(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.
§ 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).