CT Cardiomegaly

K232613

Innolitics, LLC · cleared 2024-02-28 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“The CT Cardiomegaly application is a software only (SaMD) medical device which includes automated algorithms and non-adaptive machine learning to analyze chest computed tomography (CT) images.”
Algorithmnon-adaptive machine learning algorithm using the MONAI framework
source quote (p.3)
“CT Cardiomegaly is designed to measure the maximal transverse diameter of heart and maximal inner transverse diameter of thoracic cavity and calculate the CTR from an axial CT slice containing the heart using a non-adaptive machine learning algorithm. Another difference between the indications for use for both devices is that the predicate uses an artificial intelligence algorithm using an unknown framework while the subject device utilizes a non-adaptive machine learning algorithm using the MONAI framework.”
Adaptive (vs locked)No
source quote (p.3)
“CT Cardiomegaly is designed to measure the maximal transverse diameter of heart and maximal inner transverse diameter of thoracic cavity and calculate the CTR from an axial CT slice containing the heart using a non-adaptive machine learning algorithm.”
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Reader study (MRMC)

n=275 patients · 12 site(s)

endpoints: calculating the difference between the average measurement of the human readers and the subject device's measurement; precision of selecting the slice where the heart area is the largest (key heart slice detection); accuracy of heart and inner chest segmentations

Reported performance (2 observations)

diceas written: “Observed Dice (Heart Segmentation)”0.95CI [0.950, 0.956]
source quote (p.13)
“Heart 0.95 [0.950, 0.956]”
diceas written: “Observed Dice (Inner Chest Segmentation)”0.98CI [0.982, 0.984]
source quote (p.13)
“Inner Chest 0.98 [0.982, 0.984]”

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/K232613