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The FemVue™ Saline-Air Device instills a consistent alternating pattern of saline and air as a continuous stream of contrast media into the uterus and fallopian tubes to be used in conjunction with an intrauterine catheter for performance of sono-hysterosalpingogram (Sono HSG).

The FemVue™ Saline-Air Device is a dual-barrel contrast media syringe that can be connected to an intrauterine catheter to instill saline-air contrast media during sono-hysterosalpingogram (Sono HSG) procedures. Sono HSG consists of an ultrasound evaluation of the fallopian tubes with or without assessment of the uterine cavity.

The FemVue™ Saline-Air Device, as described in the K110288 510(k) submission, is a physical medical device, specifically a contrast media syringe, rather than an AI/ML powered medical device. Therefore, many of the requested categories related to AI/ML device studies (e.g., sample size for test set, data provenance, ground truth establishment, MRMC studies, standalone algorithm performance, training set details) are not applicable to this submission.

However, based on the provided text, the acceptance criteria and the study performed can be described as follows:

1. Table of Acceptance Criteria and Reported Device Performance

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

Not applicable for a physical device where the "test set" would typically refer to a dataset for an AI/ML algorithm. The testing described is non-clinical performance testing of the physical hardware itself. The document does not specify a numerical sample size for the devices tested, nor does it refer to data provenance in the context of clinical data.

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

Not applicable. This is a non-clinical performance study of a physical device. Ground truth, in the context of expert review, is not relevant here. The "ground truth" for these tests is the physical and mechanical functionality of the device against predefined performance targets.

4. Adjudication method for the test set

Not applicable. Adjudication methods are typically used when subjective assessments by multiple experts need to be reconciled for establishing ground truth in clinical or image-based studies. This submission describes non-clinical performance testing.

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 applicable. This is not an AI/ML device, and thus no MRMC study or AI assistance evaluation was performed or is relevant.

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

Not applicable. This is not an AI/ML algorithm.

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

The "ground truth" for the non-clinical tests would have been the pre-defined engineering specifications and performance targets for the device's mechanical and fluidic functionalities. For example, "continuous and consistent stream of an alternating pattern of saline and air" would have specific measurable parameters that the device was designed to meet.

8. The sample size for the training set

Not applicable. This is a physical medical device, not an AI/ML algorithm requiring a training set.

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

Not applicable. As above, this is a physical device, and there is no training set as understood in AI/ML development.

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