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OneTouch® Reveal™ is indicated for use by individuals or health care professionals in the home or health care facilities for transmitting data from home monitoring devices such as glucose meters and insulin pumps to a server database to support diabetes management. The device is indicated for professional use and over-the-counter sales.

OneTouch® Reveal™ is a Web-based Diabetes management system. The application is designed to assist health care professionals and people with diabetes to track blood glucose levels and insulin doses. The application identifies patterns to help patients manage glycemic control. OneTouch® Reveal™ includes pattern recognition messages, reports, and the ability to view patient data remotely.

Here's an analysis of the acceptance criteria and study information for the LifeScan OneTouch® Reveal™ device, based on the provided text:

Device: OneTouch® Reveal™ (Web-based Diabetes management system)

The provided document is a 510(k) Summary, which typically focuses on demonstrating substantial equivalence to a predicate device rather than detailing specific performance acceptance criteria for a new clinical study. For software devices like OneTouch® Reveal™, "performance" often refers to software verification and validation, and usability, rather than a clinical accuracy study directly analogous to a blood glucose meter.

Based on the available information, here's what can be extracted and what is not explicitly stated:

Acceptance Criteria and Reported Device Performance

Important Note: The document does not provide specific numerical acceptance criteria (e.g., minimum accuracy percentages, specific error rates) that are typical for diagnostic devices, as this is a software system for data management. The "performance data" section broadly states that the device "meets its required specifications and performs as intended."

Study Information

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

   • Sample Size: Not explicitly stated. The document mentions "software verification and validation testing" and "Human Factors Usability Studies," but does not provide specific sample sizes (e.g., number of test cases for software, number of participants for usability).
   • Data Provenance: Not explicitly stated. For software testing, this would typically involve synthetic data, simulated data, and potentially real patient data for validation. For Human Factors, this would involve participants simulating real-world use. The location of these activities is not mentioned.

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

   • Not explicitly stated. For software verification, "ground truth" would be the expected output based on specifications, which is established by software engineers and subject matter experts. For usability, "truth" is often observed user behavior against defined tasks. No specific number or qualifications of such experts are provided.

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

   • Not explicitly stated. Adjudication methods like 2+1 or 3+1 are typically used for clinical endpoints where expert consensus is needed on a subjective interpretation (e.g., image reading). For software functionality and usability testing, this type of adjudication is generally not directly applicable.

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, an MRMC comparative effectiveness study was not done. This device is a data management system, not an AI-assisted diagnostic tool that would be evaluated with human readers. Its primary function is to track, identify patterns, and display data for users, not to provide interpretations that human readers would then review or be assisted by.

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

   • Yes, in essence, the "software verification and validation testing" evaluates the algorithm's performance in a standalone manner against its specifications (i.e., does the software correctly process data, identify patterns, and generate reports as designed). The document implies this was done when it states the device "meets its required specifications and performs as intended."

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

   • For software verification: The "ground truth" would be the expected outcomes and behaviors defined by the software requirements and design specifications. This is derived from scientific principles of glucose monitoring and diabetes management.
   • For human factors: The "ground truth" would be the successful completion of tasks by users, adherence to usability principles, and user satisfaction metrics.

7. The sample size for the training set:

   • Not applicable as this is not a machine learning/AI device in the sense of requiring a "training set" for model development. It's a rules-based software system for data management and pattern recognition. The "patterns" it identifies are likely based on predefined clinical rules or thresholds, not derived from a machine learning training dataset.

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

   • Not applicable for the same reason as point 7. The "ground truth" for the system's logic (e.g., what constitutes a "pattern" or "high glucose event") would be established by clinical experts and engineers based on established medical guidelines for diabetes management, not through a machine learning training process with labeled data.

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The OneTouch® Reveal Diabetes Management Application is a software accessory to the OneTouch® Verio Sync Blood Glucose Monitoring System, and is intended for use in the home setting by people with diabetes. It is intended to aid in the review, analysis, and evaluation of patient data to support diabetes management. The OneTouch® Reveal Diabetes Management Application receives (from both manual entry and wireless transmission), stores, and sends patient data for display and reporting. The OneTouch® Reveal Diabetes Management Application also communicates with web-based applications. The OneTouch® Reveal Diabetes Management Application is available for use on commercially-available mobile devices and uses generally-available networks and communication protocols.

The OneTouch® Reveal Diabetes Management Application (App) is a diabetes management tool that can help you determine what your blood glucose test results mean. This allows you and your health care professional to better monitor and adjust your diabetes care plan. The App is designed to work in conjunction with the OneTouch® Verio™ Sync Meter. Using the Bluetooth® feature on your meter and Apple® device, blood sugar test results can be sent directly from your meter to the App. Once a blood sugar result is sent to the App you can: Tag the blood sugar result with a meal flag, Receive Low and High Pattern messages, Add carbs, activity, medication data and Notes about your activities, Manually enter other blood sugar test results, Review results on graphs, Share your blood sugar results with others for review and follow-up, and Set reminders to prompt you to complete certain tasks.

The OneTouch® Reveal Diabetes Management Application is a software accessory designed to aid in the review, analysis, and evaluation of patient data to support diabetes management. The device works in conjunction with the OneTouch® Verio™ Sync Meter, receiving blood sugar test results via Bluetooth, and allowing users to tag results, receive pattern messages, add data (carbs, activity, medication, notes), and review/share results.

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

1. Table of Acceptance Criteria and Reported Device Performance

The provided summary does not explicitly list quantitative acceptance criteria with corresponding performance metrics in a defined table format. Instead, it describes general validation activities and concludes with substantial equivalence. The "Summary of Performance Characteristics" section indicates:

2. Sample Size Used for the Test Set and Data Provenance

The provided 510(k) summary does not specify the sample size used for the user performance evaluation study or the human factors usability studies.

The data provenance is not explicitly stated. Given it's a submission for the US FDA, it's likely that at least some of the testing involved participants in a US-equivalent demographic or was overseen by US regulatory standards. However, the sponsor is LifeScan Europe, a Division of Cilag GmbH International (Switzerland), so testing could have occurred internationally. The summary does not indicate whether data was retrospective or prospective.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

The document does not mention the use of experts to establish ground truth for the test set in the context of blood glucose pattern recognition or other analytical features. The validation focuses on software functionality and user interaction rather than diagnostic accuracy requiring expert consensus on specific cases.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method like 2+1 or 3+1. This type of adjudication is typically used for image-based diagnostics where multiple experts' opinions are combined to form a ground truth, which is not directly applicable to the described functionalities of this diabetes management application (e.g., displaying data, identifying patterns based on predefined algorithms).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No MRMC comparative effectiveness study was mentioned in the provided summary. The device's primary function is to process and display blood glucose data, not to perform interpretations that typically require multiple human readers. The summary focuses on comparing the device's functionality to a predicate device and validating its software and usability.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) was Done

Yes, a form of standalone evaluation was conducted as part of the "Full verification and validation testing of the OneTouch® Reveal™ Diabetes Management Application software." This validation would inherently involve testing the software's algorithms and functionalities (e.g., pattern recognition, data storage, display accuracy) in a standalone manner, separate from human-in-the-loop performance, to ensure they meet design specifications before user performance studies. The "user performance evaluation study" and "Human Factors Formative Usability studies" assessed the human-in-the-loop aspects.

7. The Type of Ground Truth Used

For the software validation, the "ground truth" would be established by the predefined software specifications and functional requirements. For example:

• Data Storage and Transmission: The ground truth would be that the data received, stored, and sent accurately matches the input data from the OneTouch® Verio™ Sync Meter or manual entry.
• Pattern Recognition: The ground truth would be the predefined rules or algorithms for identifying "Low patterns" and "Before-Meal High patterns." The algorithm's output (identification of a pattern) would be compared against the expected output based on these rules.
• Calculations/Display: The ground truth would be accurate mathematical calculations and correct display of data as per design.

No mention of pathology, outcomes data, or expert consensus specific to establishing the "correctness" of interpreted medical findings is made, as the device's main role is data management and simple pattern alerts based on pre-set thresholds.

8. The Sample Size for the Training Set

The document does not specify a sample size for a "training set." This type of application (diabetes management software) typically does not involve machine learning models that require explicit training data in the same way an AI diagnostic algorithm would. Its functionalities are rule-based and deterministic (e.g., displaying data, recognizing patterns based on pre-defined thresholds, calculating averages). Therefore, the concept of a "training set" is not applicable in the context usually addressed by such questions for AI/ML devices.

9. How the Ground Truth for the Training Set Was Established

As there's no mention of a traditional machine learning "training set," the concept of establishing ground truth for it is not applicable here. The "ground truth" for the device's functions (e.g., correct data processing, accurate pattern identification) would be established by its design specifications and the underlying mathematical or logical rules programmed into the software.

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