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The Macroduct Advanced Model 3710 Sweat Collection System is intended only for clinical laboratory use by qualified medical personnel for stimulation of sweat from humans for analysis for the diagnosis of cystic fibrosis.

The Macroduct Advanced Model 3710 Sweat Collection System is intended only for clinical laboratory use by qualified medical personnel for stimulation and collection of sweat from humans for analysis for the diagnosis of cystic fibrosis.

The Macroduct Advanced Sweat Collection System consists of the Macroduct Advanced Model 3710, which is a microprocessor-controlled device powered from a rechargeable lithium-ion battery, battery charging power supply and cord for charging the battery, electrode cable assembly, and a kit of single-use supplies (Pilogel discs, collectors, and straps). The system safely and efficiently accomplishes the stimulation of human sweat through pilocarpine iontophoresis using the Macroduct Advanced Model 3710. The Macroduct Advanced Sweat Collector collects a sample of the stimulated sweat. Markings on the collection tube of the collector indicate if a sufficient sweat rate is achieved during the collection of sweat. The sample can then be analyzed for indications of cystic fibrosis with the Sweat-Chek™ Sweat Conductivity Analyzer using the principle of total electrolyte concentration in the sweat sample; or with the ChloroChek® Chloridometer® using the principle of coulometric titration.

The provided document is an FDA 510(k) Premarket Notification for the Macroduct Advanced Model 3710 Sweat Collection System. This device is an iontophoresis device intended for the stimulation and collection of sweat for the diagnosis of cystic fibrosis.

The document focuses on demonstrating substantial equivalence to a predicate device (Macroduct Model 3700-SYS) rather than presenting a study proving a specific performance claim against established acceptance criteria using a novel algorithm or AI. Therefore, many of the requested elements for an AI/algorithm-based study (like MRMC studies, training/test set sample sizes, ground truth establishment for AI, etc.) are not applicable to this type of submission.

The "acceptance criteria" in this context refer to the device meeting the requirements for substantial equivalence, primarily by demonstrating that modifications do not introduce new safety or effectiveness concerns and that the device performs like the predicate.

Here's a breakdown of what can be extracted and what is not applicable:

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

For a substantial equivalence submission like this, "acceptance criteria" are typically related to maintaining the same fundamental function, safety, and effectiveness as the predicate device. The performance is demonstrated by showing that the modifications do not negatively impact these aspects.

2. Sample size used for the test set and the data provenance
The document does not describe a clinical "test set" in the sense of a dataset for an algorithm's performance evaluation. The substantial equivalence relies on demonstrating that the device itself functions as intended and safely, often through bench testing and compliance with standards rather than a clinical study comparing its output to a gold standard on patient data. There is no mention of a specific sample size for a test set or data provenance (country, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. This is not an AI/algorithm submission that would require expert-established ground truth for a test set. The "ground truth" for this device's performance is adherence to established measurement principles for sweat collection and analysis (as referenced by CLSI C34-A3).

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. No test set adjudication is described.

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 device for sweat collection, not an AI system assisting human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. There is no AI algorithm being evaluated.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
The "ground truth" for this device's functionality is its ability to accurately stimulate and collect sweat conforming to established clinical guidelines (e.g., CLSI C34-A3) for subsequent analysis. It's not about diagnosing a condition itself, but providing the sample. The "truth" is whether the system performs its intended physical function reliably and safely.

8. The sample size for the training set
Not applicable. There is no AI algorithm with a training set.

9. How the ground truth for the training set was established
Not applicable. No AI algorithm/training set.

In summary:

This FDA 510(k) submission is for a medical device (an iontophoresis sweat collection system) seeking substantial equivalence to a previously cleared device. It relies on demonstrating that modifications to the predicate device do not raise new questions of safety or effectiveness and that the device continues to meet relevant performance standards and clinical guidelines for sweat collection. It does not involve a novel AI algorithm or a study comparing AI performance against human interpretation, hence many of the requested details about test sets, AI training, and ground truth establishment for AI are not present in this document.

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