Ortho AI

K241696

Ortho AI LLC · cleared 2025-01-02 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Ortho AI is a software as a medical device (SaMD) system that provides preoperative planning data for hip replacement surgery, knee replacement surgery, and lumbar spinal fusion surgery using AI/ML models that are semi-automated and interpretable.”
AlgorithmAI/ML models that are semi-automated and interpretable
source quote (p.6)
“Ortho AI is a software as a medical device (SaMD) system that provides preoperative planning data for hip replacement surgery, knee replacement surgery, and lumbar spinal fusion surgery using AI/ML models that are semi-automated and interpretable.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (3)

Standalone

n=1,367 patients

endpoints: LLD measurements within +/- 1.96mm of human measurement; Offset (global) within +/- 0.88mm of human measurement; SFP angle within +/- 1.05mm of human measurement; Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.

Standalone

n=4,836 patients

endpoints: SS, SPT, APPt, PI, LL, PI-LL – all within 2 degrees of human measurement; No statistical difference between human vs. machine learning measurements; Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.

Standalone

n=4,536 patients

endpoints: LDFA, mPTA, aHKA, aJLOA all within 2 degrees of human measurement; No statistical difference between human vs. machine learning measurements; Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.

Reported performance (3 observations)

diceas written: “Dice coefficient (Hip model)”0.85
source quote (p.9)
“Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.”
diceas written: “Dice coefficient (Hip-spine model)”0.85
source quote (p.9)
“Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.”
diceas written: “Dice coefficient (Knee model)”0.85
source quote (p.10)
“Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 0.85.”

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
3
MAUDE reports in code, 12mo
—
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/K241696