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The SmartBolus Calculator is software intended for the management of diabetes in persons aged 2 and older requiring rapid-acting U-100 insulin. The SmartBolus Calculator calculates a suggested bolus dose based on user-entered carbohydrates, most recent sensor glucose value (or blood glucose reading if using fingerstick), rate of change of the sensor glucose (if applicable), insulin on board (IOB), and programmable correction factor, insulin to carbohydrate ratio, and target glucose value. The SmartBolus Calculator is intended for single patient, home use and requires a prescription.

The SmartBolus Calculator is a software device that is a component of the Omnipod 5 Automated Insulin Delivery System. The SmartBolus Calculator exists on the Omnipod 5 App portion of the Omnipod 5 ACE Pump and relies on the user interface of the App.

The SmartBolus Calculator receives input parameters and settings from other components of the system and calculates a suggested bolus amount of insulin to correct an elevated glucose level (a correction bolus) and/or to cover carbohydrates from a meal (meal bolus). The SmartBolus Calculator allows users to have the option of populating the current estimated glucose value and trend, which is communicated by the connected iCGM. Users may also manually enter the estimated glucose value or a blood glucose (BG) reading from a blood glucose meter. In addition to glucose, the suggested bolus dose is calculated based on the following parameters: user-entered carbohydrates, rate of change of the sensors qlucose (if using a CGM), correction factor, insulin to carbohydrate ratio, target glucose value, and insulin on board (IOB). Once the calculation is complete, the user has the option of delivering the suggested dose of insulin, modifying the amount, or canceling.

The SmartBolus Calculator can be used in the Omnipod 5 Automated Insulin Delivery System with both Manual Mode and Automated Mode.

Here's a summary of the acceptance criteria and study information for the SmartBolus Calculator, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly list specific acceptance criteria in a quantitative manner for the SmartBolus Calculator's performance. Instead, it states that:

• "Verification activities, as required by the risk analysis, demonstrated that the predetermined acceptance criteria were met and the device is safe for use."
• "Through performance testing, the Subject device has been shown to meet the Special Controls and determined to be substantially equivalent to its predicate."
• "There was no impact to clinical performance of the SmartBolus Calculator for the design change discussed in this submission."

This implies that the assessment for this 510(k) submission focused on demonstrating that the new iOS version of the SmartBolus Calculator (subject device) performs identically to the predicate Android version (K222239) and meets the same safety and effectiveness standards, rather than establishing new performance metrics.

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

The document does not specify a sample size for a test set in the context of clinical or performance data for the SmartBolus Calculator itself. The testing mentioned is primarily "software verification and validation testing" and "risk management" activities.

The data provenance is also not explicitly stated as retrospective or prospective clinical data. The testing described appears to be internal software development and validation, rather than a clinical trial.

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

This information is not provided in the document. The testing described is software-centric, and there's no mention of expert-established ground truth for a test set in a medical diagnostic sense.

4. Adjudication method for the test set

This information is not provided in the document, as it doesn't describe a clinical test set requiring adjudication.

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

An MRMC comparative effectiveness study was not mentioned or described. The device is an "Insulin Therapy Adjustment Device," not a diagnostic imaging device where MRMC studies are typically conducted. The document focuses on demonstrating substantial equivalence of a new software implementation (iOS) to an existing one (Android).

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

The SmartBolus Calculator is described as an "Algorithmic software device." Its function is to "calculate a suggested bolus dose." However, it operates as a component of the Omnipod 5 App, and "Once the calculation is complete, the user has the option of delivering the suggested dose of insulin, modifying the amount, or canceling." This indicates it's a human-in-the-loop system, where the user has ultimate control and decision-making power over the suggested dose. Therefore, a purely standalone clinical performance evaluation without human decision-making is not explicitly implied or discussed in this context. The software's calculation itself is standalone, but its application involves a human.

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

The document does not specify a type of ground truth related to clinical outcomes or expert consensus for its performance evaluation for this particular submission. The "verification activities" and "software verification and validation testing" likely used predefined software requirements, simulated data, and mathematical correctness of calculations as their "ground truth" to ensure the algorithms produced the expected outputs given specific inputs according to the established insulin calculation formulas.

8. The sample size for the training set

The document does not describe a training set in the context of machine learning. The SmartBolus Calculator is an algorithm that computes a bolus dose based on programmable factors and user inputs, not a machine learning model that requires a training set.

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

As there's no mention of a machine learning model or a training set, this information is not applicable.

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