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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

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.

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