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The Stylus is intended to assist in the placement of Low-Profile Gastrostomy Feeding Tube or Gastrostomy Button by providing temporary feeding tube stability during insertion through patient stoma tract.

The Stylus (may also be referred as Stylet, Introducer, Obturator or Stiffener) is an Introducer for the Low-Profile Gastrostomy Feeding Tube or Gastrostomy Button (LPGFT/B - K122030) originally cleared in July 2012 which was developed at Degania Silicone Ltd. The Stylus is made of Polycarbonate (Macrolon®) and has various ranges of lengths, colors and weights. The Stylus is inserted all the way into the Feeding Port of the LPGFT/B port of the cleared gastrostomy device. The use of the Stylus is optional, it provides stiffness to the LPGFT/B shaft to guide the LPGFT/B through the stoma. The Stylus should not exceed the buttons distal tip. The Stylus is being sterilized by Ethylene Oxide (EtO) along with a compatible Low Profile Gastrostomy Feeding Tube/ Gastrostomy Button kit, rather than a standalone device.

The provided text describes the "Stylus" device, which assists in placing gastrostomy feeding tubes. The 510(k) summary (K233591) details its regulatory information and a brief overview of testing. However, it does not contain the detailed information necessary to answer all sections of your request regarding acceptance criteria and study specifics.

Based on the available text, here's what can be extracted and what remains unknown:

1. Table of Acceptance Criteria and Reported Device Performance

The document mentions "Bench Performance Testing" that included:

• Tensile strength testing
• Stylus stiffness testing
• Dimensional testing
• Gastrostomy compatibility testing

The reported performance conclusion is: "The results show that the device is at least as [effective as] a legally marketed predicate device."

However, the specific numerical acceptance criteria or the quantitative results for these tests are not provided in the given text. A table cannot be generated without these details.

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

The document states, "Bench Performance Testing included tensile strength testing, styls stiffness testing dimensional testing, and gastrostomy corpatibilitytesting." However, no details about the sample size used for these tests or the data provenance (e.g., country of origin, retrospective/prospective) are provided.

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

This information is not mentioned in the provided text. The testing described appears to be bench testing, not involving human interpretation or ground truth established by experts.

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

This information is not mentioned in the provided text. Adjudication methods are typically relevant for studies involving human assessment or interpretation, which is not described for this bench 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

An MRMC study is not mentioned in the provided text. The device described, the Stylus, is a physical introducer for feeding tubes, not an AI-powered diagnostic or assistive tool for human readers.

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

This is not applicable as the Stylus is a physical medical device, not an algorithm or software. The testing mentioned is device performance testing, not algorithm performance testing.

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

For the bench performance tests mentioned (tensile strength, stiffness, dimensions, compatibility), the "ground truth" would be the engineering specifications and established physical measurement standards relevant to the device's function and comparison to the predicate. The document doesn't explicitly state "ground truth" in this context but implies a comparison to predicate device performance.

8. The sample size for the training set

The Stylus is a physical device and not an AI/ML model, so there is no training set in the context of machine learning. The testing described is physical performance testing.

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

As there is no training set for this type of device, this question is not applicable.

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The Aquarius™ Stoma Measuring Device is designed to determine the depth (length) of a well-established gastrostomy stoma tract in order to assist in proper length selection of the Aquarius™ Gastrostomy Button (G-Button). Intended for transient use (less than 60 minutes).

The Aquarius™ Stoma Measuring Device comprises a 12 Fr with graduated scale, a funnel with an inflation valve and a tubular shaft retaining balloon. The Aquarius™ Stoma Measuring Device is made of 100% silicone, EtO sterilized and for single use. Intended to be use in hospital environment.

The provided text describes the Aquarius™ Stoma Measuring Device, its intended use, and the regulatory review process. However, the document does not contain information about acceptance criteria or a study that specifically "proves the device meets the acceptance criteria" in terms of algorithmic performance, as would be common for AI/ML-based medical devices.

This device is a physical medical device and its "acceptance criteria" are related to its physical specifications, material properties, and functionality, as demonstrated through verification testing. The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a novel performance study in the way an AI/ML device would.

Therefore, many of the requested elements (e.g., sample size for AI test sets, number of experts for ground truth, MRMC studies, standalone algorithm performance) are not applicable or not present in this type of document for this type of device.

Here's an attempt to answer the questions based only on the provided text, highlighting what is present versus what is not.

Acceptance Criteria and Device Performance (Based on Provided Text)

The document lists "verification tests" performed on the device. These tests implicitly define the "acceptance criteria" by requiring the device to comply with established standards and internal requirements. The "reported device performance" is a statement that the tests were conducted and the device complied.

Specific Study Details:

1. Sample size used for the test set and the data provenance:

   • Test set size: Not explicitly stated for each test. For a physical device, this would typically involve a specific number of units manufactured for testing.
   • Data provenance: Not applicable in the context of clinical data for AI. The tests are performed on the manufactured device itself. No country of origin for "data" is relevant here in the AI context. The tests were "conducted on the final sterile device," implying tests performed by the manufacturer, Degania Silicone, Ltd. (Israel).

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. For a physical device, ground truth is established by engineering measurements, adherence to material specifications, and functional testing against predefined standards (e.g., balloon bursting volume). Expert radiologists or similar personnel are not involved in establishing ground truth for these types of tests.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable. Adjudication is relevant for subjective assessments or disagreements in clinical interpretations, which is not the nature of these engineering and material tests.

4. 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 device is a physical measuring tool, not an AI algorithm assisting human readers. No MRMC study was performed or is relevant.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is a physical medical device, not an algorithm.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The ground truth for this device's verification would be based on:

   • Engineering specifications and drawings: For dimensions, surface finish, markings.
   • Material standards: For tensile strength, biocompatibility.
   • Functional requirements: For balloon concentricity and bursting volume.
   • Regulatory standards: ISO 14971:2012 for risk management.

7. The sample size for the training set: Not applicable. This device does not involve a "training set" in the context of AI/ML.

8. How the ground truth for the training set was established: Not applicable. As there's no training set for an AI algorithm, there's no ground truth to establish for it.

In summary: The provided document is for a traditional, physical medical device. It demonstrates substantial equivalence and verification against engineering and material specifications, not algorithmic performance. Therefore, most of the questions pertaining to AI/ML device studies are not applicable. The "study" mentioned is a compilation of non-clinical verification tests confirming compliance with design and safety requirements.

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AQUARIUS™ Gastrostomy Replacement Tube is intended for use in a well-established gastrostomy tract for feeding and/or administration of medications. May be used for gastric decompression.

Aquariusi Gastrostomy Replacement Tube is a silicone feeding tube comprised of a double lumen tube with external proximal specific ENFit safety access port/s, external retention disk, balloon, and distal open flow-through tip. The tube is with external graduations (cm) and radiopaque marker at the distal end. The tube size and balloon capacity are clearly indicated on the shaft and pouch of each device. The tube is made without use of natural latex, BPA, or phthalates plasticizers.

The provided document is a 510(k) premarket notification for a medical device called the AQUARIUS™ Gastrostomy Replacement Tube. It is a submission to the FDA (Food and Drug Administration) for clearance to market the device, demonstrating substantial equivalence to a legally marketed predicate device.

This document does not describe a study related to the performance of an AI/ML (Artificial Intelligence/Machine Learning) device or a study involving human readers. Therefore, I cannot extract information related to acceptance criteria and studies in the context of AI/ML or human performance improvement.

Instead, the document details the non-clinical summary for the AQUARIUS™ Gastrostomy Replacement Tube, which focuses on device functionality and safety. Here's an analysis of the "acceptance criteria" and "study" in that context:

1. Table of Acceptance Criteria and Reported Device Performance:

The document lists various performance functionality tests conducted on the Gastrostomy Replacement Tube. While specific quantitative acceptance criteria are not explicitly stated in this summary, the completion of these tests implies that the device met pre-defined acceptable limits for each. The "reported device performance" is essentially the successful completion of these tests.

2. Sample size used for the test set and the data provenance: Not applicable. This refers to physical device testing, not a dataset. The "test set" would be the specific physical samples of the device used for each test. The provenance is the manufacturer (Degania Silicone, Ltd. in Israel).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. This is for physical device testing, not expert-adjudicated data.

4. Adjudication method for the test set: Not applicable. This refers to physical device testing, not expert-adjudicated data.

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 document is for a medical device (gastrostomy tube), not an AI/ML system.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This document is for a medical device (gastrostomy tube), not an AI/ML system.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The "ground truth" for these non-clinical tests is established by industry standards, international standards (like ISO 14971:2012 for risk management), and internal engineering specifications for the device's physical and functional performance. It's based on objective, measurable parameters and established engineering principles, not expert consensus or clinical outcomes data in the usual sense.

8. The sample size for the training set: Not applicable. This document is for a medical device (gastrostomy tube), not an AI/ML system. There is no concept of a "training set" for physical device testing.

9. How the ground truth for the training set was established: Not applicable. As there's no training set, there's no ground truth to establish for it in this context.

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