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510(k) Data Aggregation
(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.
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(30 days)
Axial3D Cloud Segmentation Service is intended for use as a cloud based service and image segmentation system for the transfer of DICOM imaging information from a medical scanner to an output file.
The Axial3D Cloud Segmentation Service output file 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.
The physical replica can be used for diagnostic purposes in the field of orthopedic, maxillofacial and cardiovascular applications.
Axial3D Cloud Segmentation Service should be used in conjunction with other diagnostic tools and expert clinical judgment.
Axial3D Cloud Segmentation Service is a secure, highly available cloud based image processing, segmentation and 3D modeling framework for the transfer of imaging information to either a digital file or as a 3D printed physical model.
Axial3D Cloud Segmentation Service is made up of a number of component parts, which allow the production of patient-specific 1:1 scale replica models, either as a digital file or as a 3D printed physical model.
Here's a breakdown of the acceptance criteria and study information for the Axial3D Cloud Segmentation Service, based on the provided text:
1. Acceptance Criteria and Reported Device Performance
The provided document describes a substantial equivalence submission to the FDA. In this context, the "acceptance criteria" are implied by the claim of substantial equivalence to the predicate device, Mimics InPrint (K173619). The primary performance metric is the accuracy of the segmentation against a defined ground truth, demonstrating that the subject device performs at least as well as the predicate and within acceptable specifications.
| Performance Metric | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|
| Measurement Accuracy of Segmentation | "within specification" and "performs at least as well as the legally marketed predicate device." | "Measurement accuracy and comparisons were performed and confirmed to be within specification." |
| "The validation highlighted that the subject device performed to a higher standard, than the predicate device." | ||
| "minimal variances were visible between the Mesh generated from subject device and the predicate device" | ||
| Accuracy of Physical Replica Printing | Demonstrated to be accurate when using compatible 3D printers. | "Validation of printing of physical replica models was performed and demonstrated to be accurate when using any of the compatible 3D printers." |
| Equivalence in Design & Functionality | Similar to predicate device in intended use, design, functionality, operating principles, and performance characteristics. | "Comparison shows the Axial3D Cloud Segmentation Service is substantially equivalent in intended use, design, functionality, operating principles and performance characteristics of the predicate device." |
| "Both devices use the same segmentation functionality and generate the same output files." | ||
| Safety & Effectiveness | As safe and effective as the legally marketed predicate device. | "The conclusions drawn from the nonclinical tests demonstrate that the proposed subject device is as safe, as effective, and performs as well as the legally marketed predicate device." |
| Minimal Variance from Original DICOM (Mesh) | Minimal variance from original DICOM images after smoothing. | "Axial3D apply minimal smoothing to the STL file generated from the labeled images to retain a higher level of accuracy to the original DICOM images." |
2. Sample Size and Data Provenance
- Test Set Sample Size: Not explicitly stated in the provided text. The document mentions "measurement accuracy and comparisons were performed" and "validation of printing of physical replica models was performed," but does not detail the number of cases or images used for these tests.
- Data Provenance: Not explicitly stated. The document does not mention the country of origin of the data or whether it was retrospective or prospective.
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: Not explicitly stated.
- Qualifications of Experts: Not explicitly stated. The document refers to "expert clinical judgment" in the Indications for Use, which suggests clinical experts are involved in the overall use of the device, but it doesn't specify their role or qualifications in establishing the ground truth for the validation study.
4. Adjudication Method
- Adjudication Method: Not explicitly stated.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not mentioned or described in the provided text. The study focuses on the comparison of the device's output (segmentation accuracy) against a predicate device, not on the improvement of human readers' performance with AI assistance.
6. Standalone (Algorithm Only) Performance
- Standalone Performance: Yes, a standalone performance study was seemingly conducted. The description states "Measurement accuracy and comparisons were performed" for the device's segmentation output, and "The validation highlighted that the subject device performed to a higher standard, than the predicate device." This indicates an assessment of the algorithm's performance independent of human-in-the-loop interaction for the specific tasks evaluated.
7. Type of Ground Truth Used
- Type of Ground Truth: The document implies comparison against the output of the predicate device ("Mimics InPrint") as a reference for accuracy, and also refers to "original DICOM images" for mesh accuracy. It doesn't explicitly state whether expert consensus or pathology was used to establish the gold standard for the initial segmentation accuracy comparison. Given the nature of segmentation, it is highly probable that expert-annotated segmentations were used as ground truth, or a method accepted as gold standard in the field for anatomical model creation.
8. Sample Size for Training Set
- Training Set Sample Size: Not explicitly stated. The document focuses on the validation and verification of the device, not its development or training data.
9. How Ground Truth for Training Set Was Established
- Ground Truth for Training Set: Not explicitly stated. As with the test set, it's not detailed how ground truth was established for any potential training data used to develop the segmentation algorithms.
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