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LawMax™ Dilator is intended for use in the dilation of the peripheral vasculature.

LawMax™ dilator is intended to dilate the puncture tunnel of the skin, subcutaneous tissue and vascular wall. The device is compised of a dilator tube and a handle connected to its proximal end. There is a lumen in the tube center from distal end to proximal end which can accept a 0.038 inch guide wire. The tube surface is coatings which can reduce the friction during the insertion manipulation. Operator can monitor the tube using fluoroscope guidance. A handle groximal end of the tube is available to facilitate operation. There is a hemostasis valve which can be adjusted to prevent blood leaking. The specification of LawMax™ dilator is definite by the outer diameter of the cylindrical part of the tube.

The provided text is a 510(k) summary for a medical device called the LawMax™ Dilator. This document describes the device, its intended use, comparison to predicate devices, and the testing conducted to demonstrate its substantial equivalence to previously cleared devices.

Based on the nature of the device (a mechanical dilator for peripheral vasculature) and the information provided, it's highly unlikely that the 510(k) submission involved the specific types of AI-related studies and acceptance criteria questions you've asked (e.g., sample size for test sets, data provenance, number of experts for ground truth, MRMC studies, standalone algorithm performance, training set details). These questions are typically relevant for AI/ML-enabled diagnostic devices or software as a medical device (SaMD), which the LawMax™ Dilator is not.

The "acceptance criteria" for the LawMax™ Dilator would be that it meets the design specifications and performs as safely and effectively as its predicate devices. The "study that proves the device meets the acceptance criteria" refers to the non-clinical performance testing detailed in the document.

Therefore, I will answer the applicable questions based on the provided text, and explicitly state when a question is not relevant or cannot be answered from the provided information.

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

Note: The document states that "Performance testing was conducted to validate and verify that the proposed device met all design specifications and was substantially equivalent to the predicate device." For each listed test, this implies that the performance met the specified criteria.

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

This information is not provided in the given text. For mechanical devices undergoing performance and biocompatibility testing, sample sizes for specific tests (e.g., number of dilators tested for bending strength, number of animals for biocompatibility) would typically be detailed in the full test reports, but not usually in the 510(k) summary. The concept of "data provenance" (country of origin, retrospective/prospective) is typically associated with clinical data for diagnostic or AI devices, which is not applicable here.

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)

This question is not applicable to this type of device and its testing. The "ground truth" concept is relevant for AI/ML diagnostic performance evaluation. For a mechanical dilator, performance is assessed through engineering tests and biological safety tests, not subjective expert evaluation of "ground truth."

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

This question is not applicable to this type of device and its testing. Adjudication methods are typically used in clinical trials or expert review processes for diagnostic accuracy, which is not relevant for a mechanical dilator.

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

This question is not applicable. An MRMC study is done for diagnostic imaging devices where human readers interpret images, often with or without AI assistance. The LawMax™ Dilator is a mechanical dilator, not an imaging or AI-enabled diagnostic device.

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

This question is not applicable. The LawMax™ Dilator is a physical medical instrument, not an algorithm or software. Therefore, the concept of "standalone algorithm performance" does not apply.

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

This question is not applicable in the context of device performance as described. The "ground truth" for a mechanical dilator's performance lies in its adherence to pre-defined engineering specifications, material properties, and biological safety standards, as evaluated by direct measurement and standardized test methods rather than expert consensus on a diagnostic outcome or pathology.

8. The sample size for the training set

This question is not applicable. Training sets are used for machine learning algorithms. The LawMax™ Dilator is a mechanical device, not an AI/ML product.

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

This question is not applicable. As stated above, this device does not involve a training set or AI/ML algorithms.

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FuStar™ Steerable Introducer is indicated to provide a pathway through which devices are introduced within the chambers and coronary vasculature of the heart or in the peripheral vasculature.

The FuStar™ Steerable Introducers are designed to perform as a guiding sheath and/or introducer sheath, which provides a pathway through which devices are introduced within the chambers and coronary vasculature of the heart or in the peripheral vasculature. Its low-profile design and atraumatic tip reduce the potential risk for trauma during percutaneous punctures while facilitating passage of a wide variety of catheters and devices. The proposed introducer can give physicians good control during operation, the curl options are available at 30mm (Small) and 50mm (Large). In each option, there are 2 connector types which are luer lock connector and silicone valve connector, and several specifications combination. Also, the visible knob with scale of controller facilitates the physicians to estimate the deflecting angle of the sheath while intra-operation. The products are available in different effective length (550mm, 700mm and 900mm) and diameter (French size, 5F, 6F, 7F, 8F, 9F and 10F). The deflectable angle of sheath is from 0 degree to 180 degree.

The provided document is a 510(k) Summary for the "FuStar™ Steerable Introducers." This document describes the device, its intended use, and the testing conducted to demonstrate its substantial equivalence to predicate devices, enabling its market clearance.

However, this document does not contain information about acceptance criteria, reported device performance in the context of acceptance criteria, sample sizes for test or training sets, data provenance, expert ground truth establishment, or multi-reader multi-case studies.

The "Testing Conclusion" section lists various types of performance, biocompatibility, and sterilization tests performed (e.g., "Primary Dimension Test," "Bending Test," "Cytotoxicity," "Sterlization Validation"). It states that these tests were conducted "to validate and verify that the proposed device met all design specifications," but it does not provide the acceptance criteria for these tests nor the specific performance results against those criteria.

Therefore, I cannot fulfill the request for a table of acceptance criteria and reported device performance or details about study design elements like sample sizes, ground truth, or expert involvement based solely on the provided text.

The document is a regulatory submission summary intended to demonstrate substantial equivalence, focusing on comparisons to legally marketed predicate devices and confirming adherence to general design specifications and safety requirements through various tests, rather than detailing a comparative effectiveness study or specific performance metrics with acceptance criteria.

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The SeQure™ Snare System is intended for use in the cardiovascular system as a tool to retrieve and manipulate foreign objects.

The SeQure™ Snare System consists of a snare, a catheter, a loader and a torquer. The snare is constructed of a nitinol shaft and a loop with the plane of the loop perpendicular to the nitinol shaft. The nitinol loop is covered with a tungsten coil which is coated by laminated titanium and its nitride to enhance radiopaque. The pre-formed snare loop can be introduced through catheter without risk of permanent deformation because of super-elasticity of nitinol material. The catheter connector and PE tubing with a radiopaque markband at its distal tip to enhance the visibility during the operation. The snare is pre-assembled with the loader and the torque, and then packaged together with corresponding catheter.

The provided documentation describes the SeQure™ Snare System (K102484), a medical device intended for retrieving and manipulating foreign objects in the cardiovascular system.

Here's an analysis of the acceptance criteria and the study data, based only on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The submission is a 510(k) Pre-Market Notification, which primarily focuses on demonstrating substantial equivalence to a predicate device rather than setting and meeting specific, quantitative acceptance criteria for de novo performance. Therefore, the "acceptance criteria" here are implicitly the design specifications and performance benchmarks of the predicate device (ev3 Amplatz "Goose Neck" Snare Device, K972511).

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

• Sample Size: The document does not specify the sample size for any of the performance tests (Dimension, Delivery and Retrieving, Torque Strength, Tensile Strength, Biocompatibility, Package).
• Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. Given that it's a pre-market notification, the performance testing would typically be prospective, conducted by the manufacturer (Lifetech Scientific (Shenzhen) Co., Ltd in China).

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

The document does not provide any information about experts, their number, or qualifications for establishing ground truth. The performance data mentioned refers to engineering and biocompatibility tests, not clinical evaluations that typically involve expert interpretation.

4. Adjudication method for the test set

The document does not mention any adjudication method. This type of information is typically relevant for clinical studies involving multiple reviewers/readers, which is not described here.

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

• MRMC Study: No, there is no mention of an MRMC comparative effectiveness study.
• AI Improvement: The device described is a physical medical device (Snare System), not an AI-powered diagnostic or assistive technology. Therefore, the concept of "human readers improve with AI" is not applicable.

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

The device is a physical snare system. The concept of "standalone algorithm performance" is not applicable. The performance tests are for the physical properties and function of the device itself.

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

The ground truth for the performance tests (Dimension, Delivery and Retrieving, Torque Strength, Tensile Strength, Biocompatibility, Package) would be against engineering design specifications, industry standards, and regulatory requirements (e.g., ISO standards for biocompatibility, mechanical testing standards for strength). There is no mention of clinical ground truth like pathology or outcomes data in this summary.

8. The sample size for the training set

The device is a physical medical device, not a machine learning or AI algorithm. Therefore, there is no "training set" in the context of AI/ML. The manufacturing processes and material specifications are developed through engineering and R&D, not algorithmic training.

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

As there is no "training set" in the AI/ML sense, this question is not applicable.

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