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The Pearl™ Diabetes Management System is intended for continuous, subcutaneous delivery of insulin at programmable basal and bolus rates for the management of diabetes mellitus in adult patients requiring insulin.

The Pearl™ Diabetes Management System is a continuous, programmable insulin delivery system. It consists of a controller, a single use pump body and an infusion set adapter. The controller unit has a user interface to program delivery parameters for basal and bolus insulin delivery. It attaches to the pump body. The pump body provides the drive mechanism and battery power and holds the pre-filled insulin cartridges. The infusion set adapter is the conduit from the insulin cartridge to the final delivery tubing.

The document provided does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and the study proving the device meets those criteria.

However, based on the available text, here's what can be extracted and inferred:

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

The document mentions several tests performed, but it does not list specific quantitative acceptance criteria or detailed results. It generally states that the system "performs reliably and delivers insulin as intended" and "design verification testing confirmed that no new questions of safety or effectiveness were identified."

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

• Sample Size: Not specified. The document only states "the major tests that were performed on the Pearl Pump System to support the modifications." It does not provide the number of devices or data points used in these tests.
• Data Provenance: Not specified. It does not mention the country of origin or if the study was retrospective or prospective. Given it is a device modification, the testing would typically be internal "design verification testing."

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

This information is not applicable as the "Pearl™ Diabetes Management System" is an insulin infusion pump, a hardware device, not an AI/ML-based diagnostic or prognostic tool that would require expert-established ground truth for a test set in the same manner. The "ground truth" for this device would be its physical and software performance against engineering specifications and regulatory standards.

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

This is not applicable for this type of medical device as it's not a diagnostic system requiring human expert adjudication of results.

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 is not applicable. The Pearl™ Diabetes Management System is an insulin pump, not an AI-assisted diagnostic or imaging system. Therefore, MRMC studies involving human readers and AI assistance are irrelevant.

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

This is not applicable in the context of AI/ML algorithm performance. The device itself (the pump) operates in a standalone manner to deliver insulin based on programmed parameters, but this is not an "algorithm-only" performance study in the AI sense.

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

The "ground truth" for this device's performance would be derived from engineering specifications, established performance standards (e.g., for accuracy, occlusion detection), and regulatory requirements for insulin infusion pumps. The document explicitly mentions "assure conformance to the requirements for its intended use" and "design verification testing confirmed that no new questions of safety or effectiveness were identified."

8. The sample size for the training set:

This is not applicable. The Pearl™ Diabetes Management System is not an AI/ML device that uses a "training set" in the conventional machine learning sense. Its functionality is based on pre-programmed logic and mechanical engineering, not learned from data.

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

This is not applicable for the reasons stated above for question #8.

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For the continuous, subcutaneous delivery of insulin at programmable basal and bolus rates for the management of diabetes mellitus in adult patients requiring insulin.

The Pearl Diabetes Management System is a continuous, programmable insulin delivery system. It consists of a controller, a single use pump body, a single use adapter and associated accessories. The controller unit has a user interface to program delivery parameters for basal and bolus insulin delivery and attaches to the pump body. The pump body provides the drive mechanism and battery power for pre-filled insulin cartridges. The adapter connects the insulin cartridge to the infusion set and contains the occlusion sensor.

Here's a breakdown of the acceptance criteria and study information for the Pearl Diabetes Management System, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation of "Implied Acceptance Criteria": The document states that the system "has been verified for performance and functionality to provide assurance that the proposed device has been designed and tested to assure conformance to the requirements for its intended use." For each test listed under "Performance Testing" and "Biocompatibility Testing," the successful completion of the test implies that the device met the pre-defined criteria for that specific test, without explicitly listing numerical thresholds for each.

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

The provided text does not specify the sample size used for the test set (e.g., how many devices were tested for accuracy, how many users participated in usability testing, or how many samples were used for biocompatibility tests).

The data provenance is internal to Asante Solutions, Inc. and is generated during the device's development and testing phases as part of its regulatory submission. It is not based on patient data from a specific country or retrospective/prospective studies in the clinical sense. Instead, it's performance and safety testing data.

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

This information is not applicable or not provided in the context of this 510(k) summary. The "ground truth" here is established by engineering specifications, regulatory standards (like EN 60601-1, IEC 60601-2-24, EN 60601-1-2, ISO 10993-1), and laboratory testing protocols, rather than expert consensus on medical images or diagnoses.

4. Adjudication Method for the Test Set

This information is not applicable or not provided. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies or for establishing ground truth in AI performance evaluations where human expert review is involved. The testing described focuses on mechanical, electrical, software, and biological characteristics against pre-defined engineering and regulatory standards.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not performed as described in the provided text. This type of study is relevant for evaluating the impact of AI on human reader performance, typically in diagnostic imaging. The Pearl Diabetes Management System is an insulin pump, not a diagnostic AI system, and its evaluation focuses on safety, performance, and usability as a standalone device.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The studies described are for a standalone device (the insulin pump itself) acting without a human-in-the-loop for its direct operation during insulin delivery. The "algorithm only" concept usually applies to AI software. While the pump contains software and algorithms for insulin delivery control, occlusion detection, and safety monitoring, the performance testing explicitly evaluates the device's inherent functional capabilities (e.g., accuracy of insulin delivery, occlusion sensing effectiveness) as a standalone system. User evaluation testing, however, does involve humans interacting with the device to program and operate it.

7. Type of Ground Truth Used

The ground truth for the various tests includes:

• Engineering specifications and design requirements: For accuracy, power management, physical robustness (drop, altitude), shipping, and single-fault conditions.
• International standards: Such as EN 60601-1, IEC 60601-2-24, EN 60601-1-2 for mechanical, electrical safety, and electromagnetic compatibility.
• Biocompatibility standards: ISO 10993-1 for biological evaluation, and specific FDA GLP regulations 21 CFR Part 58 for the individual biocompatibility tests (cytotoxicity, sensitization, irritation, systemic toxicity, genotoxicity, implantation, endotoxin).
• Usability metrics: Established during human factors validation studies based on user understanding, programming capability, and satisfactory operation.

8. Sample Size for the Training Set

The provided text does not include information on a "training set" in the context of machine learning or AI. The product testing described is for a medical device (insulin pump) that relies on classical engineering principles, software logic, and control algorithms, not on models trained on a dataset of examples. Therefore, the concept of a "training set" as understood in AI/ML is not applicable here.

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

As there is no training set for an AI/ML model mentioned, this question is not applicable. The "ground truth" for the device's design and verification is, as noted above, based on engineering specifications, regulatory standards, and established laboratory testing protocols.

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