(52 days)
Table is a software application used for the display and 3D visualization of medical image files from scanning devices such as CT and MRI. It is intended for use by radiologists, clinicians, referring physicians and other qualified individuals to retrieve, process, render, review, and assist in diagnosis, utilizing standard PC hardware.
This device is not indicated for mammography use.
Table is a volumetric imaging software designed specifically for clinicians, doctors, physicians, and other qualified medical professionals. The software runs in Windows operating systems and visualizes medical imaging data on the computer screen. Users are able to examine anatomy on a computer screen and use software tools to move and manipulate images by turning, zooming, flipping, adjusting contrast and brightness, cutting, and slicing using either touch control or a mouse. The software also has the ability to perform measurements of angle and length. There are multiple tools to annotate and otherwise mark areas of interest on the images. Additionally, Table has the ability to demonstrate pathology examples of patient data for educational purposes.
The provided 510(k) summary (K140093) describes the Anatomage Table as a volumetric imaging software for 3D visualization of medical image files (CT, MRI) for diagnosis assistance.
No specific quantitative acceptance criteria are explicitly stated in the provided document. The device's performance is primarily established through a qualitative comparison to a predicate device and general confirmation of stability and designed operation.
Here's a breakdown of the requested information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Qualitative Equivalence to Predicate Device: The software should be as effective as its predicate in essential functions. | "Testing confirms that Table is as effective as its predicates in its ability to perform its essential functions of measurement and rendering of DICOM data." |
| Stability and Operating as Designed: The software should function reliably and as intended. | "Testing confirmed that the software is stable and operating as designed." |
| Hazard Evaluation and Risk Reduction: Identified hazards should be evaluated, and risks reduced to acceptable levels. | "Testing also confirmed that the software has been evaluated for hazards and that risk has been reduced to acceptable levels." |
| Accuracy of Measurement Tools: Essential linear and angular measurements should be accurate. | "This testing included testing of measurement tools in both predicate and subject software..." (Implied accuracy through expert evaluation) |
| Rendering of DICOM Data: The software should accurately visualize DICOM data. | "...and rendering of DICOM data." (Implied accuracy through expert evaluation) |
2. Sample Size Used for the Test Set and Data Provenance
The document states "Bench testing of the software with predicate software was performed by evaluation of images rendered by Table and predicate software." However, it does not specify the sample size (number of images or cases) used for this test set nor the data provenance (e.g., country of origin, retrospective or prospective nature of the data).
3. Number of Experts and their Qualifications for Ground Truth
The testing and evaluation of the bench test were performed by "an expert in the field of radiology." Only one expert is mentioned. The document does not provide further specific qualifications (e.g., years of experience) for this expert.
4. Adjudication Method for the Test Set
The document mentions evaluation by "an expert." This suggests a single-expert assessment rather than a multi-expert adjudication method (like 2+1 or 3+1). Therefore, the adjudication method appears to be "none" in the sense of multiple experts resolving discrepancies.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The evaluation described is a bench test comparing the software's output to a predicate, assessed by a single expert. There is no mention of human readers improving with AI assistance or without.
6. Standalone Performance Study
Yes, a standalone performance study was done in the sense that the "Table" software's performance was evaluated independently in a "bench testing" scenario, comparing its outputs (rendered images and measurements) with those of a predicate software. This evaluation focused on the algorithm's capabilities without explicit human-in-the-loop performance measurement.
7. Type of Ground Truth Used
The ground truth for the test set was implicitly established through comparison to the predicate software's output and evaluation by a single radiology expert. This leans towards an expert-based assessment of accuracy and equivalence rather than pathology or outcomes data.
8. Sample Size for the Training Set
The document does not provide any information regarding a training set sample size. This suggests that if machine learning was used (which is not explicitly clear for this type of software described), the details of its training are not included in this summary. Given the description, it appears to be more of a deterministic image processing and visualization software rather than an AI/ML-driven diagnostic algorithm.
9. How Ground Truth for the Training Set Was Established
As no training set is mentioned or detailed, the document does not describe how ground truth for a training set was established.
§ 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).