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The Hysteroscopy System is indicated for rapid transcervical aspiration of the uterine cavity during the first trimester of pregnancy. It has an additional feature of visualization.

The Hysteroscopy System is designed to visualize the uterus and to facilitate physicians in the procedure of vacuum aspiration of the uterine cavity during the first trimester of pregnancy. Components of the Hysteroscopy System include an EOsterilized single-use cannula and reusable image processing unit. The Hysteroscopy System is available in five sizes that differ in length and width. Each cannula includes an internal channel to draw vacuum or negative pressure up to 500 mmHg. The optical system, which includes a CMOS image sensor, lens, and 40 mW LED, is located at the distal end of the cannula. A lens cover shields the electronics from bodily fluids. The Hysteroscopy System has two connectors: one is an electrical/data connection and the second connection is for vacuum. The cannula is not indicated for use with any other accessories. The patient-contacting materials are medical grade stainless steel, polycarbonate, medical glue, and medical nylon.

This document does not contain an acceptance criteria table or a detailed study of device performance to meet such criteria. It is a 510(k) summary for a Hysteroscopy System. While it lists various performance tests conducted, it does not provide quantitative acceptance criteria or the specific results demonstrating compliance.

Here's a breakdown of what is available based on your request, and what is missing:

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

Missing. The document lists performance tests but does not define acceptance criteria (e.g., minimum tensile strength, maximum leak rate, specific image resolution targets) nor does it present the numerical results from these tests against any defined criteria. It only states, "The protocol and results of all performance testing were acceptable."

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

Missing. The document mentions various performance tests (e.g., biocompatibility, electrical safety, mechanical performance, image quality) but does not provide details on:
* The sample sizes used for any of these tests.
* The provenance of the data (e.g., country of origin, retrospective or prospective nature). These tests appear to be bench testing or material-level assessments, not clinical trials on human subjects.

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)

Missing. This type of information would typically be relevant for studies involving diagnostic accuracy or clinical interpretation. The performance tests described are largely engineering and safety evaluations (e.g., tensile strength, vacuum integrity, electrical safety, biocompatibility, image quality parameters like field of view, resolution). There's no indication of any expert-based ground truth establishment in the context usually meant by this question (e.g., for image interpretation or diagnosis).

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

Missing. Adjudication methods are typically used in clinical studies where multiple observers or methods are used to establish a definitive diagnosis or outcome. The performance tests listed are technical validations, not clinical outcome adjudications.

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

Missing. An MRMC study is not mentioned. The device is a "Hysteroscopy System" used for visualization and vacuum aspiration. There is no mention of AI or human-in-the-loop performance improvement in this document, as it describes a medical device, not an AI software.

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

Missing. Not applicable, as this document describes a physical medical device (Hysteroscopy System), not an algorithm or AI software requiring standalone performance testing in that context.

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

Missing. As noted above, the performance tests described are largely technical/engineering validations. For example, for "image quality testing," the ground truth would be objectively measured parameters such as actual field of view, photometric measurements for illumination, and resolution targets. For "mechanical performance testing," ground truth would be defined by engineering specifications (e.g., expected tensile strength, vacuum pressure). There's no mention of clinical "ground truth" derived from expert consensus, pathology, or outcomes data.

8. The sample size for the training set

Missing. This is not an AI device, so there is no concept of a "training set" for an algorithm.

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

Missing. Not applicable, as there is no training set for an AI algorithm.

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For transcervical aspiration of the uterine cavity.

Not Found

I am sorry, but the provided text does not contain information about the acceptance criteria and the study details for a device. The document is an FDA 510(k) premarket notification letter for "Panpac Disposable Vacuum Curettes," indicating its substantial equivalence to a predicate device. It defines the device's indications for use but does not include:

1. A table of acceptance criteria and reported device performance.
2. Sample size or data provenance for a test set.
3. Number or qualifications of experts used for ground truth.
4. Adjudication method for a test set.
5. Information on a Multi-Reader Multi-Case (MRMC) comparative effectiveness study or effect size.
6. Results from a standalone algorithm-only performance study.
7. The type of ground truth used (e.g., pathology, outcomes data).
8. Sample size for a training set.
9. How ground truth for a training set was established.

Therefore, I cannot fulfill your request based on the provided input.

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The Disposable Endoscopic Cannula is indicated for rapid transcervical aspiration of the first trimester of pregnancy. It has an additional feature of visualization.

The Disposable Endoscopic Cannula is a sterile, single patient use device composed of transparent cover, suction orifice, dual-function cannula, handle, suction port, connection port, and data cable. The patient contacting components of this device are transparent cover, suction orifice and dual function cannula that are manufactured with polycarbonate.

The transparent cover contains a LED light source and CMOS camera at the distal end for visualization of the cervical canal and uterine cavity. This device has two channels, one for transcervical aspiration of the uterine cavity and the other for transmitting the image signal through a cable connected to the Endoscopic Workstation. The images captured by the camera are transmitted to the endoscopic workstation through the cable for display. When the embryo tissues are located, the operator sucks out the embryo tissues through the suction orifice connected to a vacuum source.

The Disposable Endoscopic Cannula is provided with five sizes based on outer cannula diameter: 5mm (RL1205 model), 6mm (RL1206 model), 7mm (RL1207 model), 8mm (RL1208 model), and 9mm (RL1209 model) (outer diameter). All models have a length of 170mm.

This document is a 510(k) premarket notification for a medical device called the "Disposable Endoscopic Cannula". It is not a study demonstrating machine learning algorithm performance. Therefore, I cannot extract the requested information about acceptance criteria and a study proving device performance in the context of AI/ML.

The document focuses on demonstrating substantial equivalence to a predicate device (Rigid Uterine Cannulae) for the purpose of FDA clearance. This involves comparing the new device's features, intended use, technology, and safety aspects to a legally marketed predicate device.

Here's what the document does provide regarding acceptance criteria and performance, but it's for a traditional medical device, not an AI system:

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

The document doesn't provide a direct table of acceptance criteria with corresponding performance values in the way you might expect for an AI algorithm's metrics (e.g., sensitivity, specificity). Instead, it states that the device is in "compliance with requirements" for various standards.

Here's a summary of the compliance stated:

The remaining points (2-9) are not applicable because this document describes a hardware medical device, not an AI or machine learning system. There are no test sets, training sets, ground truth, experts for annotation, or multi-reader multi-case studies described. The "performance" referred to is about the physical and electrical integrity and functionality of the device, not the accuracy of an algorithm.

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For uterine aspiration/uterine evacuation in obstetric and gynecological patients. Indications for use are rapid transcervical aspiration of the uterine cavity during the first trimester of pregnancy.

The Rigid Cannulae are injection molded plastic devices approximately 190mm in length, made from a styrenic copolymer manufactured in sizes 6mm. 7mm. 8mm. 9mm. 10mm, 11mm, and 12mm (outer diameter), straight and curved.

The provided text describes a 510(k) submission for "Rigid Uterine Cannulae." This type of device is a physical instrument, not an AI/ML powered medical device. Therefore, the questions related to AI/ML device performance (such as acceptance criteria for AI, sample sizes for test/training sets, experts for ground truth, or MRMC studies) are not applicable to this submission.

The 510(k) summary focuses on demonstrating substantial equivalence to predicate devices through comparisons of design, materials, and bench testing for physical strength and vacuum performance.

The questions you've asked are relevant for AI/ML powered medical device submissions, which typically involve software that processes data (like medical images) to generate an output. This submission describes a physical medical tool.

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