DeepCatch

K223556

MEDICALIP Co., Ltd · cleared 2023-06-16 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“DeepCatch is medial image processing software that provides 3D reconstruction and visualization of ROI, advanced image quality improvement, auto segmentation for specific target, texture analysis, etc.”
Algorithmanalyzes CT images and auto-segments anatomical structures
source quote (p.6)
“DeepCatch analyzes CT images and auto-segments anatomical structures (skin, bone, muscle, visceral fat, subcutaneous fat, internal organs and central nervous system).”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.11)
“"Content of Premarket Submission for Management of Cybersecurity in Medical Devices.", on October2, 2014”

Validation studies (5)

Retrospective clinical

n=100 cases

endpoints: DSC; Volume; Area; Ratio; Body Circumference

Retrospective clinical

n=580 cases

endpoints: DSC; Volume; Area; Ratio; Body Circumference

Retrospective clinical

n=167 images · 1 site(s)

endpoints: DSC; Volume; Area; Body Circumference

Retrospective clinical

n=100 images

endpoints: DSC

Retrospective clinical

n=100 images

endpoints: volume; ratio; area; body circumference

Reported performance (4 observations)

diceas written: “DSC (Internal Datasets)”stated without value
source quote (p.10)
“In the internal datasets test, DSC means of GT and segmentation results of DeepCatch shows greater than or equal to 90%.”
diceas written: “DSC (External Datasets)”stated without value
source quote (p.10)
“In the external data sets test, the DSC mean of GT and segmentation results of DeepCatch in all areas is more than 90%”
diceas written: “DSC (US-based data)”stated without value
source quote (p.10)
“for all anatomical structures, DSC mean was more than 90%”
diceas written: “DSC (Comparative with MEDIP PRO)”stated without value
source quote (p.11)
“Results showed that the DSC of DeepCatch was not inferior to that of MEDIP PRO. DeepCatch showed no difference in performance evaluations performed with MEDIP PRO, and showed better performance than MEDIP PRO for Muscle segmentation.”

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/K223556