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The device is indicated to be used for:
· Improvement of abdominal tone, strengthening of the abdominal muscles, development of firmer abdomen.

• · Strengthening, Toning and Firming of buttocks and thigh

The Electromagnetic Stimulator Device, with models FlexPulse, MagnaCore, and MagnetiKa, consists of a main unit, handpiece tools, and a power cable. The main unit has a power supply unit, a control unit, and a cooling unit. The control unit has a control element and a liquid crystal display. The handpiece tools have electromagnetic induction coils and cooling fans, it is a non-invasive device.

The device generates an electromagnetic field that interacts with human body tissues, aiding in muscle stimulation to strengthen and tone the abdomen, buttocks, and thighs. With two outputs, it allows simultaneous treatment using two applicators.

This document is a 510(k) summary for a medical device (Electromagnetic Stimulator Device, EMS) for muscle conditioning. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing extensive details about clinical study acceptance criteria and device performance based on those criteria, which would be typical for an AI/ML medical device.

Therefore, many of the requested items (e.g., acceptance criteria for diagnostic accuracy, sample size for test sets, expert adjudication methods, MRMC studies, standalone performance, ground truth for training/test sets) are not present in this document because they are characteristic of a different type of medical device submission (e.g., an AI-based diagnostic tool) and are not required for demonstrating substantial equivalence for a physical muscle stimulator.

The provided text details non-clinical tests and comparisons of physical and functional characteristics.

Here's an analysis based on the provided document:

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

The document does not present "acceptance criteria" in terms of diagnostic accuracy metrics or clinical outcomes with corresponding performance values that would be typical for an AI/ML device. Instead, it focuses on demonstrating substantial equivalence in terms of technical characteristics and safety/performance standards.

The acceptance criteria here are implicitly related to meeting the "substantial equivalence" definition, meaning the device is as safe and effective as the predicate. The "performance" is shown through the comparison table and non-clinical testing.

Implicit Acceptance Criteria and Reported Performance (from the Comparison Table and Non-Clinical Tests Summary):

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 document. The document describes non-clinical engineering and safety compliance tests, not clinical performance studies with patient 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)

This information is not applicable and not provided. Ground truth establishment by experts is relevant for AI-based diagnostic devices where human interpretation is the gold standard or part of the evaluation. This document is for a physical muscle stimulator.

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

This information is not applicable and not provided. Adjudication methods are used in clinical studies, particularly for diagnostic accuracy assessments, which are not detailed 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

This information is not applicable and not provided. MRMC studies are typically for AI-assisted diagnostic devices to assess the impact of AI on human reader performance. This device is not an AI-based diagnostic tool.

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

This information is not applicable and not provided. Standalone performance is a metric for AI algorithms. This device does not feature such an algorithm. It's a physical muscle stimulator.

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

This information is not applicable and not provided. "Ground truth" in the context of this submission refers to the performance of the predicate device and the established safety and performance standards (like IEC and ISO). There isn't a "ground truth" derived from patient outcomes or expert consensus on a diagnostic task, as this is not a diagnostic device.

8. The sample size for the training set

This information is not applicable and not provided. This device is not an AI/ML device that requires machine learning training sets.

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

This information is not applicable and not provided. Similar to point 8, this is not an AI/ML device requiring labeled training data.

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CO2 Laser System is indicated for incision, excision, ablation, vaporization and coagulation of body soft tissues in medical specialties including aesthetic (dermatology and plastic surgery), podiaty, otolaryngology, neurosurgery, orthopaedics, general and thorasic surgery (including open and endoscopic), dental and oral surgery and genitourinary surgery. The use with the scanning unit is indicated for ablative skin resurfacing.

The CO2 Laser System is a computer controlled radiofrequency (RF) excited carbon dioxide (CO2) laser system that delivers CO2 energy to the target treatment area at 10,600 nm. The CO2 Laser System is comprised of multiple components, including the control unit and handpieces. Laser is transmitted to the tissue via a series of lenses integrated into the articulated arm. The CO2 laser energy is absorbed by water in tissue to achieve its intended treatment effects.

The provided document is a 510(k) summary for a CO2 Laser System (Models: PureLase and VanLase). It describes the device, its indications for use, and a comparison to a predicate device to demonstrate substantial equivalence.

However, the document does not contain any information about acceptance criteria, device performance metrics (e.g., accuracy, sensitivity, specificity), sample sizes for testing or training, data provenance, expert ground truth establishment, or any kind of statistical study (like MRMC or standalone performance). It primarily lists the non-clinical tests performed to ensure the device meets design specifications and relevant safety/performance standards (e.g., IEC 60601 series, ISO 10993).

Therefore, I cannot provide the requested table and study details. The information about the study that proves the device meets the acceptance criteria is not present in this 510(k) summary. The document focuses on showing substantial equivalence based on technical specifications and adherence to safety/performance standards, not on clinical performance metrics typically associated with AI/software-as-a-medical-device (SaMD) clearances that often require clinical performance studies.

Here's what I can extract based on the provided text, and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

• Not provided. The document lists non-clinical tests (IEC standards, biocompatibility, software V&V, bench testing) but does not present specific quantitative acceptance criteria (e.g., accuracy > X%, sensitivity > Y%) or the results of such performance against a test set. This type of information is typically found in clearance documents for devices relying on diagnostic performance (like AI algorithms).

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

• Not provided. No information about a "test set" or "training set" of data (e.g., images, patient records) is mentioned. The tests conducted are primarily engineering and safety compliance tests, not clinical performance studies involving patient data.

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

• Not applicable/Not provided. Since there's no mention of a test set based on clinical data, there's no information about experts establishing ground truth for such data.

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

• Not applicable/Not provided.

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 provided. No MRMC study is mentioned. This device is a laser system, not an AI software intended to assist human readers in interpretation.

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

• Not applicable/Not provided. This is a hardware device, not a standalone algorithm.

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

• Not applicable/Not provided.

8. The sample size for the training set:

• Not provided.

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

• Not applicable/Not provided.

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