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The Omnipod 5 ACE Pump (Pod) is intended for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin. The Omnipod 5 ACE Pump is able to reliably and securely communicate with compatible, digitally connected devices, including automated insulin dosing software, to receive, execute, and confirm commands from these devices. The Omnipod 5 ACE Pump is intended for single patient, home use and requires a prescription.

The Omnipod 5 alternate controller enabled (ACE) Pump is intended to deliver insulin via a tubeless insulin pump (the Pod) that wirelessly connects to and receives insulin delivery commands from the Omnipod 5 Application (App), which is installed on a locked-down controller device or a user's personal compatible smartphone device.

The Omnipod 5 ACE Pump is part of the Omnipod 5 Automated Insulin Delivery System, which also includes the Omnipod 5 Interoperable Automated Glycemic Controller (iAGC), Omnipod 5 Bolus Calculator, and the third-party Dexcom G6 iCGM. Omnipod 5 iAGC and Bolus Calculator functions are functionally independent from the Omnipod 5 ACE Pump. The Omnipod 5 ACE Pump is intended to be digitally connected to the iCGM, the iAGC, and the Bolus Calculator.

The Omnipod 5 ACE Pump can operate in Manual Mode, delivering insulin based on userprogrammed basal rates, or in Automated Mode, where insulin is automatically delivered based on the calculations and command of a compatible iAGC. Currently, the Omnipod 5 ACE Pump is compatible with the Omnipod 5 iAGC, whose software is pre-installed on the Pod and the App. Future alternate controllers (iAGCs) may be established for use with the Pod, in which case the software modules of the Omnipod 5 iAGC would be disabled.

The Pod is a body-wearable insulin pump that affixes to the user on the back of the arm, the lower back, the abdomen, the thigh area, or any site that has a layer of fatty tissue available. It is held in place by an adhesive pad and provides up to three days of insulin before it is removed and replaced with a new Pod. The Omnipod 5 App is an Android software application installed on a handheld touchscreen device that connects to the Pod via Bluetooth Low Energy (BLE) and serves as the user interface of the system.

In addition to programmed basal delivery and automated insulin delivery, the Omnipod 5 ACE Pump allows users to deliver bolus doses at values that are either inputted manually or calculated by the Omnipod 5 Bolus Calculator based on the user's settings and userentered parameters. The Pod has the ability to connect to a compatible iCGM through BLE and receive data for use with the Omnipod 5 iAGC and Omnipod 5 Bolus Calculator.

The Omnipod 5 App has the ability to wirelessly connect to the Insulet Cloud which it utilizes for registering new devices, authenticating users, ensuring hardware devices and host operating systems are compatible, and completing over the air software (OTA) and firmware (FOTA) updates. In addition, data from the App uploads regularly to the Insulet Cloud for data management purposes.

The Omnipod 5 ACE Pump is intended for the subcutaneous delivery of insulin and is stated to be substantially equivalent to the Omnipod DASH Insulin Management System with Interoperable Technology.

Acceptance Criteria and Reported Device Performance

The provided text primarily focuses on regulatory compliance and substantial equivalence to a predicate device rather than precise acceptance criteria and their corresponding empirical results in a clear tabular format. However, the document does mention performance aspects, particularly regarding Delivery Accuracy and Occlusion Detection.

Based on the available information, the following can be inferred:

The document also mentions compliance with various standards, which implicitly sets acceptance criteria for aspects like software validation, risk management, human factors, and cybersecurity. However, specific quantitative acceptance values for these broader categories are not detailed in the provided text.

Study Information

1. Sample size used for the test set and the data provenance:
   The document does not specify the sample sizes (number of devices, test conditions, etc.) used for the performance tests (e.g., delivery accuracy, occlusion detection). It also does not provide details on the data provenance (e.g., country of origin, retrospective or prospective). The testing appears to be primarily lab-based performance verification rather than clinical data from human subjects.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   This information is not provided. The performance data presented (e.g., delivery accuracy, occlusion detection) would typically be derived from objective measurements against known standards rather than expert-established ground truth in a diagnostic context.

3. Adjudication method for the test set:
   Not applicable, as the performance tests are quantitative measurements against defined specifications.

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:
   Not applicable. The Omnipod 5 ACE Pump is an insulin infusion pump, not an AI-assisted diagnostic imaging device that would typically involve a multi-reader multi-case study. The focus is on the device's functional performance and safety.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
   The device itself (the Pod) performs autonomously in its defined functions (e.g., insulin delivery, occlusion detection) based on its pre-programmed software and commands. Therefore, the "Performance Testing" data can be considered standalone algorithm performance for specific functionalities. However, the system is designed to be used with a human-in-the-loop (the user managing their diabetes) and interfaces with an automated insulin dosing software (iAGC). The document indicates "software verification and validation testing" was performed, which would cover the device's algorithmic performance.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
   For Delivery Accuracy and Occlusion Detection, the ground truth would be based on objective physical measurements against established engineering and medical device standards (e.g., volumetric measurements for insulin delivery, controlled pressure or flow scenarios for occlusion detection). This is not expert consensus, pathology, or outcomes data.

7. The sample size for the training set:
   Not applicable. The document describes a medical device, an insulin pump, which is not an AI/ML model that learns from a training set in the typical sense. Its software is developed and validated through traditional software engineering processes, not machine learning training.

8. How the ground truth for the training set was established:
   Not applicable, as there is no mention of a machine learning training set.

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