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510(k) Data Aggregation
(16 days)
BIOTRONICS3D, LTD.
The 3Dnet Suite is intended to be used by physicians for the display of 2D/3D visualization of DICOM compliant medical image data, such as CT, MRI, and Ultrasound scans.
The 3Dnet Suite provides several levels of functionality to the user:
- basic analysis tools they use on a daily basis such as 2D review, orthogonal multiplanar reconstructions (MPR), oblique MPR, curved MPR, Slab MPR AvgIP, MIP, MinIP, measurements, annotations, reporting, distribution etc.
- tools for in-depth analysis, such as segmentation, endoscopic review, color VR slab, grayscale VR slab, 3D volume review, path definition and boundary detection etc.
- Specialist tools and workflow enhancements for specific clinical applications which provide target workflows, custom UI, targeted measurement and visualization, including colon screening which is indented for the screening of patients for colonic polyps, tumors and other lesions using tomographic colonography.
The 3Dnet Suite is a software device for evaluating scanned images of selected human organ. The basic visualization module of 3Dnet Suite is Examiner.
The Examiner allows the processing, review, analysis, communication and media interchange of multi dimensional digital images acquired from a variety of imaging devices.
It provides multi-dimensional visualization of digital images to aid clinicians in their analysis of anatomy and pathology. The 3Dnet user interface follows typical clinical workflow patterns to process, review and analyze digital images including:
- Retrieve image data over the network via DICOM
- Select images for closer examination from a gallery of 2D and 3D views
- Interactively manipulate an image in real time to visualize anatomy and pathology
- Annotate, tag, measure and record selected views
- Output selected views to DICOM and JPEG files or expert views to another DICOM device.
Here's an analysis of the provided text regarding the acceptance criteria and study for the 3Dnet Suite device:
Acceptance Criteria and Device Performance for 3Dnet Suite
This 510(k) summary focuses on demonstrating substantial equivalence to predicate devices for the 3Dnet Suite, a medical image processing software system. As such, the "acceptance criteria" are primarily established through a comparison of functionalities and a demonstration that the new device performs those functions equivalently and safely. Formal, quantitative performance metrics with specific thresholds (like accuracy, sensitivity, specificity percentages) are not explicitly stated as distinct acceptance criteria that the device must meet in a numerical sense. Instead, the acceptance criteria are implicitly met by:
- Matching/exceeding functionalities of predicate devices.
- Demonstrating accurate processing and visualization of medical images.
- Ensuring the software operates reliably and consistently.
- Adhering to software development standards.
The study described is a non-clinical test performed for the determination of substantial equivalence.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Implied) | Reported Device Performance (from "Discussion of non-clinical tests") |
|---|---|
| Functional Equivalence: Display 2D/3D visualization of DICOM compliant medical image data (CT, MRI, Ultrasound). | The application provided interactive orthogonal and multiplanar reformatted 2D and 3D image from datasets to detect and evaluate known abnormalities or status of organs. |
| Accurate Measurement & Analysis: Provide measurement tools (volume, linear, angular) for analysis of observed structures. | The volume, linear and angular measurements features, provided in the software, were used to evaluate and quantify any abnormality of organs or status of any internal organ structures. Accuracy correlated "perfectly" with pre-calculated values for phantom datasets. |
| Reliability & Usability: Operate reliably, be easy to use, and capable of evaluating DICOM compliant scanned images. | The product has shown itself to be reliable, easy to use and capable of evaluating DICOM compliant scanned images of any human organs. |
| Software Quality: Developed consistent with accepted standards for software development. | The 3Dnet Suite has been developed in a manner consistent with accepted standards for software development, including both unit and system integration testing protocols. |
| DICOM Conformance: Validate DICOM functionality with other compliant applications and tools. | The DICOM functionality with regards to DICOM SOP classes as stated in the DICOM conformance statement was validated with a number of other DICOM compliant applications and DICOM validation tools as part of the development and testing process. |
| Safety & Effectiveness: Pose no new questions of safety or effectiveness compared to predicate devices. | "We conclude from these tests that 3Dnet Suite is substantially equivalent to the predicated devices in its ability to evaluate any human organs." and "3Dnet Suite does not raise any new questions of safety or effectiveness." |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: "Scanned image datasets of various patient organs with known abnormalities or status" and "phantom datasets." The exact number of patient datasets or phantom datasets is not specified in the provided text.
- Data Provenance:
- Patient Data: "Patients Image Data in our installations in Europe." This indicates a European origin. The text does not explicitly state if this data was retrospective or prospective, but the phrasing "known abnormalities or status" suggests it was existing, retrospective data used for testing.
- Phantom Data: Origin not specified, but likely internally generated or standard phantoms.
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 on the patient or phantom datasets. It refers to "known abnormalities or status" for patient data and "pre-calculated values" for phantom data, implying an established ground truth, but the method of establishment or the experts involved are not detailed.
4. Adjudication Method for the Test Set
The document does not specify any formal adjudication method (e.g., 2+1, 3+1). The testing appears to be an internal verification process against "known" or "pre-calculated" ground truth.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No, a MRMC comparative effectiveness study was not done. The submission focuses on demonstrating substantial equivalence through functional comparison and non-clinical testing, not a comparative study with human readers with and without AI assistance.
6. If a Standalone Study (Algorithm Only, Without Human-in-the-Loop Performance) Was Done
Yes, a standalone study was done. The "discussion of non-clinical tests" describes the device's performance against "known abnormalities or status" and "pre-calculated values" using phantom and patient datasets without mention of human interaction in the direct performance evaluation. The device is designed for physicians to use, but the testing described here evaluates the software's inherent ability to process, visualize, and measure.
7. The Type of Ground Truth Used
- For Patient Data: "Known abnormalities or status" (e.g., presence, location, and characteristics of polyps, tumors, or other lesions). This implies a clinical ground truth likely established by expert interpretation or, potentially, pathology/surgical findings, though not explicitly stated.
- For Phantom Data: "Pre-calculated values." This is an objective, engineered ground truth based on the design specifications of the phantom.
8. The Sample Size for the Training Set
The document does not mention a training set or its sample size. This device is described as an "Image processing system" or "Medical Image Processing software system" which provides visualization and analysis tools. There is no indication that it employs machine learning or AI that would require a distinct training set in the modern sense. The "development" and "testing process" mentioned relate to software engineering principles rather than AI model training.
9. How the Ground Truth for the Training Set Was Established
As no training set is mentioned for an AI/ML context, this question is not applicable. The software development process mentioned involves "unit and system integration testing protocols," which use "known abnormalities or status" (for patient data) and "pre-calculated values" (for phantom data) as verification and validation ground truth.
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