(36 days)
4D Sono-Scan 1.0 is intended to analyze digital ultrasound images for computerized 3-dimensional and 4-dimensional (dynamic 3D) image processing.
The 4D Sono-Scan 1.0 reads certain digital 3D/4D image file formats for reprocessing to a proprietary 3D/4D image file format for subsequent 3D/4D tomographic reconstruction and rendering. It is intended as a general purpose digital 3D/4D ultrasound image processing tool.
4D Sono-Scan 1.0 intended as software for reviewing 3D/4D data sets and perform basic measurements in 3D.
The 4D Sono-Scan 1.0 is a clinical application package for high performance PC platforms based on Microsoft® Windows® operating system standards. 4D Sono-Scan 1.0 is proprietary software for the analysis, storage, retrieval, reconstruction and rendering of digitized ultrasound B-mode images. The data can be acquired by ultrasound machines that are able to acquire and store 4D datasets (i.e. Toshiba Aplio XG or Zonare Z.ONE). The digital 3D/4D data can be used for basic measurements like areas, distances and volumes.
4D Sono-Scan 1.0 is compatible to different TomTec Image-Arena platforms and their derivatives (i.e. Zonare IQ Workstation) for offline analysis. The platform enhances the workflow by providing the database, import, export and other advanced high-level research functionalities. All analyzed data and images will be transferred to the platform for reporting and statistical quantification purposes via the Generic CAP Interface.
The Generic CAP (= clinical application packages) Interface is used to connect clinical application packages (=CAPs) to platforms to exchange digital medical data.
The provided text describes the 4D Sono-Scan 1.0 device, its intended use, and a general statement about its testing. However, it does not explicitly define acceptance criteria in a quantifiable table format, nor does it detail a specific study with statistical results to prove the device meets such criteria.
The document refers to verification and validation documentation (Chapter 16), which would typically contain such information, but these chapters are not included in the provided text.
Based on the information available:
1. Table of Acceptance Criteria and Reported Device Performance:
No explicit table of acceptance criteria or specific quantifiable performance metrics are provided in the given text. The document generally states that "The overall product concept was clinically accepted and the clinical test results support the conclusion that the subject device is as safe as effective, and performs as well as the predicate devices."
2. Sample Size Used for the Test Set and Data Provenance:
- Test Set Sample Size: Not specified.
- Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
Not specified.
4. Adjudication Method for the Test Set:
Not specified.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
No mention of an MRMC study or any effect size comparing human readers with and without AI assistance is provided. The device is software for analyzing existing ultrasound images and performing measurements, not directly an AI-assisted diagnostic tool for human readers in the sense of improving their diagnostic accuracy.
6. Standalone (Algorithm Only) Performance:
The document states, "Software testing and validation were done at the module and system level according to written test protocols established before testing was conducted." This implies standalone testing of the software's functionality (e.g., ability to reprocess, reconstruct, render, and perform basic measurements), rather than a clinical performance study with human subjects. However, no specific performance metrics for this standalone testing are provided beyond the general statement that test results support its safety and effectiveness compared to predicates.
7. Type of Ground Truth Used:
While not explicitly stated, for a device performing basic measurements on ultrasound images, the "ground truth" for testing would likely involve:
- Comparison of software measurements against manually performed measurements (e.g., by experts) on the same images.
- Comparison of software-generated 3D/4D reconstructions against expected anatomical structures or other validated reconstruction methods.
- Verification of the software's ability to accurately read and reprocess different 3D/4D image file formats.
8. Sample Size for the Training Set:
Not applicable. The 4D Sono-Scan 1.0 is described as a "clinical application package" and "proprietary software for the analysis, storage, retrieval, reconstruction and rendering of digitized ultrasound B-mode images." It performs "basic measurements like areas, distances and volumes." There is no indication that this device uses machine learning or AI models that require a separate "training set" in the conventional sense. It appears to be a rule-based or algorithmic software for image processing and quantification.
9. How the Ground Truth for the Training Set Was Established:
Not applicable, as there is no indication of a training set for machine learning. The software's functionality would have been developed and verified against known principles of image processing, geometry, and engineering specifications.
§ 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).