i2Contour

K233822

MRIMath LLC · cleared 2024-08-08 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“MRIMath i2contour is intended for the semi-automatic labeling, visualization, and volumetric quantification of WHO grade 4 glioblastoma (GBM) from a set of standard MRI images of male or female patients 18 years of age or older who are known to have pathologically proven glioblastoma.”
AlgorithmAI-powered segmentation
source quote (p.8)
“AI-powered segmentation of the magnetic resonance images (MRI) of patients diagnosed with glioblastoma multiforme is the technological principle for both the subject and predicate devices.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedNo

Validation studies (2)

Reader study (MRMC)

n=33 patients

endpoints: mean DSC

Reader study (MRMC)

n=46 patients · 19 site(s)

endpoints: DSC proportions; mean overall DICE scores; Sensitivity; Specificity; mean Hausdorff distances; volume measurements; kappa scores; Bland-Altman differences; inter-user variability

Reported performance (10 observations)

sensitivity0.927
source quote (p.9)
“Sensitivity and specificity for T1c AI were 92.7% and 97.2%, respectively.”
specificity0.972
source quote (p.9)
“Sensitivity and specificity for T1c AI were 92.7% and 97.2%, respectively.”
diceas written: “FLAIR AI DSC proportions exceeding p0”0.85CI (72%, 92%)
source quote (p.9)
“For the FLAIR AI, the DSC proportions exceed p0, 85% of the time, with a confidence interval (CI) of (72%, 92%) and p-value of <0.001, implying that our proportion is significantly different than 50%.”
diceas written: “T1C AI DSC proportions exceeding p0”0.93CI (82%, 98%)
source quote (p.9)
“For T1C, the DSC proportions exceed p0, 93% of the time, with a CI of (82%, 98%) and p-value of <0.001 of the time, implying that our proportion is significantly different than 50%.”
diceas written: “mean overall DICE scores for T1c AI”0.95CI (93%, 96%)
source quote (p.9)
“The mean overall DICE scores for the post-contrast T1 (T1c) AI were 0.95 with a 95% confidence interval (C.I) of (93%, 96%), closely matching the radiologists' scores.”
diceas written: “mean DSC for true positive T1c images”0.83
source quote (p.9)
“For true positive T1c images, AI segmentation scored a mean DSC of 83%, versus radiologists' ranging from 76% to 86%.”
diceas written: “FLAIR AI mean DSC”0.92CI (90%, 94%)
source quote (p.9)
“The FLAIR AI mean DSC was 92% with a 95% CI interval of (90%, 94%), also matching the radiologists scores.”
sensitivityas written: “median sensitivity for FLAIR AI”0.934
source quote (p.9)
“The AI also achieved a mean DICE score of 80% for true positive FLAIR slices, against the radiologists' 75%-83%, and exhibited a median sensitivity and specificity of 93.4% and 98.6%, respectively.”
specificityas written: “median specificity for FLAIR AI”0.986
source quote (p.9)
“The AI also achieved a mean DICE score of 80% for true positive FLAIR slices, against the radiologists' 75%-83%, and exhibited a median sensitivity and specificity of 93.4% and 98.6%, respectively.”
diceas written: “mean DICE score for true positive FLAIR slices”0.8
source quote (p.9)
“The AI also achieved a mean DICE score of 80% for true positive FLAIR slices, against the radiologists' 75%-83%, and exhibited a median sensitivity and specificity of 93.4% and 98.6%, respectively.”

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/K233822