Overjet Charting Assist

K233590

Overjet, Inc · cleared 2024-02-23 · product code QIH · Radiology

FDA 510(k) summary for Overjet Charting Assist (K233590)

Constat organizes this clearance into predicate context, validation evidence, reported performance, postmarket signals, and reimbursement links. Each extracted claim stays tied to the public FDA source; missing fields remain explicitly unstated.

Validation studies
1
Cited predicates
1
Performance observations
34
Product code
QIH
Official FDA 510(k) database record ↗FDA 510(k) source PDF ↗

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“Overjet Charting Assist is a Medical Image Management and Processing System (MIMPS) intended to detect natural dental structures including detection of tooth anatomy (enamel, pulp), and tooth numbering, as well as dental structures added through past restorative treatments: implants, crowns, endodontic treatment (previous root canal treatment), fillings. The device is intended to assist dental professionals in producing dental charts based on image analysis.”
AlgorithmAI based image analysis
source quote (p.5)
“Both devices utilize AI based image analysis.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedNo

Validation studies (1)

Standalone

n=634 images · 44 site(s)

endpoints: tooth level sensitivity of past restorative treatment; dental tooth anatomy; tooth level sensitivity (overall classification accuracy) of tooth numbering

Reported performance (34 observations)

sensitivity0.883CI (86.6%, 90.1%)
source quote (p.7)
“Tooth level standalone sensitivity for past restorative structures was 88.3% with 95% CI's of (86.6%, 90.1%).”
sensitivityas written: “Tooth level sensitivity of past restorative structures by Gender (Females)”0.895
source quote (p.6)
“Tooth level sensitivity of past restorative structures by Gender was 89.5% for Females and 86.9% for Males”
sensitivityas written: “Tooth level sensitivity of past restorative structures by Gender (Males)”0.869
source quote (p.6)
“Tooth level sensitivity of past restorative structures by Gender was 89.5% for Females and 86.9% for Males”
specificityas written: “Specificity of past restorative structures by Gender (Females)”0.983
source quote (p.6)
“specificity was 98.3% for Females and 97.9% for Males”
specificityas written: “Specificity of past restorative structures by Gender (Males)”0.979
source quote (p.6)
“specificity was 98.3% for Females and 97.9% for Males”
sensitivityas written: “Tooth level Sensitivity of dental tooth anatomy by Gender (Females)”0.956
source quote (p.6)
“Tooth level Sensitivity of dental tooth anatomy was 95.6% for Females and 96.2% for Males.”
sensitivityas written: “Tooth level Sensitivity of dental tooth anatomy by Gender (Males)”0.962
source quote (p.6)
“Tooth level Sensitivity of dental tooth anatomy was 95.6% for Females and 96.2% for Males.”
sensitivityas written: “Fillings Sensitivity”0.799CI (0.770, 0.824)
source quote (p.6)
“Fillings: Sensitivity of 0.799 (0.770, 0.824), Specificity of 0.986 (0.983, 0.990)”
specificityas written: “Fillings Specificity”0.986CI (0.983, 0.990)
source quote (p.6)
“Fillings: Sensitivity of 0.799 (0.770, 0.824), Specificity of 0.986 (0.983, 0.990)”
sensitivityas written: “RCT Sensitivity”0.943CI (0.918, 0.966)
source quote (p.6)
“RCT: Sensitivity of 0.943 (0.918, 0.966), Specificity of 0.999 (0.998, 1.000)”
specificityas written: “RCT Specificity”0.999CI (0.998, 1.000)
source quote (p.6)
“RCT: Sensitivity of 0.943 (0.918, 0.966), Specificity of 0.999 (0.998, 1.000)”
sensitivityas written: “Crown Sensitivity”0.954CI (0.937, 0.970)
source quote (p.6)
“Crown: Sensitivity of 0.954 (0.937, 0.970), Specificity of 0.994 (0.992, 0.996)”
specificityas written: “Crown Specificity”0.994CI (0.992, 0.996)
source quote (p.6)
“Crown: Sensitivity of 0.954 (0.937, 0.970), Specificity of 0.994 (0.992, 0.996)”
sensitivityas written: “Implant Sensitivity”0.898CI (0.860, 0.935)
source quote (p.6)
“Implant: Sensitivity of 0.898 (0.860, 0.935), Specificity of 0.998 (0.997, 0.999)”
specificityas written: “Implant Specificity”0.998CI (0.997, 0.999)
source quote (p.6)
“Implant: Sensitivity of 0.898 (0.860, 0.935), Specificity of 0.998 (0.997, 0.999)”
sensitivityas written: “Enamel Sensitivity”0.949CI (0.943, 0.956)
source quote (p.7)
“Enamel: Sensitivity of 0.949 (0.943, 0.956), Specificity of 0.863 (0.831, 0.893)”
specificityas written: “Enamel Specificity”0.863CI (0.831, 0.893)
source quote (p.7)
“Enamel: Sensitivity of 0.949 (0.943, 0.956), Specificity of 0.863 (0.831, 0.893)”
sensitivityas written: “Pulp Sensitivity”0.856CI (0.840, 0.872)
source quote (p.7)
“Pulp: Sensitivity of 0.856 (0.840, 0.872), Specificity of 0.951 (0.938, 0.961)”
specificityas written: “Pulp Specificity”0.951CI (0.938, 0.961)
source quote (p.7)
“Pulp: Sensitivity of 0.856 (0.840, 0.872), Specificity of 0.951 (0.938, 0.961)”
accuracyas written: “Panoramic Image Classification Accuracy”0.992CI (0.988, 0.996)
source quote (p.7)
“Panoramic Image: 0.992 (0.988, 0.996)”
accuracyas written: “Bitewing Image Classification Accuracy”0.989CI (0.983, 0.995)
source quote (p.7)
“Bitewing Image: 0.989 (0.983, 0.995)”
accuracyas written: “Periapical Image Classification Accuracy”0.969CI (0.954, 0.983)
source quote (p.7)
“Periapical Image: 0.969 (0.954, 0.983)”
accuracyas written: “Primary Teeth Classification Accuracy”0.988CI (0.970, 1.000)
source quote (p.7)
“Primary Teeth: 0.988 (0.970, 1.000)”
accuracyas written: “Permanent Teeth Classification Accuracy”0.988CI (0.984, 0.991)
source quote (p.7)
“Permanent Teeth: 0.988 (0.984, 0.991)”
sensitivityas written: “Tooth Level standalone sensitivity for dental tooth anatomy (enamel/pulp)”0.959CI (95.1%, 96.5%)
source quote (p.7)
“Tooth Level standalone sensitivity for dental tooth anatomy (enamel/pulp) was 95.9% with 95% CI's of (95.1%, 96.5%).”
sensitivityas written: “Tooth Level standalone sensitivity (Overall Classification Accuracy) for dental tooth numbering”0.988CI (98.4%, 99.1%)
source quote (p.7)
“Tooth Level standalone sensitivity (Overall Classification Accuracy) for dental tooth numbering was 98.8% with 95% CI's of (98.4%, 99.1%).”
diceas written: “Dice for Dental Tooth Anatomy Mean Std. Dev.”0.836CI 0.098
source quote (p.7)
“Dice for Dental Tooth Anatomy resulted in a Mean Std. Dev. 0.836 (0.098) with 95% CI's (0.832, 0.842).”
diceas written: “Dice for Dental Tooth Anatomy 95% CI”stated without valueCI (0.832, 0.842)
source quote (p.7)
“Dice for Dental Tooth Anatomy resulted in a Mean Std. Dev. 0.836 (0.098) with 95% CI's (0.832, 0.842).”
diceas written: “Dice for Past Restorative Structures Mean Std. Dev.”0.918CI 0.078
source quote (p.7)
“Dice for Past Restorative Structures resulted in a Mean Std. Dev. 0.918 (0.078) with 95% CI's (0.914, 0.923).”
diceas written: “Dice for Past Restorative Structures 95% CI”stated without valueCI (0.914, 0.923)
source quote (p.7)
“Dice for Past Restorative Structures resulted in a Mean Std. Dev. 0.918 (0.078) with 95% CI's (0.914, 0.923).”
ppvas written: “PPV for dental tooth anatomy at the tooth level”0.967CI (0.960, 0.973)
source quote (p.7)
“PPV for dental tooth anatomy at the tooth level was 0.967 with 95% CI's of (0.960, 0.973)”
npvas written: “NPV for dental tooth anatomy at the tooth level”0.71CI (0.672, 0.745)
source quote (p.7)
“NPV for dental tooth anatomy at the tooth level was 0.710 with 95% CI's of (0.672, 0.745)”
ppvas written: “PPV for past restorative structures at the tooth level”0.958CI (0.948, 0.967)
source quote (p.7)
“PPV for past restorative structures at the tooth level was 0.958 with 95% CI's of (0.948, 0.967)”
npvas written: “NPV for past restorative structures at the tooth level”0.945CI (0.935, 0.954)
source quote (p.7)
“NPV for past restorative structures at the tooth level was 0.945 with 95% CI's of (0.935, 0.954)”

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

1
recalls in product code, 24mo
4
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
2
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K241684 (decision 2024-08-27) from Overjet, Inc for a matching device line ("Overjet Charting Assist") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K241684

  • recall_reason_pattern

    Software/algorithm-related recall in product code QIH (PIE Medical Imaging B.V., initiated 2026-05-07): "User measurement value reported by diagnostic bioimaging software intended to measure/visualize cardiovascular structures may be larger (0.8% to 34.4%) than the actual anatomical d" Recalling firm is another firm in the same product code.

    first seen 2026-08-26 · recall res_event_number:98930

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 2026-08-26.

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.

Related FDA clearances

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Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K233590