MSKai

K240793

MSKai · cleared 2024-12-16 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“MSKai is a medical device (software) for inspecting and evaluating T2-weighted magnetic resonance imaging (MRI) of the lumbar spine. The software is an imaging interpretation tool that assists radiologists and neuro/ortho spine surgeons (“users”) to identify and measure lumbar spine features in medical images and document their interpretations in a report. The segmentation and measurements are classified using "alerts” based on rule-based algorithms. The user also identifies and classifies any other observations that the software may not annotate. SaMD Yes”
AlgorithmMask Region-based Convolutional Neural Network
source quote (p.8)
“Mask Region-based Convolutional Neural Network”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.11)
“Remedy Logic conforms to the cybersecurity requirements by implementing a process of preventing unauthorized access, modifications, misuse or denial of use, or the unauthorized use of information that is stored, accessed or transferred from a medical device to an external recipient, per FDA Guidance for Industry and Food and Drug Administration Staff, Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions, issued on September 27, 2023, as well as FDA Guidance for Industry and Food and Drug Administration Staff, Postmarket Management of Cybersecurity in Medical Devices, issued on December 28, 2016. The vulnerability assessment and penetration testing demonstrated satisfactory security performance.”

Validation studies (1)

Standalone

n=238 patients · 5 site(s)

endpoints: maximum Mean Absolute Error (MAE) below a predetermined allowable error limit; minimum Mean Dice Coefficient (MDC) above a predetermined allowable limit

standards: IEC 62304:2006/AMD 1:2015, ISO 14971:2019, IEC 62366-1:2015+AMD1:2020, ISO 15223-1:2016, NEMA PS 3.1 - 3.20 (2016)

Reported performance (1 observation)

diceas written: “Mean Dice Coefficient (MDC) for Vertebral Body (L1) Sagittal”0.968CI 0.92-0.98
source quote (p.14)
“Vertebral Body (L1) Sagittal 0.968 0.92-0.98 0.8”

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/K240793