United Orthopedic Knee Patient Specific Instrumentation

K230850

Enhatch, Inc. · cleared 2023-12-20 · product code OOG · Orthopedic

Premarket evidence — what FDA accepted

Device typehardware with ml
source quote (p.6)
“The United Orthopedic Knee Patient Specific Instrumentation is comprised of: United Orthopedics (UO) surgical guides (hardware), anatomical models (physical replica), Intelligent Surgery Knee CT Segmentation Engine / Intelligent Surgery Knee X-ray Segmentation Engine (software), and Intelligent Surgery Knee Implant Recognition Engine (software).”
Algorithmdeep learning algorithms
source quote (p.6)
“The Intelligent Surgery Knee CT Segmentation Engine and X-ray Segmentation Engine are web applications that use deep learning algorithms to detect and extract region of interest (ROI) information (femur and tibia) from medical imaging data (DICOM).”
Adaptive (vs locked)No
source quote (p.6)
“Note, these algorithms are static and non-adaptive; they do not alter their behavior over time based on user input.”
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.11)
“FDA Guidance on Content of Premarket Submissions for Management of Cybersecurity in Medical Devices.”

Validation studies (5)

Bench

sample size not stated

endpoints: verify and validate the accuracy of the software generated segmentation masks for CT and X-ray based DICOM data

standards: ISO 14971, IEC 62304:2006 & A1:2016, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices, FDA Guidance on Content of Premarket Submissions for Management of Cybersecurity in Medical Devices

Bench

sample size not stated

endpoints: verify and validate the accuracy of the software generated 3-dimensional models from CT and X-ray based DICOM data

standards: ISO 14971, IEC 62304:2006 & A1:2016, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices, FDA Guidance on Content of Premarket Submissions for Management of Cybersecurity in Medical Devices

Bench

sample size not stated

endpoints: verify and validate the accuracy of landmark detection, system usage and accuracy

standards: ISO 14971, IEC 62304:2006 & A1:2016, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices, FDA Guidance on Content of Premarket Submissions for Management of Cybersecurity in Medical Devices

Bench

sample size not stated

endpoints: verify and validate the amount of wear debris generated from use of the surgical guides

standards: ISO 14971, IEC 62304:2006 & A1:2016, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices, FDA Guidance on Content of Premarket Submissions for Management of Cybersecurity in Medical Devices

Bench

sample size not stated

endpoints: verify and validate the amount overall system performance in terms of system usage, and accuracy

standards: ISO 14971, IEC 62304:2006 & A1:2016, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices, FDA Guidance on Content of Premarket Submissions for Management of Cybersecurity in Medical Devices

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

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
41
MAUDE reports in code, 12mo
-4%
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 Orthopedic 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/K230850