FDA-cleared profile
3D-Side SA
Customize supports adult total ankle arthroplasty planning with CT segmentation, 3D models, implant positioning and sizing, plan review, and X-ray-informed planning.
Clinical fit
Start with the authorized purpose, then verify how it fits your service line and reading workflow.
Regulatory identity
The FDA listing establishes the regulatory identity. It does not by itself establish local workflow fit, pricing, security, or performance in your environment.
Implementation
Use these fields to structure a vendor demo, security review, and implementation estimate.
Public materials reviewed for this batch do not establish the complete security posture for the exact release. Request data-flow, identity, audit, encryption, retention, support-access, vulnerability-management, backup, and incident-response evidence.
Map the installed model, build, options, population, anatomy, inputs, and outputs to K221432 before acceptance.
exact submission · checked 2026-09-03Later product-family features are not evidence that this cleared release includes them.
DICOM CT and X-ray review, server case management, implant library, 3D CAD visualization, and downstream plan export; validate identifiers, model geometry, implant catalog, revision control, approval trace, and surgical availability.
exact submission · checked 2026-09-03Test representative identifiers, orientation, geometry, measurements, units, routing, failure handling, and round trips locally.
Use a signed local test set for the supported population and workflow. Include common and difficult cases, exclusions, acquisition variation, failed or low-quality inputs, reviewer overrides, and downstream effects. Not for diagnosis, spine surgery, or implant, instrument, or patient-specific-device design. Machine-learning segmentation is an initialization that requires engineer supervision and manual editing, followed by physician review.
exact submission · checked 2026-09-03Define stop-use, escalation, rollback, and revalidation triggers before production use.
Model capital and recurring cost against net staff time after review, throughput, repeat imaging or rework, room time, consumables, infrastructure, training, service, downtime, upgrade, and replacement costs.
product family · checked 2026-09-03Vendor productivity claims are scenario inputs, not guaranteed local savings.
Monitor by exact hardware, software and model version, patient population, anatomy, protocol, operator, input quality, output edits or overrides, failed studies, repeat acquisition or rework, downstream impact, downtime, incidents, recalls, and updates. Track the limits that matter for this product: Not for diagnosis, spine surgery, or implant, instrument, or patient-specific-device design. Machine-learning segmentation is an initialization that requires engineer supervision and manual editing, followed by physician review.
exact submission · checked 2026-09-03Revalidate after material software, hardware, model, protocol, interface, or clinical-policy changes.
Evidence
Metrics are shown only when they are tied to a source, endpoint, population, and tested product version.
Sensitivity is not the primary applicable endpoint for this acquisition, reconstruction, segmentation, planning, guidance, or quantitative-biomarker function. Use the task-specific endpoints and tested population reported below.
Specificity is not the primary applicable endpoint for this acquisition, reconstruction, segmentation, planning, guidance, or quantitative-biomarker function. Use the task-specific endpoints and tested population reported below.
Machine-learning initialization for ankle segmentation in Customize.
Tested version: Customize for ankle arthroplasty cleared in K221432Manufacturer nonclinical segmentation, repeatability, reproducibility, and 3D-model accuracy testing · 126 ankle CT scans with an average age of 62 years, 32% female and 68% male
exact submission · Tested version: Customize for ankle arthroplasty cleared in K221432 · n=126 · Sponsor or vendor studyK221432
Manufacturer evidence submitted for substantial equivalence; local clinical, technical, and workflow acceptance remains necessary.
Economics and lifecycle
These are common procurement questions; unknown values remain visible until a source supports them.
No public list price for the exact cleared configuration was found. Obtain a written quote separating hardware or license basis, enabled features, implementation, interfaces, infrastructure, training, service, upgrades, renewal, consumables, and exit costs.
No separate named-product payment identified in reviewed public material
Centers for Medicare & Medicaid Services · United StatesBuild a local total-cost and value model; do not infer reimbursement from FDA clearance.
Safety and lifecycle
Recall and adverse-event records are shown only after product matching. Adverse-event reports do not establish incidence or causality.
A zero-result exact-identifier search does not prove that no recall, correction, adverse-event report, or product-family safety signal exists.
Buyer worksheet
Research record
This audit distinguishes completed source review from fields that have not yet been researched.
Recovered from the prior exact-submission extraction and normalized under the current provenance rules. Exact FDA materials, current product-family sources, selected clinical literature, reimbursement context, and exact-identifier recall candidates were reviewed. Pricing, contracted services, enabled options, final security architecture, and local interoperability remain organization-specific evidence. Automated exact-name discovery found 20 PubMed and 10 ClinicalTrials.gov candidate records. Candidates require human product and version matching; zero candidates is not evidence that no studies exist. Native FDA recall identifiers produced 0 postmarket candidate records; 0 have been reviewed (0 published, 0 rejected) and 0 remain unreviewed.
Candidate leads remain unpublished until a human confirms the exact product and tested version.
Source classes: fda ai list, fda decision summary, vendor product page, reimbursement policy, fda device recall, pubmed, clinical trials, openfda device recall
Sources
Sources accessed through 2026-09-03.
Alternatives
Disior Ltd
Bonelogic 2.2 assists orthopedic healthcare professionals with diagnosis and surgical planning by semi-automatically segmenting bony structures from CT and producing foot and...
Disior Ltd
SMART Bun-Yo-Matic CT analyzes CT images of the foot and ankle to provide three-dimensional visualizations, measurements, and surgical-planning information for orthopaedic care.
Agada Medical, Ltd.
Auto-Seg and Spine Auto-Seg accept spine and pelvis CT images and provide three-dimensional segmentation and labels to support spine-surgery planning and assessment by spine...
Mighty Oak Medical
Acorn 3D Software and Acorn 3DP Model process CT, synthetic CT, and CT angiography for segmentation, visualization, measurement, treatment planning, and three-dimensional...