Customize
3D-Side SA • Radiology • 03/16/2022
Device Purpose
The "Customize" software facilitates pre-operative planning for shoulder arthroplasty by enabling physicians to create and manipulate patient-specific 3D anatomical models from CT scans and simulate implant placement using a digital library. Its methodology leverages medical image processing for segmentation and CAD technology for surgical simulation, with its performance validated by studies on segmentation quality, reproducibility, and 3D model accuracy.
Summary
Customize is a software system intended for the pre-operative planning of shoulder arthroplasty in adult patients. It assists clinicians by processing CT-scan DICOM data to generate 3D CAD models, allowing for visualization, modification, and validation of surgical plans, including implant selection from an integrated library and optimized positioning, while also aiding in treatment communication and case management. The system's science is based on medical image processing for accurate 3D anatomical model segmentation and CAD technology for surgical simulation, enabling precise manipulation of implants relative to anatomy. Its effectiveness is supported by performance data from software verification and validation, including studies on segmentation accuracy, repeatability, reproducibility for shoulder anatomies, and 3D model generation accuracy for the humerus and scapula; a distinguishing feature is its distance map tool for estimating inter-object distances.
AI Regulatory Concerns
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Keywords
Device Metadata
Relevant Clinical Questions
- Q1. How does the software's segmentation accuracy for complex shoulder pathologies, such as severe glenoid bone loss, compare to its performance on standard anatomies?; What is the typical time required for a clinician to generate a complete surgical plan using Customize, from CT data import to final plan validation?; Can the Customize software integrate with intra-operative navigation systems or robotic platforms for shoulder arthroplasty?; What are the specific metrics and tolerances used to define and validate the 'accuracy study on 3D model generation for humerus and scapula'?; How does the 'distance map' feature account for soft tissue presence or compression when estimating distances between bony structures or implants, and what is its validated accuracy?
Risk Classification
ESL AnalysisFDA Class II via 510(k) pathway; IEC 62304 Class B (software safety classification); Authorized before §524B enforcement
Learn how Parasoft automates IEC 62304 compliance testing →Cybersecurity Evidence
Powered by SBOMator™Based on AI-generated device summaries, not the full FDA submission. Items not detected here may still be present in the complete submission package.
Supply Chain BOM Evidence
Based on the CycloneDX / OWASP ECMA-424 xBOM ecosystem. Detection is from the AI-generated summary.
Analysis:
• Analysis is based on the AI-generated device summary, not the full FDA submission package. Click "Analyze Full FDA PDF" below to scan the actual submission document.
• Evidence of SaaSBOM detected in AI-generated device summary.
• This device was authorized before §524B took effect. Cybersecurity evidence requirements were less prescriptive at the time.
• Postmarket cybersecurity monitoring plan not clearly documented in AI-generated device summary.
• Not detected in AI-generated device summary: SBOM, ML-BOM, HBOM, CBOM, VDR/VEX, OBOM. These may be present in the full FDA submission.
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