ART-PLAN

K220813

TheraPanacea · cleared 2022-06-17 · product code QKB · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“ART-Plan is a software application intended to display and visualize 3D multi-modal medical image data. The user may import, define, display, transform and store DICOM3.0 compliant datasets (including regions of interest structures). These images, contours and objects can subsequently be exported/distributed within the system, across computer networks and/or to radiation treatment planning systems. Supported modalities include CT, PET-CT, CBCT, 4D-CT and MR images. ART-Plan supports AI-based contouring on CT and MR images and offers semi-automatic and manual tools for segmentation.”
AlgorithmAI-based contouring, deep-learning based automatic segmentation
source quote (p.5)
“ART-Plan supports AI-based contouring on CT and MR images and offers semi-automatic and manual tools for segmentation. ART-Plan offers deep-learning based automatic segmentation for the following localizations:”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (20)

Retrospective clinical

sample size not stated

endpoints: Mean DSC of each organ was compared with the tolerance threshold of 0.8.

standards: AAPM requirements

Retrospective clinical

sample size not stated

endpoints: non-inferiority of using Annotate's pseudo-CT for treatment planning in terms of dosimetric measures as compared to CT-based treatment planning.

Retrospective clinical

sample size not stated

endpoints: non-inferiority of using Annotate's pseudo-CT for treatment planning in terms of dosimetric measures as compared to CT-based treatment planning.

Retrospective clinical

sample size not stated

endpoints: high generalizability of the commercial tool, initially made for adults, to pediatric cases and its clinical implementation feasibility.

Retrospective clinical

sample size not stated

endpoints: acceptable contours on MR brain structures.

Retrospective clinical

sample size not stated

endpoints: acceptable contours for a specific list of gynecological structures on a Female pelvis CT image.

Reader study (MRMC)

sample size not stated

endpoints: clinically acceptable (compared to inter-expert variability) for all MR-T1 Brain structures.

Standalone

sample size not stated

endpoints: annotation of organs as compared to other devices which have been cleared for use in the US.

Retrospective clinical

sample size not stated

endpoints: performance of auto segmentation is demonstrated

Bench

sample size not stated

endpoints: usability test results for the ART-Plan v1.10.0 for compliance with IEC 62366-1:2015+AMD1:2020 Medical devices Application of usability engineering to medical devices.

standards: IEC 62366-1:2015+AMD1:2020

Retrospective clinical

sample size not stated

endpoints: quality of the rigid and the deformable fusion algorithms

Retrospective clinical

sample size not stated

endpoints: quality of ITV calculation algorithm

Retrospective clinical

sample size not stated

endpoints: performances of the SmartFuse module for the clinical case of fusion of an MRI towards a planning CT

Retrospective clinical

sample size not stated

endpoints: performances of the SmartFuse module for fusion of CTs towards planning MRIs

Retrospective clinical

sample size not stated

endpoints: performances of the SmartFuse module on the clinical case of using fusion for MRI replannification.

Retrospective clinical

sample size not stated

endpoints: quality of the rigid and the deformable fusion algorithms of the SmartFuse module for replanification of CT-based treatments.

Retrospective clinical

sample size not stated

endpoints: acceptable contours for structures of the thorax region: thoracic aorta and bronchial trees.

Retrospective clinical

sample size not stated

endpoints: acceptable contours for 9 organs evaluated on MR Truefisp images of patients.

Retrospective clinical

sample size not stated

endpoints: acceptable contours for following cervical lymph nodes levels

Retrospective clinical

sample size not stated

endpoints: clinically acceptable contours

Reported performance (2 observations)

diceas written: “Dice Similarity Coefficient (DSC) (mean)”stated without value
source quote (p.22)
“The Dice Similarity Coefficient (DSC) is equal to or superior to the acceptance criteria set by the AAPM: DSC (mean)≥ 0.8.”
diceas written: “Dice Similarity Coefficient (DSC) (mean) vs inter-expert variability”stated without value
source quote (p.22)
“The Dice Similarity Coefficient (DSC) is equal to or superior to inter-expert variability: DSC (mean)≥ 0.54 or DSC (mean) ≥ mean (DSC inter-expert) + 5%”

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
0
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
1
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K232479 (decision 2023-12-22) from TheraPanacea for a matching device line ("ART-Plan") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K232479

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/K220813