Overjet Calculus Assist

K220928

Overjet Inc. · cleared 2022-12-16 · product code MYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“Overjet Calculus Assist (OCalA) is a radiological automated concurrent-read computer-assisted detection software intended to aid in the detection of interproximal calculus deposits on both bitewing and periapical radiographs.”
AlgorithmThe decision layer processes the image to ensure it is the correct data type, and then annotates it via the algorithm
source quote (p.6)
“2 - The decision layer processes the image to ensure it is the correct data type, and then annotates it via the algorithm”
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)

Standalone

n=618 images

endpoints: Sensitivity; Specificity; AFROC AUC

Reader study (MRMC)

n=614 images

endpoints: Sensitivity; Specificity; alternative free response receiver operating characteristic (AFROC)

standards: ISO 14971

Reported performance (14 observations)

sensitivity0.741CI 66.2%, 82.0%
source quote (p.7)
“Overall standalone sensitivity was 74.1% (66.2%, 82.0%) for bitewing radiographs”
specificity0.994CI 99.1%, 99.6%
source quote (p.7)
“Overall standalone specificity was 99.4% (99.1%, 99.6%) for bitewing radiographs”
aurocas written: “auc”0.859CI 0.823, 0.894
source quote (p.8)
“Image Type Bitewing AUC 0.859 95% CI1 0.823, 0.894”
sensitivityas written: “Standalone Sensitivity (Periapical)”0.729CI 65.3%, 80.5%
source quote (p.7)
“and 72.9% (65.3%, 80.5%) for periapical radiographs.”
specificityas written: “Standalone Specificity (Periapical)”0.996CI 99.3%, 99.8%
source quote (p.7)
“and 99.6% (99.3%, 99.8%) for periapical radiographs.”
aurocas written: “Standalone AUC (Periapical)”0.867CI 0.828, 0.903
source quote (p.8)
“Image Type Periapical AUC 0.867 95% CI1 0.828, 0.903”
sensitivityas written: “Assisted Reader Sensitivity (Bitewing)”0.84CI 78.8%, 88.2%
source quote (p.8)
“For bitewing radiographs, overall reader sensitivity improved from 74.9% (68.3%, 80.2%) to 84.0% (78.8%, 88.2%) unassisted vs assisted.”
sensitivityas written: “Assisted Reader Sensitivity (Periapical)”0.844CI 78.8%, 89.2%
source quote (p.8)
“For periapical radiographs, overall reader sensitivity improved from 74.7% (69.9%, 79.0%) to 84.4% (78.8%, 89.2%) unassisted vs assisted.”
specificityas written: “Assisted Reader Specificity (Bitewing)”0.986CI 98.4%, 98.9%
source quote (p.8)
“For bitewing radiographs, overall reader specificity decreased slightly from 98.8% (98.7%, 99.0%) to 98.6% (98.4%, 98.9%) unassisted vs assisted.”
specificityas written: “Assisted Reader Specificity (Periapical)”0.98CI 97.7%, 98.4%
source quote (p.9)
“reader specificity also decreased slightly from 98.1% (97.8%, 98.4%) to 98.0% (97.7%, 98.4%) unassisted vs assisted.”
aurocas written: “Assisted Reader Avg AUC of AFROC (Bitewing)”0.878CI 0.844, 0.913
source quote (p.9)
“For the average of all readers, AUC increased from 0.840 (0.800, 0.880) to 0.878 (0.844, 0.913) on bitewing radiographs”
aurocas written: “Assisted Reader Avg AUC of AFROC (Periapical)”0.9CI 0.870, 0.929
source quote (p.9)
“and from 0.846 (0.808, 0.884) to 0.900 (0.870, 0.929) on periapical radiographs.”
aurocas written: “Unassisted Reader Avg AUC of AFROC (Bitewing)”0.84CI 0.800, 0.880
source quote (p.9)
“For the average of all readers, AUC increased from 0.840 (0.800, 0.880) to 0.878 (0.844, 0.913) on bitewing radiographs”
aurocas written: “Unassisted Reader Avg AUC of AFROC (Periapical)”0.846CI 0.808, 0.884
source quote (p.9)
“and from 0.846 (0.808, 0.884) to 0.900 (0.870, 0.929) on periapical radiographs.”

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); 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/K220928