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The DSA 2000ex device is used in vascular imaging applications. During X-ray exposures, the DSA 2000ex is used to acquire video images from the video display chain provided by the X-ray manufacturer's system. The images are stored in the DSA 2000ex solid state memory, and written to the hard disk medium. Images are processed in real-time to provide increased image usability. The processing is primarily subtraction, but also includes window and level adjustments, as well as optional noise reduction, landscaping, image rotation and pixel shifting. The Eigen DSA 2000ex device is used in X-ray cardiology and radiology labs to enhance diagnostic capabilities of radiologists, and cardiologists, with minimal intervention required by users to perform basic capture, playback, and archiving functions. Additional functions include allowing measurements to be made for quantizing stenosis and guidance of catheters in the Roadmapping mode.

The DSA 2000ex device is used in vascular imaging applications. During X-ray exposures, the DSA 2000ex is used to acquire video images from the video display chain provided by the X-ray manufacturer's system. The images are stored in the DSA 2000ex solid state memory, and written to the hard disk medium. Images are processed in real-time to provide increased image usability. The processing is primarily subtraction, but also includes window and level adjustments, as well as optional noise reduction, landscaping, image rotation and pixel shifting. The Eigen DSA 2000ex device is used in X-ray cardiology and radiology labs to enhance diagnostic capabilities of radiologists, with minimal intervention required by users to perform basic capture, playback, and archiving functions. Additional functions include allowing measurements to be made for quantizing stenosis and guidance of catheters in the Roadmapping mode.

The provided text describes the Eigen DSA 2000ex, a Digital Subtraction Angiography device, and its substantial equivalence to predicate devices. However, the document does not contain a detailed study with specific acceptance criteria, reported device performance metrics, or information about sample sizes, ground truth establishment, expert involvement, or MRMC studies that are typically associated with AI/ML device evaluations.

The relevant section, "Testing and Performance Data," states: "All product and engineering specifications were verified and validated. Test images as well as test phantoms incorporating simulated stenosis were developed and used to verify system performance through verification, validation and benchmarking." This is a very high-level statement and lacks the specificity required to answer the questions thoroughly.

Therefore, for aspects related to detailed performance studies and acceptance criteria as you've requested, the information is not available in the provided document.

Here's a breakdown of what can be extracted and what is not available:

1. Table of acceptance criteria and the reported device performance

The document mentions "product and engineering specifications were verified and validated," and "Test images as well as test phantoms incorporating simulated stenosis were developed and used to verify system performance." However, specific quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds for stenosis detection) and the corresponding measured performance values are not provided.

2. Sample size used for the test set and the data provenance

• Sample Size (test set): Not Available. The document mentions "test images" and "test phantoms incorporating simulated stenosis" but does not specify the number of these.
• Data Provenance: Not Available. Given the nature of "test images" and "test phantoms," these are likely internally generated or simulated, not clinical patient data from a specific country or retrospective/prospective study.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of experts: Not Available.
• Qualifications of experts: Not Available.

The document does not describe the establishment of ground truth by human experts for the test set.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Adjudication Method: Not Available. No information on expert review or adjudication is provided.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study: No. This is not an AI/ML device in the modern sense; it's an image processing system. Therefore, an MRMC study comparing human readers with and without AI assistance is not described or relevant for this type of device according to the provided text. The device "enhances diagnostic capabilities of radiologists, and cardiologists" by improving image usability, but this is through image processing, not an AI-driven diagnostic aid.
• Effect Size: Not Applicable/Not Available.

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

• Standalone Study: Not explicitly described. The device itself is an image processing system, which operates in a "standalone" fashion to process images. However, a formal "standalone performance study" with metrics like sensitivity/specificity for a diagnostic task, as would be expected for an AI algorithm, is not detailed. The system's "performance" is verified against engineering specifications and test phantoms, implying system-level functional performance rather than diagnostic accuracy.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Type of Ground Truth: "Test phantoms incorporating simulated stenosis." This suggests an engineered, known truth set for evaluating the device's ability to process and visualize specific features. It's not clinical ground truth derived from pathology or patient outcomes.

8. The sample size for the training set

• Sample Size (training set): Not Applicable/Not Available. This device is an image processing system, not an AI/ML device that undergoes "training" in the contemporary sense. It's built based on established algorithms for image subtraction, noise reduction, etc., not trained on a dataset.

9. How the ground truth for the training set was established

• Ground Truth Establishment (training set): Not Applicable/Not Available. As it's not an AI/ML device that requires training, the concept of a training set ground truth does not apply. The algorithms are predefined based on image processing principles rather than learned from data.

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The 3-D Imaging Workstation is intended to be used by physicians in the clinic or hospital for 2-D and 3-D visualization of ultrasound images of the prostate gland. Additional software features include patient data management, multi-planar reconstruction, segmentation, image measurement and 3-D image registration.

The 3-D Imaging Workstation is designed to display the 2-D live video received from commercially available ultrasound machines and use this 2-D video to reconstruct a 3-D ultrasound image. The system has been designed to work with the clinicians' existing ultrasound machine, end fire TRUS probe, commercially available needle guide and needle gun combination. Additional software features include patient data management, multiplanar reconstruction, segmentation, image measurement and 3-D image registration.

The 3-D Imaging Workstation is comprised of a mechanical assembly that holds the ultrasound probe and tracks probe position while the physician performs a normal ultrasound imaging procedure of the subject prostate. The mechanical tracker is connected to a PC-based workstation containing a video digitizing card and running the image processing software. Control of the ultrasound probe and ultrasound system is done manually by the physician, just as it would be in the absence of the 3-D Imaging Workstation. However, by tracking the position and orientation of the ultrasound probe while capturing the video image, the workstation is able to reconstruct and display a 3-D image and 3-D rendered surface model of the prostate.

The reconstructed 3-D image can be further processed to perform various measurements including volume estimation, and can be examined for abnormalities by the physician. Patient information, notes, and images may be stored for future retrieval.

Locations for biopsies may be selected by the physician, displayed on the 3-D image and 3-D rendered surface model, and stored. Previously stored 3-D models may be recalled and a stored 3-D model may be aligned or registered to the current 3-D model of the prostate.

Finally, the physician may attach a commercially available biopsy needle guide to the TRUS probe and use the probe and biopsy needle to perform tissue biopsy. Whenever the ultrasound machine is turned on by the physician, the live 2-D ultrasound image is displayed on the screen of 3-D Imaging Workstation during the biopsy. As the TRUS probe with attached needle guide is maneuvered by the physician, the position and orientation of the probe is tracked. The 3-D Imaging Workstation is able to add, display and edit plans for biopsy sites as well as an estimate of the probe position and needle trajectory relative to the 3-D image and 3-D rendered surface model of the prostate.

The 3-D Imaging Workstation offers the physician additional 3-D information for assessing prostate abnormalities, planning and implementing biopsy procedures. The additional image processing features are generated with minimal changes to previous TRUS probe based procedures, and the physician always has access to the live 2-D ultrasound image during prostate assessment or biopsy procedure.

Here's an analysis of the acceptance criteria and study information for the 3-D Imaging Workstation, based on the provided text:

Important Note: The provided 510(k) summary is very high-level and does not detail specific acceptance criteria or quantitative performance metrics typically found in a robust validation study report. Instead, it focuses on demonstrating "substantial equivalence" to predicate devices. Therefore, much of the requested information cannot be extracted directly from this document. The answers below reflect what can be found or inferred from the text.

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria (Inferred from "Substantial Equivalence" claim): The device's performance characteristics (e.g., image measurement, multi-planar reformatting, segmentation, image registration, image storage/retrieval, patient information management) are sufficiently similar to the predicate devices (3-Dnet Suite K063107 and XELERIS 2 Processing and K051673) to achieve "substantial equivalence" for its intended use. While no specific quantitative thresholds are stated, the implication is that the performance meets industry standards and is clinically acceptable for its intended purpose.

2. Sample size used for the test set and the data provenance

• Sample Size: Not specified in the provided text. The testing involved "Test phantoms incorporating simulated prostates."
• Data Provenance: The device was tested using "Test phantoms incorporating simulated prostates." This implies the data was generated in a controlled, artificial environment rather than derived from human patient data. There is no mention of country of origin, retrospective, or prospective data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. It's unclear if human experts were involved in establishing ground truth for the phantom studies, as phantoms often have known, measurable properties that can serve as ground truth directly.

4. Adjudication method for the test set

• Adjudication Method: Not specified.

5. If a multi-reader multicase (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study: No, a multi-reader multicase (MRMC) comparative effectiveness study was not mentioned or described. This study focuses on validating the device's functionality and its "substantial equivalence" to predicate devices, not on comparing reader performance with and without the device. The device is a "3-D Imaging Workstation," not an AI-assisted diagnostic tool in the sense of directly altering human reader performance outcomes in an MRMC study.

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

• Standalone Performance: The study described uses "Test phantoms" for "verification, validation... and benchmarking." This type of testing would primarily evaluate the algorithm's performance in reconstructing images, calculating volumes, and tracking, independent of real-time human interaction with live patient data for diagnostic decision-making. Therefore, a form of standalone performance evaluation on simulated data was conducted for the technical aspects of the software. However, it's not a standalone diagnostic performance reported with metrics like sensitivity/specificity for disease detection on clinical data.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Type of Ground Truth: The ground truth for the test set was based on the known properties of "Test phantoms incorporating simulated prostates." This implies a known physical standard rather than expert consensus, pathology, or outcomes data from human subjects.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. The document describes a 510(k) submission for a medical imaging workstation, not a machine learning or AI algorithm development process that typically involves a distinct training set. The device's functionality is based on established image processing algorithms, not a trainable model.

9. How the ground truth for the training set was established

• Ground Truth for Training Set: Not applicable. As noted above, the device does not appear to be an AI algorithm developed with a training set in the conventional sense. Its ground truth for validation was based on physical phantoms.

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