)
source quote (p.5)
“Image level standalone sensitivity was 88%, 95% CI's (0.847, 0.914).”
specificity0.842CI 95% CI's (0.810, 0.847)
source quote (p.5)
“Image Level standalone specificity was 84.2%, 95% CI's (0.810, 0.847).”
sensitivityas written: “Reader improvement for assisted readers at the instance (polygon) level sensitivity”0.162CI (0.125, 0.194)
source quote (p.5)
“Reader improvement for assisted readers at the instance (polygon) level sensitivity averaged across all readers was 16.2% (0.125, 0.194).”
sensitivityas written: “Polygon (instance) level standalone sensitivity”0.664CI 95% CI's (0.615, 0.711)
source quote (p.5)
“Polygon (instance) level standalone sensitivity was 66.4%, 95% CI's (0.615, 0.711).”
sensitivityas written: “Dexis - Sensitivity”0.867CI (0.800, 0.933)
source quote (p.5)
“Dexis - Sensitivity: 0.867 (0.800, 0.933)”
specificityas written: “Dexis - Specificity”0.885CI (0.827, 0.942)
source quote (p.5)
“Specificity: 0.885 (0.827, 0.942)”
sensitivityas written: “e2v - Sensitivity”0.861CI (0.785, 0.937)
source quote (p.5)
“e2v - Sensitivity: 0.861 (0.785, 0.937)”
specificityas written: “e2v - Specificity”0.804CI (0.728, 0.875)
source quote (p.5)
“Specificity: 0.804 (0.728, 0.875)”
sensitivityas written: “Gendex - Sensitivity”0.889CI (0.815, 0.951)
source quote (p.5)
“Gendex - Sensitivity: 0.889 (0.815, 0.951)”
specificityas written: “Gendex - Specificity”0.793CI (0.712, 0.865)
source quote (p.5)
“Specificity: 0.793 (0.712, 0.865)”
sensitivityas written: “Schick - Sensitivity”0.908CI (0.836, 0.961)
source quote (p.5)
“Schick - Sensitivity: 0.908 (0.836, 0.961)”
specificityas written: “Schick - Specificity”0.891CI (0.827, 0.945)
source quote (p.5)
“Specificity: 0.891 (0.827, 0.945)”
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
-100%
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).