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Iontophoresis Electrodes are intended to be used to introduce soluble salts and other drugs into the body as an alternative to hypodermic injection.

The proposed device is indicated for the administration of soluble salts or other drugs into the body for medical purposes. The Iontophoresis Electrodes utilize adhesive electrodes have both anode and cathode electrodes that adhere to the skin. The electrodes may either be connected by leads (wires) to a separate controller Forming a current loop. The portion of the active electrode that contacts the skin also has a drug/solution reservoir. End users saturate the reservoir with the ionic solution to be delivered. The reservoir contacts the electrode and the drug/solution is delivered to the patient transdermally.

Five models (Small, Medium, Large, Butterfly and Return Electrode) are included in this series products, and they have the same intended purposes, working theories and specifications.

The provided document describes the acceptance criteria and the study that proves the device meets those criteria for the Iontophoresis Electrodes (K232020).

Here's the breakdown of the requested information:

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

The document presents a comparison to a predicate device (Iontophoresis Drug Delivery Electrodes, K040495) to demonstrate substantial equivalence, rather than setting explicit numerical acceptance criteria for a new device's performance. The "acceptance criteria" are implied by the predicate device's specifications and the non-clinical test conclusions.

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

The document explicitly states: "No clinical study is included in this submission."
Therefore, there is no sample size for a clinical test set or information on its provenance. The testing was non-clinical.

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, as no clinical study was conducted that would require expert ground truth. The evaluation focused on non-clinical performance and safety based on recognized standards.

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

Not applicable, as no clinical study was conducted.

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 device is an Iontophoresis Electrode, which is a physical device for drug delivery, not an AI or imaging diagnostic tool that would involve human readers or AI assistance.

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 or software.

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

Not applicable, as no clinical study was conducted. The "ground truth" for the non-clinical tests was established by compliance with international standards (IEC 60601-1, IEC 60601-1-2, ISO 10993-5, ISO 10993-10, ISO 10993-23) and comparison to a legally marketed predicate device.

8. The sample size for the training set

Not applicable. This is a physical medical device, not a machine learning model.

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

Not applicable. This is a physical medical device, not a machine learning model.

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Electrosurgical disposable grounding pads are to conduct electrosurgical energy from target tissue of a patient back to an electrosurgical unit (ESU), or generator.

Electrosurgical disposable grounding pads construct of a layer of conductive aluminum foil with a foam as the supportive base of the pad. The aluminum foil is coated with adhesive hydro-gel layer. There is another film placed above the adhesive hydro-gel layer, which is used to protect the gel during the transportation and storage and will be removed prior to use.

They are available in different combinations of configurations, types, intended populations and conductive area.

They are not provided in sterile conditions and not required to be sterilized by the end user prior to operation. However, they are for single use only.

The provided text is a 510(k) summary for Electrosurgical Disposable Grounding Pads. This document is a regulatory submission to the FDA for a medical device and not a study proving the device meets acceptance criteria for an AI/ML powered device.

Therefore, many of the requested criteria related to AI/ML model performance evaluation (e.g., sample size for test/training sets, expert ground truth, MRMC study, human reader improvement, standalone performance, etc.) are not applicable to this document as it describes a physical, non-software medical device.

The document focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing (bench testing) and biocompatibility, not through AI/ML performance metrics.

Here's a breakdown based on the provided document, addressing the applicable points and explaining why others are not applicable:

Acceptance Criteria and Study for Electrosurgical Disposable Grounding Pads

1. Table of Acceptance Criteria and Reported Device Performance

For this physical device, "acceptance criteria" are derived from compliance with recognized medical device standards and demonstrating substantial equivalence to a predicate device. The "reported device performance" is its successful compliance with these standards.

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

• Sample Size: Not specified in the document. For non-clinical (bench) testing of physical devices, sample sizes are typically determined by engineering and statistical principles to ensure robust testing, but the explicit number of units tested is not provided in this regulatory summary.
• Data Provenance: The tests were non-clinical tests conducted to verify compliance with international standards (IEC, ISO). The location of the testing is not explicitly stated, but the company is based in China. The data would be prospective, as it was generated specifically for this submission.

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

• Not Applicable. This is a physical electrosurgical device, not an AI/ML model for which "ground truth" would be established by experts reviewing data (e.g., medical images). Ground truth for this device is based on objective measurements and compliance with engineering and safety standards.

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

• Not Applicable. This concept is relevant to expert review of data (e.g., medical images) to establish ground truth for AI/ML validation, not for the non-clinical 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 device is a physical electrosurgical pad, not an AI/ML system, so no MRMC study or assessment of human reader improvement with AI assistance was performed.

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

• Not Applicable. This device is a physical electrosurgical pad, not an AI/ML algorithm.

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

• For this physical device, "ground truth" is established through objective measurements and compliance with recognized international standards (e.g., specified resistance limits, temperature rise limits, material compatibility, etc.) during non-clinical (bench) testing. There is no human interpretation of data for ground truth establishment as would be the case for an AI/ML diagnostic device.

8. The sample size for the training set

• Not Applicable. This device is a physical medical device, not an AI/ML model that requires a training set.

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

• Not Applicable. As no AI/ML model was developed, there was no training set and thus no ground truth established for it.

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