Ortho AI
K241696Ortho AI LLC · cleared 2025-01-02 · product code QIH · Radiology
Premarket evidence — what FDA accepted
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.”
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.”
source quote (p.6)
“The software guides the user through a predetermined workflow that begins with the use of preoperative radiographic images as input to the software. As part of this initial preoperative workflow, the software places digital annotations on these preoperative images, which can be modified by the user (semi-automated). The software additionally includes functionality to store user, patient, and case information. The Ortho AI software will semi-automate the above listed tasks by taking a radiological image as input and delivering key measurements of the surgeon's interest through an AI algorithm. These landmarks are then editable by the user.”
Validation studies (3)
Retrospective clinical
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.
Retrospective clinical
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.
Retrospective clinical
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)
source quote (p.9)
“Overall performance as measured by Dice coefficient shows all connected domains to be above our acceptance criteria of 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.”
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
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). 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).