(237 days)
Axial3D Cloud Segmentation Service is intended for use as a cloud-based service and image segmentation Framework for the transfer of DICOM imaging information from a medical scanner to an output file, which can be used for the fabrication of physical replicas of the output file using additive manufacturing methods.
The output file or physical replica can be used for treatment planning and/or diagnostic purposes in the field of orthopedic, maxillofacial, and cardiovascular applications in adults. The or physical replica may also be used for pediatrics between the ages of 12 and 21 years of age in cardiovascular applications.
Axial3D Cloud Segmentation Service should be used with other diagnostic tools and expert clinical judgment.
Axial3D Cloud Segmentation Service is a secure, highly available cloud-based image processing, segmentation, and 3D modelling framework for the transfer of imaging information either as a digital file or as a 3D printed physical model.
This document describes the Axial3D Cloud Segmentation Service and its FDA clearance.
Here's an analysis of the acceptance criteria and study data based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Stated or Implied) | Reported Device Performance |
|---|---|
| Measurement accuracy for segmentation | +/- 0.7mm |
| Validation for intended use | Successfully validated |
| Printing accuracy of physical replica models | Demonstrated to be accurate with compatible printers |
| Software requirements and risk analysis | Successfully verified and traced |
2. Sample Size Used for the Test Set and Data Provenance
The document does not explicitly state the sample size used for the test set in terms of the number of patient cases or images. It mentions "nonclinical testing" and "software design verification and validation testing on all three software components of the device."
The data provenance (country of origin, retrospective/prospective) is not specified in the provided text.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document does not specify the number of experts used or their qualifications for establishing ground truth for the test set.
4. Adjudication Method for the Test Set
The adjudication method used for the test set is not specified in the provided text.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size
The document does not mention an MRMC comparative effectiveness study or any effect size related to human readers improving with AI vs. without AI assistance. The device is a "segmentation framework" and its output (digital file or 3D printed model) is to be "used in conjunction with other diagnostic tools and expert clinical judgment." This suggests the device's role is preparatory rather than directly diagnostic in an unassisted workflow.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study Was Done
Yes, a standalone performance assessment was conducted for the algorithm's segmentation accuracy. The "measurement accuracy and comparisons were performed and confirmed to be within specification of +/- 0.7mm," indicating an evaluation of the algorithm's output against a reference.
7. The Type of Ground Truth Used
The type of ground truth used is not explicitly stated. However, given the measurement accuracy reported ("+/- 0.7mm") for segmentation, it implies a comparison against a highly precise reference, likely either a gold standard segmentation created by expert manual contouring or a known physical dimension.
8. The Sample Size for the Training Set
The sample size used for the training set is not specified in the provided text.
9. How the Ground Truth for the Training Set Was Established
How the ground truth for the training set was established is not specified in the provided text.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).