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The device is used for endoscopic injection into gastrointestinal mucosa, or to endoscopically introduce a sclerosing agent into selected sites to control actual or potential bleeding lesions in the digestive system.

The Sclerotherapy Needle device consists of Luer connector, handle, sheath, infusion tube, fixed button connection tube and needle. EO sterilization and use for single use only. The Sclerotherapy Needle device is designed to pass through the channel of endoscope to mark the lesions of the digestive tract and use it for injection. Compatibility with endoscopes, working lengths are 1200mm,1800mm, 2000mm, 2300mm, and the minimum working channel is ф 2.8 mm.

This document describes the premarket notification (510(k)) for the Sclerotherapy Needle by Zhejiang Chuangxiang Medical Technology Co., LTD., which has been determined substantially equivalent to the predicate device. However, this document does not contain the specific acceptance criteria or a detailed study outlining device performance against such criteria for the Sclerotherapy Needle.

Instead, the document primarily focuses on establishing substantial equivalence to a predicate device (Hangzhou AGS's Disposable Sclerotherapy Needle, K190032) based on similarities in design, configuration, intended use, and fundamental technology.

Here's an analysis of the information provided and what is missing in relation to your request:

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

• Missing. The document explicitly states: "The following bench tests were performed on Sclerotherapy Needle: Appearance, Physical properties. The results of all testing were passing." However, it does not provide a table detailing specific acceptance criteria (e.g., maximum force for needle deployment, flow rate through the infusion tube, specific dimensions, etc.) or the measured performance values for these tests. It only states that the tests "were passing," which indicates compliance with internal or regulatory requirements but lacks the specific data requested.

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 "bench tests," but it does not specify the sample size for these tests or the provenance of the data.

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 / Missing. This device is a physical medical instrument (sclerotherapy needle), not an AI/imaging device requiring expert interpretation for ground truth establishment. The "bench tests" would typically be evaluated against engineering specifications, not expert consensus on diagnostic images.

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

• Not Applicable / Missing. As above, this concept typically applies to diagnostic studies with multiple readers, not bench testing of a physical medical device.

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 a physical medical device, not an AI diagnostic tool. Therefore, an MRMC study related to AI assistance for human readers is not relevant.

6. 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.

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

• Not Applicable. For the bench tests performed ("Appearance, Physical properties"), the "ground truth" would be the device's adherence to engineering drawings, material specifications, and functional requirements, as validated by standard measurement techniques and test methods. It's not a diagnostic "ground truth" in the clinical sense.

8. The sample size for the training set:

• Not Applicable. This is a physical medical device, not a machine learning model, so there is no "training set."

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

• Not Applicable. As above, no training set.

Summary based on the provided text:

The document confirms that the Sclerotherapy Needle underwent "bench tests" for "Appearance, Physical properties" and that the results "were passing." It also notes compliance with biocompatibility (ISO 10993), sterilization (ISO 11135), and ethylene oxide residuals standards (ISO 10993-7). However, the detailed acceptance criteria (what constitutes "passing" for each specific test of appearance and physical properties) and the actual performance data are not disclosed in this summary. The entire premise of this 510(k) summary is to demonstrate substantial equivalence to an existing predicate device rather than to provide a comprehensive performance study with explicit acceptance criteria and results.

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The Disposable Sclerotherapy Needle is intended for endoscopic injection into the gastrointestinal mucosa.

The Disposable Sclerotherapy Needle device consists of: Luer connector, handle, molding metal tube, Inner sheath connection limit tube, Fixing sleeve, Outer sheath, Inner sheath, connection tube, metal cap, needle. EO Sterilization and use for single use only. The material expected to come into contact with the patient is SUS304 、SUS303、PP/PE.

The provided document describes a 510(k) premarket notification for a medical device, the "Disposable Sclerotherapy Needle." This type of submission aims to demonstrate substantial equivalence to a legally marketed predicate device, rather than proving the device's efficacy through extensive clinical trials as would be required for a novel device.

Therefore, the acceptance criteria and study detailed in this document are not for an AI/ML powered device, nor do they involve complex aspects like:

• AI/ML model performance metrics: There are no metrics like sensitivity, specificity, AUC, etc.
• Test set and training set details: No data sets, their provenance, or sample sizes related to AI are mentioned because there is no AI component.
• Expert ground truth establishment: No expert consensus readings or adjudication methods are discussed because there is no AI algorithm to evaluate.
• MRMC studies: No human reader studies are conducted because the device is a physical instrument, not an AI diagnostic tool.
• Standalone algorithm performance: There's no algorithm to perform standalone.

Instead, the document focuses on bench testing and biocompatibility evaluations for a physical medical instrument.

Here's a breakdown of the relevant information provided, adapted to the context of a physical medical device:

Acceptance Criteria and Device Performance (for a physical medical device)

The document implicitly defines "acceptance criteria" by stating compliance with relevant international standards and successful completion of bench tests. The "performance" is demonstrated by the passing results of these tests and the determination of substantial equivalence.

Study Details (for a physical medical device)

1. Sample Size for the Test Set and Data Provenance:

• Test Set: The document states that "bench tests were performed." While "sample size" for specific physical properties is not explicitly quantified (e.g., "N=X needles tested"), it refers to the number of units tested to ensure properties like appearance and physical integrity. For medical devices, this is typically a statistically relevant sample from a production batch, not a "data set" in the computational sense.
• Data Provenance: Not applicable in the context of "data provenance" for AI models. The testing was conducted by the manufacturer, Hangzhou AGS MedTech CO., Ltd, in China (as per their address). It is implied these are results from prospective testing specifically conducted for this 510(k) submission, not retrospective data.

2. Number of Experts Used to Establish Ground Truth and Qualifications:

• This question is not applicable to this submission. The "ground truth" for a physical device like a sclerotherapy needle is established by established engineering specifications, material standards, and functional performance benchmarks (e.g., a needle must be sharp enough to penetrate tissue without bending, the Luer connector must fit). There are no human "experts" establishing a "ground truth" in the way a radiologist establishes disease presence for AI training. The adherence to international standards and passing bench tests are the "truth."

3. Adjudication Method for the Test Set:

• Not applicable. There is no "adjudication" in the sense of reconciling divergent expert opinions, as there are no expert readings of images or data. Bench tests yield objective, measurable results (pass/fail, within specified tolerances).

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• No, not performed, and not applicable. This is a physical medical device, not an AI assistant. MRMC studies are designed to compare the diagnostic performance of human readers with and without AI assistance. This device does not have an AI component.

5. Standalone Performance:

• Not applicable in the AI sense. There isn't an "algorithm" to perform standalone. The closest equivalent would be the "bench tests" performed on the device itself, which demonstrated its standalone physical properties and compliance with standards.

6. Type of Ground Truth Used:

• The "ground truth" for this device's performance is based on:
  • Engineering Specifications and Design Requirements: The device must meet its predetermined design and functional requirements.
  • International Standards: Adherence to ISO standards (ISO 10993, ISO 11135-1, ISO 10993-7) for biocompatibility and sterilization.
  • Bench Test Results: Objective measurements of physical properties (e.g., appearance, integrity, mechanical characteristics).
  • Comparison to Predicate Device: The predicate device itself serves as a benchmark for substantial equivalence, implying its safe and effective performance in the past.

7. Sample Size for the Training Set:

• Not applicable. This device does not have an AI/ML component, therefore there is no "training set."

8. How the Ground Truth for the Training Set Was Established:

• Not applicable. Since there is no training set for an AI model, there is no ground truth to be established in this context.

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Ask a specific question about this device

Ask a specific question about this device