Medical Image Post-processing Software (uOmnispace.CT)

K242624

Shanghai United Imaging Healthcare Co., Ltd. · cleared 2025-05-14 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“uOmnispace.CT is a software for viewing, manipulating, evaluating and analyzing medical images.”
Algorithmdeep-learning algorithm
source quote (p.7)
“Introduce deep-learning algorithm in applications of Lung Density Analysis, Vessel Analysis, Heart, Liver Evaluation and Cardiovascular Combined Analysis.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.22)
“These documentations include: ... Cybersecurity Documents”

Validation studies (5)

Retrospective clinical

n=100 cases

endpoints: Dice coefficient for lung segmentation; Dice coefficient for airway segmentation

standards: NEMA PS 3.1 - 3.20 Digital Imaging and Communications in Medicine (DICOM) Set (2023e), ISO 14971 Medical devices - Application of risk management to medical devices (Third Edition 2019-12), IEC 62304 Medical device software - Software life cycle processes (Edition 1.1, 2015)

Retrospective clinical

n=156 cases

endpoints: Dice coefficient for bone removal (Abdomen & Limbs); Dice coefficient for bone removal (Head & Neck)

standards: NEMA PS 3.1 - 3.20 Digital Imaging and Communications in Medicine (DICOM) Set (2023e), ISO 14971 Medical devices - Application of risk management to medical devices (Third Edition 2019-12), IEC 62304 Medical device software - Software life cycle processes (Edition 1.1, 2015)

Retrospective clinical

n=72 cases

endpoints: Dice coefficient for coronary artery extraction; Dice coefficient for heart chamber segmentation

standards: NEMA PS 3.1 - 3.20 Digital Imaging and Communications in Medicine (DICOM) Set (2023e), ISO 14971 Medical devices - Application of risk management to medical devices (Third Edition 2019-12), IEC 62304 Medical device software - Software life cycle processes (Edition 1.1, 2015)

Retrospective clinical

n=74 cases

endpoints: Dice coefficient for liver segmentation; Dice coefficient for hepatic artery segmentation

standards: NEMA PS 3.1 - 3.20 Digital Imaging and Communications in Medicine (DICOM) Set (2023e), ISO 14971 Medical devices - Application of risk management to medical devices (Third Edition 2019-12), IEC 62304 Medical device software - Software life cycle processes (Edition 1.1, 2015)

Retrospective clinical

n=80 cases

endpoints: Dice coefficient for hepatic portal vein segmentation; Dice coefficient for hepatic vein segmentation

standards: NEMA PS 3.1 - 3.20 Digital Imaging and Communications in Medicine (DICOM) Set (2023e), ISO 14971 Medical devices - Application of risk management to medical devices (Third Edition 2019-12), IEC 62304 Medical device software - Software life cycle processes (Edition 1.1, 2015)

Reported performance (10 observations)

diceas written: “Dice Similarity Coefficient (lung segmentation)”0.9801
source quote (p.24)
“lung segmentation 0.9801”
diceas written: “Dice Similarity Coefficient (airway segmentation)”0.8954
source quote (p.24)
“airway segmentation 0.8954”
diceas written: “Dice Similarity Coefficient (Bone removal (Abdomen & Limbs))”0.96957
source quote (p.25)
“Bone removal (Abdomen & Limbs) 0.96957”
diceas written: “Dice Similarity Coefficient (Bone removal (Head & Neck))”0.955
source quote (p.25)
“Bone removal (Head & Neck) 0.955”
diceas written: “Dice Similarity Coefficient (Coronary artery extraction)”0.916
source quote (p.27)
“Coronary artery extraction 0.916”
diceas written: “Dice Similarity Coefficient (Heart chamber segmentation)”0.97
source quote (p.27)
“Heart chamber segmentation 0.970”
diceas written: “Dice Similarity Coefficient (Liver segmentation)”0.981
source quote (p.29)
“Liver segmentation 0.981”
diceas written: “Dice Similarity Coefficient (Hepatic artery segmentation)”0.927
source quote (p.29)
“Hepatic artery segmentation 0.927”
diceas written: “Dice Similarity Coefficient (Hepatic portal vein segmentation)”0.933
source quote (p.30)
“Hepatic portal vein segmentation 0.933”
diceas written: “Dice Similarity Coefficient (Hepatic vein segmentation)”0.914
source quote (p.30)
“Hepatic vein segmentation 0.914”

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/K242624