Search Results

Abys® Medical Cysware® 4H is intended for use as a software interface and image segmentation system for the transfer of medical imaging information to an output file. Abys® Medical Cysware® 4H is also intended as pre-operative software for surgical planning assistance. Abys® Medical Cysware® 4H is intended to be used by clinician with appropriate clinical judgement.

Abys® Medical Cysart@ 4H is a medical display intended for 3D image visualization and image interaction. The stereoscopic 3D images are generated from 3D volumetric data acquired from CT scan source. The device is intended to provide visual information to be used by clinical with appropriate clinical judgement for analysis of surgical options, and the intraoperative display of the images. Abys® Medical Cysart@ 4H is intended to be used as an adjunct to the interpretation of images performed using diagnostic imaging systems and is not intended for primary diagnosis. Abys® Medical Cysart® 4H is intended to be used as a reference display for consultation to assist the clinician with appropriate clinical judgement who is responsible for making all final patient management decisions.

Abys® Medical Cysware® 4H web platform is a web-based medical device designed and intended for use prior to surgery to gather in one place the information needed by the surgeon to make a surgical planning. As a result, a planning assistance file is created and contains medical imaging, 3d models, documents, and notes. The ABYS® MEDICAL Cysware® 4H web platform is used to export the planning assistance file to the Abys® Medical Cysart® 4H mixed reality application, another medical software.

The Abys® Medical Cysart® 4H mixed reality application is a medical device designed and intended for use in office room and in operating room to display and manipulate all documents in the planning assistance file generated from the Abys® Medical Cysware® 4H web platform.

Here's an analysis of the acceptance criteria and study information for Abys Medical's Cysware 4H and Cysart 4H devices, based on the provided text:

Acceptance Criteria and Device Performance Study

The FDA 510(k) summary provides details on the performance testing conducted for the Cysware 4H and Cysart 4H devices. The testing was non-clinical.

1. Table of Acceptance Criteria and Reported Device Performance

For Cysware 4H:

For Cysart 4H:

2. Sample Size Used for the Test Set and Data Provenance

The provided document does not explicitly state the sample size used for the non-clinical performance test set. It mentions "fifteen users," and "three users" for simultaneous connection tests for Cysware 4H, but not for the accuracy of measurements or segmentation where image data would be the primary "sample."

The data provenance is not explicitly mentioned (e.g., country of origin of data, retrospective or prospective). However, the general context is about software testing and validation against technical specifications rather than a clinical study on patient data from specific sources. The segmentation and mesh generation accuracy for Cysware 4H specifically mentions using "DICOM from CT scan source," but the origin of these CT scans is not provided.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

This information is not explicitly provided in the non-clinical performance data section. The testing described focuses on technical specifications and usability, rather than expert-derived ground truth on clinical diagnostic images. For measures like accuracy of segmentation, there would have been a "ground truth" for comparison, but the method of establishing it and the experts involved are not detailed.

4. Adjudication Method for the Test Set

An adjudication method (e.g., 2+1, 3+1) is not mentioned as the study described is non-clinical performance testing rather than a clinical study requiring adjudication of findings (like a diagnostic accuracy study).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The document states, "Clinical testing was not required to demonstrate substantial equivalence." Therefore, no effect size of how much human readers improve with AI vs. without AI assistance is provided.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

The performance tests for Cysware 4H's "Accuracy of Cysware 4H segmentation algorithm and Mesh generation" can be considered a standalone performance assessment of the algorithm's capability. The reported error of "lower than 1.25mm" against a ground truth (though not fully described) indicates a standalone evaluation.

7. The Type of Ground Truth Used

For the accuracy of measures (distance, angle) and segmentation accuracy of Cysware 4H, the ground truth would typically be reference measurements or segmentation derived from the input CT scan data. While the method of establishing this ground truth (e.g., expert consensus, manual annotation by a highly qualified individual, comparison to a gold standard software) is not explicitly detailed, it would inherently be a technical ground truth rather than pathological or outcomes data, as these are non-clinical hardware/software performance tests.

For Cysart 4H, the ground truth for parameters like refresh rate, autonomy, scale, connection time, display quality, and voice command efficacy are based on technical specifications and measurable operational performance criteria rather than clinical ground truth from patient data.

8. The Sample Size for the Training Set

The document does not provide information on the sample size used for the training set for any algorithms within Cysware 4H or Cysart 4H. It is stated that the software was developed, verified, and validated, implying standard software development and QA practices, but details on machine learning model training data are absent.

9. How the Ground Truth for the Training Set was Established

As no information on a training set or specific machine learning models requiring labeled training data is provided, how the ground truth for such a training set was established is not detailed.

Ask a specific question about this device