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The Brainsway Deep Transcranial Magnetic Stimulation System is indicated to be used as an aid in short-term smoking cessation for adults.

The Brainsway Deep TMS System enables direct non-invasive activation of deep brain structures. Transcranial magnetic stimulation (TMS) is a non-invasive technique used to apply brief magnetic pulses to the brain. The pulses are administered by passing high currents through an electromagnetic coil placed adjacent to a patient's scalp. The pulses induce an electric field in the underlying brain tissue. When the induced field is above a certain threshold, and is directed in an appropriate orientation relative the brain's neuronal pathways, localized axonal depolarizations are produced, thus activating neurons in the targeted brain structure.

The Brainsway Deep TMS System is composed of the following main components:

1. Cart
   a) TMS Neurostimulator
   b) Cooling System
   c) Positioning Device
2. Helmet
   a) Aiming Apparatus (i.e., ruler/grid)
   b) Electromagnetic Coil (H4-Coil)
   c) Cap

Based on the provided text, the device in question is the Brainsway Deep TMS System, indicated as an aid in short-term smoking cessation for adults. The document outlines a 510(k) submission, classifying it as a Class II medical device.

Crucially, the provided document explicitly states under the "Clinical Performance Data" section: "Not Applicable." This indicates that no direct clinical efficacy or performance study of the device itself (i.e., algorithm only or human-in-the-loop performance with the device) was conducted or provided for this specific 510(k) submission for the Brainsway Deep TMS System for smoking cessation.

Instead, the submission focuses on demonstrating substantial equivalence to previously cleared predicate devices by Brainsway Ltd., specifically:

• Main Predicate (K200957): Brainsway Deep TMS System for smoking cessation.
• Reference Predicate (K183303): Brainsway Deep TMS System for OCD (used to demonstrate equivalence of device modifications like the new stimulator).

The acceptance criteria and performance data provided are primarily non-clinical (bench) performance data showing that the modified Brainsway Deep TMS System performs similarly to already cleared versions. The "study that proves the device meets the acceptance criteria" in this context is the bench testing and compliance with recognized standards to demonstrate that the modifications made do not raise new safety or effectiveness concerns compared to the predicate devices.

Therefore, many of the requested details about acceptance criteria, test set sample size, expert ground truth, adjudication methods, MRMC studies, standalone performance, and ground truth for training/test sets are not applicable or not provided in this specific 510(k) summary, as the clearance relies on substantial equivalence based on technical and performance similarities to existing cleared devices, rather than new clinical efficacy demonstrations.

Given this, I will answer the questions based on the information available in the document, highlighting what is and is not present.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a substantial equivalence submission based on non-clinical performance and a pre-existing indication, the "acceptance criteria" are not framed as specific clinical performance metrics (e.g., sensitivity, specificity for a diagnostic device) but rather as successful demonstration of technical equivalence and safety.

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

• Sample Size for Test Set: Not applicable / Not specified. The data provided focuses on non-clinical bench testing of the modified device's components and system performance against engineering and safety standards, rather than clinical performance on a patient test set.
• Data Provenance: The document does not specify the country of origin for the non-clinical bench test data. It is inherently prospective in the sense that new tests were performed on the modified device, but these are engineering tests, not clinical data from patients.

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 indicated in point 2, there was no clinical test set requiring expert ground truth for patient outcomes or conditions. The "ground truth" for the non-clinical tests would be the established engineering and safety standards (e.g., IEC standards, FDA guidelines for software validation).

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

• Not applicable. No clinical test set requiring adjudication was performed or reported in this 510(k) summary.

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

• No. This is a medical device for treatment (Transcranial Magnetic Stimulation), not an AI diagnostic/imaging device. Therefore, a MRMC study for AI assistance to human readers is not relevant and was not performed.

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

• The device (Brainsway Deep TMS System) is a physical medical device. It does not operate as a standalone algorithm in the way a diagnostic AI would. Its performance is evaluated through its physical output (magnetic pulses) and electrical/mechanical characteristics as detailed in the non-clinical tests, and its substantial equivalence to a predicate device. Clinical performance of the therapy using the device relies on the operator (human-in-the-loop) and the patient, but the performance evaluation here is for the device's technical specifications.

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

• For non-clinical performance: The "ground truth" is adherence to established engineering, electrical, mechanical, and software safety standards (e.g., IEC 60601-1, IEC 60601-1-2, IEC 62304) and comparison to the output/characteristics of the legally marketed predicate devices.
• For the overall indication (smoking cessation): The acceptability of the indication itself and its efficacy as a therapy stems from the prior clearance of the predicate device (K200957), which would have established this efficacy through clinical trials (though not detailed in this particular 510(k) summary).

8. The sample size for the training set

• Not applicable. This device is not an AI/ML algorithm that requires a "training set" in the conventional sense for learning patterns from data. The "training" in this context would be the design and development processes for the hardware and software, and testing against specifications.

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

• Not applicable. As above, there is no "training set" for an AI/ML algorithm in this context.

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The Brainsway Deep TMS System is indicated for the treatment of depressive episodes in adult patients suffering from Major Depressive Disorder who failed to achieve satisfactory improvement from previous anti-depressant medication treatment in the current episode.

The Brainsway Deep TMS System enables direct non-invasive activation of deep brain structures. Transcranial magnetic stimulation (TMS) is a non-invasive technique used to apply brief magnetic pulses to the brain. The pulses are administered by passing high currents through an electromagnetic coil placed adjacent to a patient's scalp. The pulses induce an electric field in the underlying brain tissue. When the induced field is above a certain threshold, and is directed in an appropriate orientation relative the brain's neuronal pathways, localized axonal depolarizations are produced, thus activating neurons in the targeted brain structure. The FDA cleared Brainsway Deep TMS System is composed of the following main components: 1. An Electromagnetic Coil 2. A TMS Neurostimulator 3. A Cooling System 4. A Positioning Device 5. A Cart

This document is a 510(k) premarket notification for the Brainsway Deep TMS System. It focuses on demonstrating substantial equivalence to a previously cleared device (K122288), also from Brainsway Ltd., rather than presenting a de novo study with explicit acceptance criteria for a novel device performance claim. Therefore, the information requested in the prompt, which is typically found in clinical studies establishing the efficacy and safety of a new device, is largely not applicable or not provided in the context of this 510(k) submission.

Here's an analysis based on the provided text:

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

The document does not explicitly present a table of acceptance criteria for diagnostic/AI performance, nor does it report specific device performance metrics in that context. The "acceptance criteria" here are implicitly related to demonstrating substantial equivalence for modifications to an existing device.

From the text, the performance aspects evaluated are primarily related to safety and technical specifications:

• Output Waveform
• Electrical Field Spatial Distribution
• Magnetic Field Strength Gradient Testing
• Software validation testing (compliance with FDA guidelines and IEC 62304)
• Compliance with electrical and mechanical safety, electromagnetic compatibility, and software validation standards (IEC 60601-1, IEC 60601-1-2, IEC 62304).

The reported performance is that these characteristics are substantially equivalent to the predicate device and that the device complies with the mentioned standards.

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 applicable as this submission is for modifications to an existing device, and the testing described is non-clinical (bench testing) and software validation. No new clinical test set (i.e. patients for diagnostic performance evaluation) was used.

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. There was no clinical test set requiring expert-established ground truth for performance evaluation of a new diagnostic claim. The "ground truth" here is the established safety and performance of the predicate device and relevant international standards.

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

This information is not applicable. No clinical test set requiring adjudication was used.

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

A MRMC comparative effectiveness study was not done. The device is not an AI-assisted diagnostic tool but a therapeutic device (Repetitive Transcranial Magnetic Stimulation System).

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

This information is not applicable. The device is a therapeutic system, not an algorithm for standalone diagnostic performance.

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

For this 510(k) submission, the "ground truth" for the device's technical performance and safety is its conformance with recognized international standards (IEC 60601-1, IEC 60601-1-2, IEC 62304) and demonstration of substantial equivalence to a previously cleared predicate device in terms of:

• Output Waveform
• Electrical Field Spatial Distribution
• Magnetic Field Strength Gradient
• Software validation

Safety and effectiveness are established by showing that modifications do not introduce new hazards and that the device operates within established parameters, which are deemed safe and effective based on the predicate device's clearance.

8. The sample size for the training set

This information is not applicable. This is not an AI/ML device requiring a training set in the conventional sense. The "training" for the device's development and validation would refer to engineering design, testing iterations, and software development, not a data-driven machine learning training set.

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

This information is not applicable for the same reasons as point 8.

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