OsteoDetect

DEN180005

Imagen Technologies, Inc. · granted 2018-05-24 · product code QBS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.2)
“OsteoDetect is a software device designed to assist clinicians in detecting distal radius fractures during the review of posterior-anterior (PA) and lateral (LAT) radiographs of adult wrists. The software uses deep learning techniques to analyze wrist radiographs (PA and LAT views) for distal radius fracture in adult patients. The device is a software-only device.”
Algorithmdeep learning techniques; Machine Learning System comprised of an Image Preprocessor module, Fracture Detector module (generates confidence score and conditional probability map), and Image Postprocessor module (determines fracture presence and bounding box coordinates).
source quote (p.2)
“The software uses deep learning techniques to analyze wrist radiographs (PA and LAT views) for distal radius fracture in adult patients. The Image Processing & Fracture Detection (Machine Learning System) processes eligible DICOM objects and analyses for the presence or absence of distal radius fractures. It is comprised of three modules: Image Preprocessor module – the image undergoes image preprocessing. Fracture Detector module – this module analyzes the processed image and generates two outputs: a confidence score (confidence for presence of distal radius fracture), and a conditional probability map encoding the location of the fracture (if present). Image Postprocessor module – determines whether a fracture is present based on the value of the confidence score and (if present) generates the coordinates of the fracture bounding box.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.7)
“The cybersecurity documentation is consistent with the recommendations for information that should be included in premarket submissions outlined in the FDA guidance document “Content of Premarket Submissions for Management of Cybersecurity in Medical Devices: Guidance for Industry and Food and Drug Administration Staff” (issued October 2, 2014). Information related to cybersecurity reviewed included: Hazard analysis related to cybersecurity risks; traceability documentation linking cybersecurity controls to risks considered; summary plan for validating software updates and patches throughout the lifecycle of the medical device; summary describing controls in place to ensure that the medical device will maintain its integrity; and device instructions for use and product specifications related to recommended cybersecurity controls appropriate for the intended use of the device.”

Validation studies (2)

Standalone

n=1,000 images

endpoints: Detection Accuracy (AUC, sensitivity, specificity, PPV, NPV); Localization Accuracy

Reader study (MRMC)

n=200 cases

endpoints: Superiority of aided reader diagnostic accuracy (AUC); Population-average sensitivity and specificity of aided and unaided reads

Reported performance (11 observations)

sensitivity0.803CI (0.785, 0.819)
source quote (p.16)
“OD-Aided 0.803 (0.785, 0.819)”
specificity0.914CI (0.903, 0.924)
source quote (p.16)
“OD-Aided 0.914 (0.903, 0.924)”
aurocas written: “auc”0.889
source quote (p.15)
“ROC AUC Full Model OD - Aided 0.889”
ppvas written: “Reader Study PPV (OD-Aided)”0.883CI (0.868, 0.896)
source quote (p.16)
“OD-Aided 0.883 (0.868, 0.896)”
npvas written: “Reader Study NPV (OD-Aided)”0.853CI (0.839, 0.865)
source quote (p.16)
“OD-Aided 0.853 (0.839, 0.865)”
aurocas written: “Standalone AUC”0.965CI 0.953, 0.976
source quote (p.11)
“The AUC of the ROC is 0.965 (95% confidence interval: 0.953, 0.976).”
sensitivityas written: “Standalone Sensitivity”0.921CI (0.886, 0.946)
source quote (p.11)
“Sensitivity 0.921 (0.886, 0.946)”
specificityas written: “Standalone Specificity”0.902CI (0.877, 0.922)
source quote (p.11)
“Specificity 0.902 (0.877, 0.922)”
ppvas written: “Standalone PPV”0.813CI (0.769, 0.850)
source quote (p.11)
“PPV 0.813 (0.769, 0.850)”
npvas written: “Standalone NPV”0.961CI (0.943, 0.973)
source quote (p.11)
“NPV 0.961 (0.943, 0.973)”
accuracyas written: “Localization Accuracy (pixels)”33.52
source quote (p.11)
“The average number of pixels between the centroids of the OsteoDetect model's predicted bounding box and that of the reference standard bounding box was 33.52”

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 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/DEN180005