Search Results

The intended use of the JET software is to get high-resolution anatomical images with minimal artifacts due to physiologic motion which can interfere with image quality. This technique can also provide the improvements on signal-to-noise with eliminating or reducing the image degradation caused by the motions.

Imaging of:

Any anatomies that can have the artifact caused by the physiologic motion such as:.

• CSF flow .
• Abdominal or chest wall movement due to the breathing .
• Respiratory movement on shoulders .
• Uncontrolled movements due to the stroke, pediatric or uncooperative patients ●

JET software provides high-resolution anatomical images with minimal artifacts due to physiological motion, which is caused by movement of anatomical regions due to breathing, CSF flow or other involuntary movements and can interfere with image quality.

The provided text is a 510(k) Premarket Notification for the Toshiba America Medical Systems, Inc. JET Software. The document focuses on establishing substantial equivalence to a predicate device and does not contain information about specific acceptance criteria, a detailed study proving performance against acceptance criteria, or most of the other requested details regarding the study design and ground truth establishment.

Here's a breakdown of the information that can be extracted and the information that is missing:

The submission is primarily focused on demonstrating substantial equivalence to a previously cleared device (K063361), implying that the performance is expected to be similar or better without a need for a new comprehensive performance study against specific acceptance criteria. The approval indicates the FDA found the device substantially equivalent based on the information provided, which likely includes technical comparisons rather than a full clinical performance study with defined acceptance criteria and statistical analysis against those criteria.

1. A table of acceptance criteria and the reported device performance

• Acceptance Criteria (Extrapolated from Intended Use/Device Description):

  • Produce high-resolution anatomical images.
  • Minimize artifacts due to physiological motion (e.g., CSF flow, breathing, uncontrolled movements).
  • Improve signal-to-noise ratio by eliminating or reducing image degradation caused by motions.

• Reported Device Performance: The document states that the "Algorithm was modified to support a new combination of image acquisition and reconstruction techniques." It also claims that the software is expected to "get high-resolution anatomical images with minimal artifacts due to physiologic motion... This technique can also provide the improvements on signal-to-noise with eliminating or reducing the image degradation caused by the motions."

  • Direct performance metrics against explicit acceptance criteria are NOT provided in the submission. The submission is a justification of substantial equivalence, not a detailed performance study with quantitative results.

Missing Information (Not Available in the Provided Text):

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not specified. The document does not describe a "test set" in the context of a performance study.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable/Not specified. Ground truth establishment for a test set is not discussed.

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

• Not applicable/Not specified.

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

• Not specified. The document focuses on the software's image quality improvement, not on its impact on human reader performance in a comparative effectiveness study.

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

• Not explicitly described as a standalone study with quantitative metrics. The device itself is software (an algorithm), and its intended function is to improve image quality. The claim of "high-resolution anatomical images with minimal artifacts" and "improvements on signal-to-noise" implies a standalone performance, but no specific study design or results are provided.

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

• Not specified. Given the nature of the device (image quality improvement for motion artifacts), ground truth would likely involve expert visual assessment of artifact reduction and image clarity, but this is not detailed.

8. The sample size for the training set

• Not specified. The document does not mention a "training set" for the algorithm, as it predates the common terminology for deep learning models. It discusses algorithm modifications and new acquisition/reconstruction techniques.

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

• Not applicable/Not specified. No training set is mentioned in the context of ground truth.

Ask a specific question about this device