MOZI TPS

K223724

Manteia Technologies Co., Ltd. · cleared 2023-07-10 · product code MUJ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“The proposed device, MOZI Treatment Planning System (MOZI TPS), is a standalone software which is used to plan radiotherapy treatments (RT) for patients with malignant or benign diseases. MOZI TPS is a software device and is not supplied sterile because the device doesn't come in contact with the patient. MOZI TPS is a software device and does not have a Shelf Life.”
AlgorithmMonte Carlo method for dose calculation, Intensity based for rigid and deformable registration, Deep learning method for automatic contouring
source quote (p.4)
“dose calculation with Monte Carlo method of plan design and optimization And it also uses deep learning method for automatic contouring of structure delineation. Auto rigid registration algorithms Intensity based Auto deformable registration algorithms Intensity based Auto segmentation algorithms Deep learning”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedYes
source quote (p.13)
“The cybersecurity of the subject device has been comprehensively risk assessed and tested, and has traceability. Corresponding supporting documents are provided in this submission.”

Validation studies (7)

Bench

sample size not stated

standards: FDA Quality System Regulation (21 CFR Part 820), ISO 13485 Quality Management System standard, IEC 62304 Software Life Cycle standard, ISO 14971 Risk Management Standard

Bench

sample size not stated

standards: AAPM TG-119 Report

Bench

sample size not stated

Bench

sample size not stated

endpoints: Normalized Mutual Information (NMI); Hausdorff Distance (HD)

Bench

sample size not stated

standards: IEC 62083-2009 Standard

Bench

n=18 patients

Bench

n=187 images

endpoints: Mean Dice Similarity Coefficient (DSC)

Reported performance (5 observations)

diceas written: “Mean DSC values (Brain)”0.98
source quote (p.11)
“Brain 0.98 0.01”
diceas written: “Mean DSC values (Brainstem)”0.88
source quote (p.11)
“Brainstem 0.88 0.03”
diceas written: “Mean DSC values (BrachialPlexus L)”0.61
source quote (p.11)
“BrachialPlexus L 0.61 0.05”
diceas written: “Mean DSC values (Lung_L)”0.99
source quote (p.12)
“Lung_L 0.99 0.00”
diceas written: “Mean DSC values (OpticNerve_L)”0.61
source quote (p.12)
“OpticNerve_L 0.61 0.07”

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

35
recalls in product code, 24mo
17
MAUDE reports in code, 12mo
-50%
vs code's own 3-yr baseline
2
drift signals on this device
  • recall_reason_pattern

    Software/algorithm-related recall in product code MUJ (Philips Medical Systems (Cleveland) Inc, initiated 2025-08-05): "Due to a software issue, there is a potential image error of the Region of Interest for expansion/contraction for HFP (Head First Prone), FFS (Feet First Supine) and FFP (Feet Firs" Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:97049

  • recall_reason_pattern

    Software/algorithm-related recall in product code MUJ (Philips Medical Systems (Cleveland) Inc, initiated 2025-07-17): "Due to software issue, Radiation Therapy Planning system may provide incorrect dataset calculations when performing the "Stopping Power Ratio" (SPR) ," Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:97309

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/K223724