CoLumbo

K241211

Smart Soft Healthcare · cleared 2024-08-15 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“CoLumbo is a medical device (software) for viewing and interpreting magnetic resonance imaging (MRI) of the lumbar spine.”
AlgorithmDeep Convolutional Image-to-Image Neural Network
source quote (p.8)
“Deep Convolutional Image-to-Image Neural Network”
Adaptive (vs locked)No
source quote (p.5)
“The user is responsible for confirming segmentation and all measurement outputs. The output is an aid to the clinical workflow of measuring patient anatomy and should not be misused as a diagnosis tool. User-confirmed/defined settings control the sensitivity of the software for labelling measurements in an image. The user (not the software) controls the threshold for identifying out-of-range measurements, and, in every case once an out-of-range measurement is identified, the user must confirm or reject its presence.”
PCCPNo
Cybersecurity addressedYes
source quote (p.10)
“Smart Soft Healthcare 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. The vulnerability assessment and penetration testing demonstrates satisfactory security performance with no critical and high risk vulnerabilities.”

Validation studies (1)

Standalone

n=100 patients

standards: IEC 62304:2006/AMD 1:2015 Medical device software — Software life cycle processes — Amendment 1, IEC 82304-1:2016 Health software — Part 1: General requirements for product safety, ISO 14971:2019 Medical devices — Application of risk management to medical devices, IEC 62366-1:2015+AMD1:2020 Medical devices — Part 1: Application of usability engineering to medical devices, ISO 20417:2021 Medical devices — Information to be supplied by the manufacturer

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
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/K241211