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Cefaly Connected - OTC is indicated for:

• 1. Acute treatment of migraine with or without aura in patients 18 years of age or older
• 2. Preventative treatment of migraine in patients 18 years of age or older
     Cefaly Connected - Rx is indicated for:
• 1. Acute treatment of migraine with or without aura in patients 18 years of age or older
• 2. Prophylactic treatment of migraine in patients 18 years of age or older

CEFALY Connected - Rx and CEFALY Connected - OTC consists of neurostimulators, Cefaly Electrodes, and mobile application. The neurostimulators are small, non-invasive, and portable devices meant to be worn on the forehead using a self-adhesive electrode to treat migraines similar to Cefaly® Dual Connected - Rx, and Cefaly® Dual Connected - OTC of K212071. The mobile application, CeCe Miqraine Management App, is a two-way communicatinq, mobile application, installed and run on users' personal mobile devices such as smartphone or tablet. The app is compatible with neurostimulators of CEFALY Connected devices (Rx and OTC). The mobile application communicates with the neurostimulator through Bluetooth and allows users to track the treatments.

The provided text describes a 510(k) premarket notification for a medical device called "CEFALY Connected - OTC" and "CEFALY Connected - Rx." The submission focuses on demonstrating substantial equivalence to a predicate device, the "Cefaly® Dual Series" (K212071). Since this is a 510(k) submission, the primary aim is to show that the new device is as safe and effective as a legally marketed predicate device, not necessarily to prove absolute effectiveness against a specific disease. Therefore, the "acceptance criteria" and "study" are primarily focused on demonstrating equivalence and safety/performance as compared to the predicate, especially regarding changes made to the mobile application.

Here's an analysis of the acceptance criteria and study information:

Acceptance Criteria and Reported Device Performance

The core of the submission relies on demonstrating that while there are differences in the mobile application's functionalities, the fundamental medical device functions and underlying technology of the neurostimulator remain equivalent in terms of safety and efficacy to the predicate device.

Study Details

The provided document describes studies primarily focused on bench testing and software validation to demonstrate substantial equivalence, rather than a clinical trial proving direct efficacy of the new components.

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

   • Test Set Sample Size: Not explicitly stated in terms of patient numbers or a dataset size for evaluating device performance. The testing described is "Software Verification and Validation, including interoperability testing" and "Bench testing." These tests would typically use specific test cases and simulated scenarios rather than a traditional patient test set.
   • Data Provenance: Not applicable in the context of patient data. The tests are technical assessments of software functionality and device interoperability.

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

   • Not applicable. The ground truth for software verification and validation, and bench testing, is established by predefined functional requirements, technical specifications, and expected outputs based on engineering principles and regulatory standards. It's not typically established by clinical experts in the same way as an AI diagnostic study.

3. Adjudication method for the test set:

   • Not applicable. As the testing pertains to technical performance and software functionality (e.g., whether a button does what it's supposed to, whether Bluetooth communication is stable, whether intensity values are within tolerance), there isn't a "ground truth" established by human experts that requires adjudication.

4. 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 MRMC study was done. The device is a "Transcutaneous Electrical Nerve Stimulator to Treat Headache," and the changes concern the mobile application interface. This is not an AI-assisted diagnostic device, nor does it involve "human readers" in a clinical interpretation context.

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

   • The core neurostimulator device functions independently of the mobile app. The document explicitly states: "The neurostimulator can perform treatment sessions independent of the mobile application." The mobile app provides supplementary control and monitoring functions. So, in a sense, the neurostimulator's standalone performance (without the app) is inherent to its prior clearance (K212071) and acknowledged in this submission. The "algorithm" here refers to the software controlling the neurostimulator and the mobile app's functionalities. The studies performed were on the software (verification, validation, interoperability) and system (bench testing, cybersecurity).

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

   • The "ground truth" for the tests performed (software verification, validation, interoperability bench testing) would be the device's technical specifications, design requirements, and regulatory standards. For example, if the app is supposed to display device battery level, the ground truth is the actual battery level measured by the device. If the app sends control commands, the ground truth is whether the device responded correctly and within expected parameters.
   • The effectiveness of the core neurostimulator for migraine treatment was established in prior submissions (K212071) through clinical performance data, which would have involved outcomes data related to migraine treatment. However, this submission focuses on the safety and performance of the new software features in the mobile app.

7. The sample size for the training set:

   • Not applicable. This is not a machine learning or AI device that requires a training set in the conventional sense. The "software" in question refers to the mobile application's control logic and user interface, which are developed through traditional software engineering methods, not statistical learning from large datasets.

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

   • Not applicable, as there is no training set for an AI/ML algorithm.

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