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The Modular Medical MODD1 Insulin Delivery System is indicated for the subcutaneous delivery of insulin at set and variable rates, for the management of diabetes mellitus in persons requiring insulin, for individuals 18 years of age and greater.

The Modular Medical MODD1 Insulin Delivery System (i.e., MODD1 System) consists of the following:

• A reusable, software-controlled programmable Pump,
• A single-use, disposable 3.0mL (300 unit) Insulin Cartridge with an integrated battery to supply power to the Pump, infusion tubing, and Tubing Cap for attaching to the Infusion Set.
• . A single-use, disposable Adhesive Pad for affixing the MODD1 System to the user's body and allowing for temporary removal of the MODD1 System,
• A single-use, disposable Infusion Set inserted into the patient's subcutaneous tissue, and
• The MMI App, a smartphone application used to program the basal delivery schedule on the Pump.

Accessories to the MODD1 System are FDA-cleared and include the following:

• A single-use disposable syringe (K110771) and needle (K021475) used to fill the Insulin Cartridge reservoir, and
• A reusable Inserter (K163400), optionally used to insert the Infusion Set into the patient's subcutaneous tissue.

The MODD1 System has been demonstrated to be compatible with Humalog U-100 insulin.

The MODD1 System can deliver insulin at basal rates between 0.5 - 4 units per hour (programmable in 0.1-unit increments) and user-selected bolus doses of between 2 and 20 units (in 2-unit increments). Basal insulin delivery may also be suspended for a set period of 30 minutes when required by the user.

The delivery of insulin is achieved by gear motor rotation of a single fixed-position camshaft (components of the Pump controlled by software) that interfaces with the Insulin Cartridge pistons to control the movement of insulin from the insulin reservoir, into an interim chamber, and into the infusion tubing towards the Infusion Set inserted into the patient's subcutaneous tissue. Software monitors dispense output via feedback provided by multiple sensors, providing an alarm when issues with delivery (e.g., malfunctions, low reservoir volume, occlusions) are detected.

The Pump serves as the primary user interface of the MODD1 System. A Control Button is used to prime the fluid path, program bolus deliveries, check system status, and to temporarily suspend insulin delivery. The multi-color LED and piezo buzzer of the Pump provide audio-visual feedback to the user during the set-up process, programming of a bolus delivery, and changing of delivery modes (e.g., bolus, basal, suspend). Audio-visual feedback is also used to inform the user of system alarms (automatically generated) and system status (when requested by the user).

The MMI App is downloaded directly onto an Apple® iPhone 12 Pro Max running iOS version 16. The MMI App allows for the configuration of a basal delivery schedule on the Pump, consisting of up to two basal rates in a 24-hour period. The MMI App and Pump communicate using Bluetooth® Low Energy (BLE) and Near Field Communication (NFC). After the basal delivery schedule has been programmed on the Pump, all insulin delivery functions are controlled using the Pump user interface. Optional features of the MMI App include a dashboard display of current and historical Pump information and delivery history.

The provided text is a 510(k) Summary for the Modular Medical MODD1 Insulin Delivery System. It details the device, its indications for use, comparison to a predicate device, and a summary of non-clinical performance data.

However, the document does not provide information on an AI-driven medical device, nor does it conduct a study involving human readers or expert ground truth. It describes the regulatory submission for an insulin pump.

Therefore, I cannot extract the requested information regarding acceptance criteria, device performance, sample sizes, expert ground truth establishment, adjudication methods, MRMC studies, standalone AI performance, or training set details from the provided text.

The text describes compliance with various medical device standards (e.g., ISO 14971, IEC 62366-1, ISO 10993-1, IEC 60601-1, IEC 62304) and includes bench testing for infusion delivery accuracy and occlusion detection, but these are part of standard medical device validation, not specific to AI performance as requested in the prompt.

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MiniMed Reservoir- This Reservoir is indicated for the subcutaneous infusion of medication, including insulin, from compatible Medtronic insulin pumps and infusion sets. Refer to your Medtronic insulin pump user guide for compatibility.

Extended Reservoir- The Extended Reservoir is indicated for the subcutaneous infusion of medication, including insulin, from compatible Medtronic insulin pumps and infusion sets. Refer to the Medtronic insulin pump user guide for a list of compatible insulins and infusion sets.

The MiniMed family of reservoirs (MMT-326A, MMT-342, MMT-342, MMT-342G) (herein referred to as "reservoirs" or "subject devices") are sterile medication containers designed for single use. The reservoirs are a component of the Medtronic Insulin Pump Delivery System used by patients with diabetes mellitus, requiring the subcutaneous administration of insulin to maintain acceptable blood glucose levels. The reservoirs are indicated for the subcutaneous infusion of medication, including insulin, from compatible Medtronic insulin pumps (i.e., Paradigm pumps and NGP pumps) and infusion sets. Refer to the Medtronic insulin pump user guide for a list of compatible insulins and infusion sets. The reservoirs have the same principle of operation as the predicate devices (MMT-326A, MMT-332A, MMT-342, MMT-342G) and are designed to mechanically connect to compatible infusion sets. Reservoirs are connected to the infusion set via the tubing connector (H-Cap), which enables insulin infusion from the reservoir through a fluid path into the subcutaneous tissue.

This document describes a 510(k) premarket notification for Medtronic MiniMed's Extended Reservoir and MiniMed Reservoir. The purpose of the submission is to qualify an additional sterilization site and an optimized ethylene oxide (EO) sterilization cycle for these devices.

Here's an analysis of the provided text in response to your questions:

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

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics in a pass/fail format. Instead, it states that "The test results demonstrate the subject devices met all the product requirements and specifications of the predicate devices." and "Medtronic concludes the reservoirs can be sterilized at the additional facility using the optimized parameters for EO sterilization cycle."

The acceptance criteria are implicitly defined by the product requirements and specifications of the predicate devices. The performance reported is that the subject devices met these predicate device requirements/specifications.

The relevant performance data from the text is summarized as follows:

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document states: "The testing was performed on MMT-332A since the subject devices have the same hardware design, materials, and attributes as the predicate device."

• Sample Size: The document does not specify the exact sample size (number of units) used for the verification testing. It only mentions that the testing was performed on "MMT-332A" (a 3.0mL MiniMed Reservoir).
• Data Provenance: The document does not explicitly state the country of origin of the data or whether it was retrospective or prospective. Given that it's "verification testing," it would typically be prospective data generated by Medtronic as part of the submission process. Medtronic MiniMed is located in Northridge, CA, USA, and the contract manufacturer and one sterilization site are in Puerto Rico, so the data likely originated from these locations.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

This is not applicable to this submission. The device is an infusion pump accessory, and the testing focuses on the impact of a sterilization process change on the device's physical, chemical, and functional attributes, as well as its sterility. It does not involve human interpretation or diagnostic performance where experts establish a "ground truth" in the diagnostic sense. The "ground truth" here is the established product requirements and specifications of the predicate devices.

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

This is not applicable. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies involving interpretation of medical images or diagnostic results, where multiple experts provide opinions that may need to be reconciled to establish a ground truth. The testing described here is engineering and sterilization validation, which does not involve such an adjudication process.

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. An MRMC study is relevant for AI-powered diagnostic or screening devices where human readers (e.g., radiologists) interpret cases with and without AI assistance. The Medtronic reservoirs are physical medical devices, and the submission is about a sterilization process change, not an AI diagnostic tool.

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

This is not applicable. The device is not an algorithm or software. It is a physical medical device (reservoir) for insulin delivery.

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

The "ground truth" for this submission is the established product requirements and specifications of the predicate devices (MiniMed Reservoir 1.8mL (MMT-326A), MiniMed Reservoir 3.0mL (MMT-332A), and Extended Reservoir (MMT-342, MMT-342G)). The verification testing aimed to demonstrate that the subject devices, after the sterilization change, still meet these existing, validated performance standards.

8. The sample size for the training set

This is not applicable. This is not a machine learning or AI device that requires a training set. The submission concerns changes to a manufacturing process (sterilization) for an existing medical device.

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

This is not applicable, as there is no training set for this type of device submission.

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The Omnipod GO Insulin Delivery Device is intended for the subcutaneous infusion of insulin at a preset basal rate in one 24-hour time period for 3 days (72 hours) in adults with type 2 diabetes.

The Omnipod GO Pod helps manage diabetes by providing continuous subcutaneous insulin delivery. To facilitate insulin dose titration and provide appropriate options across a wide range of daily insulin needs, the Omnipod GO Device will come in seven different models: 10, 15, 20, 25, 30, 35, and 40 units per day, with each model delivering insulin at a fixed rate over the 72hour life of the device. There is no ability to deliver a bolus dose of insulin using the Omnipod GO device. Like the proposed predicate device (V-GO Insulin Delivery System, K103825), Omnipod GO will be entirely self-contained in an on-body device that is single-use, sterile, and disposable. It is small, lightweight, and designed to the body via an adhesive pad. The adhesive backing keeps the device securely in place for up to 3 days (72 hours).

The provided text describes the Omnipod GO Insulin Delivery Device, but it does not contain specific acceptance criteria or the study details to prove the device meets those criteria, especially in the format of a table with reported device performance.

Here's what can be extracted from the document regarding performance and evaluation, and where information is missing:

Missing Information:

• Specific Acceptance Criteria Table: The document doesn't provide a table explicitly listing acceptance criteria for performance metrics (e.g., specific accuracy ranges they aimed for). It mentions "predetermined acceptance criteria were met" but doesn't detail what they were.
• Reported Device Performance: While it states the device delivers insulin accurately and detects occlusions, specific numerical performance results (e.g., X% accuracy, occlusion detection time) are not provided in the summary.
• Test Set Sample Size: The document refers to "human factors validation" and "performance testing" but does not specify the sample size used for these tests.
• Data Provenance (Test Set): Not explicitly stated whether the test data was retrospective or prospective, or its country of origin.
• Number of Experts & Qualifications (for Ground Truth): Not mentioned for any ground truth establishment.
• Adjudication Method: Not mentioned.
• MRMC Comparative Effectiveness Study: Not mentioned as being performed.
• Effect Size of Human Readers Improvement with AI: Not applicable as no MRMC study with AI assistance is mentioned.
• Standalone (Algorithm Only) Performance: The device is an insulin delivery device, not an AI diagnostic/screening algorithm, so this concept (algorithm only performance) doesn't directly apply in the typical sense for medical imaging or similar devices. Its performance is inherent to its physical operation.
• Type of Ground Truth Used: For mechanical performance, "ground truth" would implicitly be highly accurate measurements against known standards. For human factors, it would be based on participant observations and feedback. However, it's not explicitly detailed.
• Training Set Sample Size: Not applicable/not mentioned, as there is no mention of a machine learning model being "trained" in the typical sense for this device.
• How Ground Truth for Training Set was Established: Not applicable/not mentioned.

What is provided in the document:

The document outlines a series of non-clinical performance data categories and general statements about compliance and effectiveness.

Summary of Non-Clinical Performance Data (What was done, but not detailed results or acceptance criteria):

• Risk Management: Completed in accordance with ISO14971:2019. "Verification activities... demonstrated that the predetermined acceptance criteria were met." (Details of criteria and results are missing).
• Safety Assurance: Device utilizes an insulin pump design marketed with other Omnipod devices. Risk mitigations from previous devices are similarly implemented to provide safety against overdose and underdose (occlusion detection, delivery accuracy).
• Human Factors Validation: Followed IEC 62366:2015-1, HE75:2009, and FDA guidance. "A robust validation evaluation was performed to demonstrate safe and effective use... Results... demonstrate that the Omnipod GO has been found to be safe and effective." (Specific data, sample size, or acceptance criteria are missing).
• Software Validation: Performed in accordance with IEC 62304:2015 and FDA guidance.
• Cybersecurity: Analysis performed following FDA guidance.
• Performance Testing: "Demonstrated that the device delivers insulin accurately at various flow rates and that it can effectively detect when an occlusion occurs and promptly notify the user." (Specific metrics, acceptance criteria, and results are missing).
• Biocompatibility: Evaluation performed per ISO 10993-1:2018 (patient-contacting materials are the same as currently marketed Omnipod Pod devices).
• Sterilization: Product adoption completed into the family of devices under sterilization validation.
• Electrical Safety and EMC Testing: Performed to verify compliance with IEC 60601-1:2005 and IEC 60601-1-2.

Key Performance Claim related to Predicate Device Comparison (Table 1.01):

This is the only specific quantitative performance metric with a value mentioned in the entire document. The document implies this "improved" accuracy value meets the new device's acceptance criterion for insulin delivery accuracy.

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The Extended Reservoir is indicated for the subcutaneous infusion including insulin, from compatible Medtronic insulin pumps and infusion sets. Refer to your Medtronic insulin pump user guide for a list of compatible insulins and infusion sets.

Extended Reservoir (herein referred to as "EWR" or "MMT-342") is a sterile medication container designed for single use. The Extended Reservoir (MMT-342) is a component of the Medtronic Insulin Pump Delivery System used by patients with diabetes mellitus, requiring subcutaneous administered insulin, to maintain acceptable blood glucose levels. The Extended Reservoir (subject device) is indicated for the subcutaneous infusion of medication, including insulin, from compatible Medtronic insulin pumps and infusion sets. Refer to your Medtronic insulin pump user guide for a list of compatible insulins and infusion sets.

The provided text describes a 510(k) premarket notification for an "Extended Reservoir" (MMT-342) by Medtronic MiniMed, Inc. The primary purpose of this notification is to demonstrate substantial equivalence to a predicate device (Medtronic MiniMed Paradigm Reservoir MMT-332A, K032005) while extending the duration of use from 3 days to 7 days.

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

Acceptance Criteria and Reported Device Performance

The core acceptance criterion is to demonstrate that extending the duration of use for the reservoir from 3 days to 7 days does not negatively impact the safety and effectiveness of the device. Since there are no changes in hardware design, materials, manufacturing, packaging, sterilization processes, fluid capacity, insulin compatibility, or reservoir assembly, the acceptance criteria are implicitly tied to maintaining the performance characteristics of the predicate device over the extended duration.

The text does not provide a specific table of quantitative acceptance criteria for parameters like insulin delivery accuracy, occlusion detection, or material degradation. Instead, it states that "The test results demonstrate that MMT-342 (subject device) met all the product requirements and specifications of MMT-332A (predicate device)." This implies that the performance over 7 days matches the established performance standards of the 3-day predicate.

The reported device performance, in summary, is that the Extended Reservoir (MMT-342) successfully maintains the same safety and effectiveness as the predicate device (MMT-332A) when used for up to 7 days.

Study Details

1. Sample size used for the test set and the data provenance:
   The document states: "Medtronic performed verification testing to support extending the duration of the reservoirs use (from up to 3 days to up to 7 days)." However, the specific sample size for this verification testing is not provided in the extracted text.
   The provenance of the data is not explicitly mentioned (e.g., country of origin, retrospective/prospective). It is implied to be internal testing conducted by Medtronic.

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 in the extracted text. The verification testing described is likely technical performance testing rather than human expert assessment of a medical condition.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
   This information is not applicable as the document describes technical verification testing, not a clinical study involving adjudication of clinical observations.

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:
   This information is not applicable. The device is an insulin reservoir, not an AI-assisted diagnostic tool.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   This information is not applicable. The device is a physical medical device (insulin reservoir), not a software algorithm.

6. The type of ground truth used (expert concensus, pathology, outcomes data, etc.):
   For mechanical/material performance, the "ground truth" would be established engineering specifications and validated test methods (e.g., for infusion accuracy, material integrity, sterility maintenance). The document indicates that the subject device "met all the product requirements and specifications of MMT-332A." This implies the predicate device's established performance standards serve as the ground truth.

7. The sample size for the training set:
   This information is not applicable as the device is a physical medical device, not a machine learning model.

8. How the ground truth for the training set was established:
   This information is not applicable for the same reason as above.

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The Omnipod Insulin Management System is intended for subcutaneous delivery of insulin at set and variable rates for the management of diabetes mellitus in persons requiring insulin and for the quantitative measurement of glucose in fresh whole capillary blood (in vitro) from the finger.

The glucose measurements should not be used for the diagnosis of or screening for diabetes. The PDM glucose meter is intended for single-patient use and should not be shared.

Abbott FreeStyle test strips are used with the built-in FreeStyle meter for the quantitative measurement of blood glucose in fresh whole capillary blood from the finger, upper arm, and palm. Abbott FreeStyle Control Solutions are used to verify that the meter and test strips are working together properly and that the test is performed correctly.

The Omnipod DASH Insulin Management System is intended for subcutaneous delivery of insulin at set and variable rates for the management of diabetes mellitus in persons requiring insulin.

Additionally, the Omnipod DASH System is interoperable with a compatible blood glucose meter to receive and display glucose measurements.

The subject devices provide for the management of insulin therapy and blood glucose monitoring by patients with diabetes mellitus. They are each comprised of two primary components: the disposable insulin infusion pump (Pod) and an associated wireless remote controller referred to as the Personal Diabetes Manager (PDM). The PDMs incorporate a suggested bolus calculator which aids the user in determining the insulin bolus dosage needed based on carbohydrates ingested, most recent blood glucose reading. programmable correction factor, insulin to carbohydrate ratio, target blood glucose value, and Insulin on Board (IoB).

The Pod is a body-wearable insulin pump that affixes to the user on the back of the arm, the lower back or 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 PDM is a handheld device that controls the Pod. The user interfaces with the device system through the PDM, where they control basal and bolus delivery and various insulin program settings and calculations. The PDM also has a food library to assist with carbohydrate calculations, and it maintains several variables in a history log for the viewer to track their diabetes therapy. The Omnipod Insulin Management System PDM has an integrated blood glucose meter and communicates with the Pod using wirelessly using secure, low power, bi-directional radio frequency (RF) communications at 433.92MHz. The Omnipod DASH Insulin Management System PDM does not have an integrated blood glucose meter, but is interoperable with a compatible blood glucose meter to receive and display glucose measurements. The Omnipod DASH PDM communicates to the Pod and a compatible blood glucose meter using Bluetooth Low Energy.

Both systems are for prescription use only.

The provided text describes modifications to the Omnipod Insulin Management System and Omnipod DASH Insulin Management System to include Lyumjev U100 insulin in their labeling as a compatible insulin. The submission emphasizes that the devices are substantially equivalent to their predicate devices cleared in K192659 because the only change is the addition of this new insulin and that performance testing demonstrates no adverse effect on safety when using Lyumjev U100.

Here's an analysis of the requested information based on the provided text:

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

The document does not explicitly state "acceptance criteria" in a tabulated format for the Lyumjev U100 insulin. Instead, it refers to broad performance categories and comparative characteristics with the predicate device. The core "acceptance criteria" can be inferred as demonstration that the device's performance (delivery accuracy, flow rates, etc.) is not negatively impacted and that the insulin's stability is maintained when used with Lyumjev U100.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not provide specific sample sizes for the "in-use stability and leachables testing" conducted with Lyumjev U100 insulin. It also does not specify the country of origin of the data or whether the test was retrospective or prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the document. The testing described (drug stability/compatibility) is laboratory-based and does not involve human expert interpretation for ground truth establishment in the traditional sense of image analysis or diagnostic studies.

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

This information is not provided. Adjudication methods are typically relevant for studies involving human interpretation or subjective assessments, which do not appear to be the case here.

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 done. The device is an insulin management system, not an AI-assisted diagnostic tool that would involve human readers.

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

The testing described (drug stability and compatibility) is inherently "standalone" in that it assesses the physical and chemical interaction between the device components and the insulin, independent of human interaction within the testing framework. However, this is not an "algorithm-only" performance as would be seen in AI device submissions. The submission is for hardware/software system compatibility with a new drug.

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

The ground truth for the stability and compatibility testing would be established through laboratory analytical methods (e.g., chemical assays, spectroscopic analysis) to determine the integrity and concentration of the insulin, as well as the presence of leached substances, compared against established specifications for the insulin and device materials. This is not expertise-based ground truth like pathology or outcomes data.

8. The sample size for the training set

This information is not applicable and not provided. This submission is for compatibility with a new insulin, not an AI/ML algorithm that requires a training set.

9. 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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The Omnipod DASH Insulin Management System is intended for subcutaneous delivery of insulin at set and variable rates for the management of diabetes mellitus in persons requiring insulin.

Additionally, the Omnipod DASH System is interoperable with a compatible blood glucose meter to receive and display glucose measurements.

The Omnipod Insulin Management System is intended for subcutaneous delivery of insulin at set and variable rates for the management of diabetes mellitus in persons requiring insulin and for the quantitative measurement of glucose in fresh whole capillary blood (in vitro) from the finger.

The glucose measurements should not be used for the diagnosis of or screening for diabetes. The PDM glucose meter is intended for single-patient use and should not be shared.

Abbott FreeStyle test strips are used with the built-in FreeStyle meter for the quantitative measurement of blood glucose in fresh whole capillary blood from the finger, upper arm and palm. Abbott Freestyle Control Solutions are used to verify that the meter and test strips are working together properly and that the test is performed correctly.

The subject devices provide for the management of insulin therapy by patients with diabetes mellitus. The devices are comprised of two primary components: the disposable insulin infusion pump (Pod) and an associated wireless remote controller referred to as the Personal Diabetes Manager (PDM). The PDMs incorporates a suggested bolus calculator which aids the user in determining the insulin bolus dosage needed based on carbohydrates ingested, most recent blood glucose reading, programmable correction factor, insulin to carbohydrate ratio, target blood glucose value, and Insulin on Board (IoB).

The Pod is a body-wearable insulin pump that affixes to the user on the back of the arm, the lower back or 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 PDM is a handheld device that controls the Pod. The user interfaces with the device system through the PDM, where they control basal and bolus delivery and various insulin program settings and calculations. The PDM also has a food library to assist with carbohydrate calculations, and it maintains several variables in a history log for the viewer to track their diabetes therapy. The Omnipod Insulin Management System PDM has an integrated blood glucose meter and communicates with the Pod using wirelessly using secure, low power, bi-directional radio frequency (RF) communications at 433.92MHz. The Omnipod DASH Insulin Management System PDM does not have an integrated blood glucose meter, but is interoperable with a compatible blood glucose meter to receive and display glucose measurements. The Omnipod DASH PDM communicates to the Pod and a compatible blood glucose meter using Bluetooth Low Energy.

The systems are for prescription use only.

This document is a 510(k) premarket notification for the Omnipod Insulin Management System and Omnipod DASH Insulin Management System. It primarily discusses a labeling change to add Fiasp U100 insulin as compatible and asserts substantial equivalence to a previously cleared device (K182630). Therefore, the information typically found for acceptance criteria and a study proving device performance in a de novo or original 510(k) submission (which often includes specifics on a clinical trial or performance study with detailed metrics) is not present here.

Based on the provided document, I can extract the following relevant information regarding acceptance criteria and studies:

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

The document does not provide a table with specific quantitative acceptance criteria or detailed reported device performance metrics in the way a clinical study report would. Instead, it states that:

• Acceptance Criteria Mentioned: "Verification activities, as required by the risk analysis, demonstrated that the predetermined acceptance criteria were met and the devices are safe for use." However, the specific criteria themselves are not listed.
• Reported Device Performance:
  • "Drug Stability and Compatibility; In-use stability and leachables testing was conducted with Fiasp U100 insulin to verify and validate that the systems do not adversely affect the insulin."
  • "Fiasp stability testing in Omnipod Pods"
  • "Leachables study"

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not specify the sample sizes for the stability and leachables testing, nor does it provide information about the provenance of the data (e.g., country of origin, retrospective or prospective nature).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not applicable to the type of testing described (drug stability and leachables) and is therefore not present in the document. These tests are laboratory-based and do not involve expert interpretation or ground truth establishment in the same way a diagnostic device might.

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

This information is not applicable to the type of testing described (drug stability and leachables) and is therefore not present in the document.

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 information is not applicable. The device is an insulin management system, not an AI-assisted diagnostic tool requiring MRMC studies.

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

This information is not applicable in the context of the described modifications. The primary device function (insulin delivery) is not an algorithm-only standalone performance that would be assessed in this manner for the stated purpose of the submission. The "dose calculator" is mentioned within the context of the PDM, implying human interaction.

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

For the stability and leachables testing, the "ground truth" would be established by scientific analytical methods (e.g., chemical assays, chromatography) to measure insulin degradation, chemical composition of leachates, and ensure the drug substance remains within predefined acceptable ranges, as per established pharmaceutical and medical device testing standards. The document does not explicitly detail these laboratory "ground truth" methods but refers to "Fiasp stability testing in Omnipod Pods" and a "Leachables study."

8. The sample size for the training set

The document describes a 510(k) submission for a modification (labeling change for compatible insulins) to existing devices. There is no mention of a "training set" as would be used for machine learning or AI algorithm development because that is not the nature of this submission. The testing described (stability, leachables) does not involve training sets.

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

As there is no "training set" reported for this submission, this question is not applicable.

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Omnipod Insulin Management System

The Omnipod® Insulin Management System is intended for subcutaneous delivery of insulin at set and variable rates for the management of diabetes mellitus in persons requiring insulin and for the quantitative measurement of glucose in fresh whole capillary blood (in vitro) from the finger.

The glucose measurements should not be used for the diagnosis or screening for diabetes. The PDM glucose meter is intended for single patient use and should not be shared.

Abbott FreeStyle® test strips are used with the built-in FreeStyle meter for the quantitative measurement of blood glucose in fresh whole capillary blood from the finger, upper arm and palm. Abbott Freestyle Control Solutions are used to verify that the meter and test strips are working together properly and that the test is performed correctly.

Omnipod DASH Insulin Management System

The Omnipod DASH Insulin Management System is intended for subcutaneous delivery of insulin at set and variable rates for the management of diabetes mellitus in persons requiring insulin.

Additionally, the Omnipod DASH System is interoperable with a compatible blood glucose meter to receive and display glucose measurements.

The subject devices provide for the management of insulin therapy and blood glucose monitoring by patients with diabetes mellitus. They are each comprised of two primary components: the disposable insulin infusion pump (Pod) and an associated wireless remote controller referred to as the Personal Diabetes Manager (PDM). The PDMs incorporate a suggested bolus calculator which aids the user in determining the insulin bolus dosage needed based on carbohydrates ingested, most recent blood glucose reading, programmable correction factor, insulin to carbohydrate ratio, target blood glucose value, and Insulin on Board (IoB).

The Pod is a body-wearable insulin pump that affixes to the user on the back of the arm, the lower back or 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 PDM is a handheld device that controls the Pod. The user interfaces with the device system through the PDM, where they control basal and bolus delivery and various insulin program settings and calculations. The PDM also has a food library to assist with carbohydrate calculations, and it maintains several variables in a history log for the viewer to track their diabetes therapy. The Omnipod Insulin Management System PDM has an integrated blood glucose meter and communicates with the Pod using wirelessly using secure, low power, bi-directional radio frequency (RF) communications at 433.92MHz. The Omnipod DASH Insulin Management System PDM does not have an integrated blood glucose meter, but is interoperable with a compatible blood glucose meter to receive and display glucose measurements. The Omnipod DASH PDM communicates to the Pod and a compatible blood glucose meter using Bluetooth Low Energy.

Both systems are for prescription use only.

The provided document is a 510(k) summary for the Omnipod Insulin Management System and Omnipod DASH Insulin Management System. It describes modifications to an existing device rather than a new device and thus does not contain the specific information requested in the prompt regarding a study that proves the device meets specific acceptance criteria with detailed statistical analysis.

However, based on the Performance Data and Standards Compliance section, I can extract information about the types of testing performed and the general conclusions, even if the detailed acceptance criteria and study designs are not fully elaborated.

Here's an attempt to answer using the available information, noting where specific details are missing:

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information: The document does not provide the specific numerical acceptance criteria (e.g., specific thresholds for leachables, precise depth specifications, or quantitative results from the stability/compatibility testing). It states that criteria were "met" or "verified" or "validated."

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

The document mentions "Real-world data analysis: An analysis of post-market data gathered from devices with the modified soft cannula was conducted..."

• Sample Size: Not specified.
• Data Provenance: Retrospective, post-market data. The country of origin is not specified.

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

Not applicable as this was not a diagnostic image-based AI study relying on expert ground truth. The "ground truth" for cannula performance or insulin stability would be derived from laboratory measurements and clinical outcomes, not expert consensus on interpretations.

4. Adjudication Method for the Test Set

Not applicable for this type of device modification study. Adjudication methods like 2+1 or 3+1 typically refer to reconciliation of discrepancies among multiple expert readers in diagnostic studies.

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

No, an MRMC comparative effectiveness study was not mentioned. This type of study is more common for diagnostic imaging AI. The document focuses on performance testing of the device's physical and chemical properties and post-market safety.

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

The device is an insulin pump system with a bolus calculator, not a standalone AI algorithm for diagnosis. Therefore, this question isn't directly applicable in the typical sense of AI standalone performance. The "algorithm" for dose calculation is part of the system's intended function, and its overall safety and effectiveness were assessed as part of the system.

7. The Type of Ground Truth Used

• For Drug Stability and Compatibility: Laboratory analytical results for insulin integrity (e.g., potency, purity, presence of leachables).
• For Soft Cannula Studies: Engineering measurements and specifications for insertion depth and insulin infusion depth.
• For Safety and Effectiveness (Real-world data): Clinical outcomes, adverse event reports, and other post-market surveillance data.
• For Sterilization: Microbiological test results (bioburden) and compliance with sterilization standards.
• For Risk Management: Verification activities demonstrating that risks identified were mitigated to an acceptable level as defined by ISO 14971.

8. The Sample Size for the Training Set

Not applicable. This document describes a modification to an existing device, and the "real-world data analysis" was for post-market validation, not for training a machine learning model in the context of an AI device. The bolus calculator is rule-based and user-programmed, not an AI model requiring a training set in the typical sense.

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

Not applicable, as there's no mention of a traditional "training set" for an AI model.

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The Omnipod DASH™ Insulin Management System is intended for subcutaneous delivery of insulin at set and variable rates for the management of diabetes mellitus in persons requiring insulin.

Additionally. the Omnipod DASH System is interoperable with a compatible blood glucose meter to receive and display glucose measurements.

The Omnipod DASH™ Insulin Management System provides for the management of insulin therapy by patients with diabetes mellitus. It is comprised of two primary components: the disposable insulin infusion pump (Pod), and an associated Bluetooth Low Energy (BLE) enabled remote controller referred to as the Personal Diabetes Manager (PDM). The PDM can communicate with interoperable, compatible BLE enabled blood glucose meters. The PDM incorporates a suggested bolus calculator which aids the user in determining the insulin bolus dosage needed based on carbohydrates ingested, most recent blood glucose reading, programmable correction factor, insulin to carbohydrate ratio, target blood glucose value and Insulin on Board (IoB).

The Pod is a body-wearable insulin pump that affixes to the user on the back of the arm, the lower back or 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 PDM is a handheld device that controls the Pod. The user interfaces with the device system through the PDM using a touch screen, similar to a smartphone, where they control basal and bolus delivery and various insulin program settings and calculations. The PDM also has a food library to assist with carbohydrate calculations, and it maintains several variables in a history log for the viewer to track their diabetes therapy. The device system is for prescription use only.

The provided text describes the Omnipod DASH™ Insulin Management System and its 510(k) submission (K180045) to the FDA. The submission asserts substantial equivalence to a predicate device (K162296 Omnipod® Insulin Management System).

However, the document does not contain a typical acceptance criteria table with reported device performance in a numerical or statistical format that is commonly used for AI/ML-based diagnostic devices. Instead, it focuses on demonstrating safety and effectiveness through compliance with various standards and guidance documents. The "acceptance criteria" are implied by the successful completion of various tests and adherence to regulatory standards.

Here's a breakdown of the requested information based on the provided text, recognizing that it pertains to an insulin pump and not a diagnostic AI/ML device:

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

The document does not explicitly provide a table of acceptance criteria with corresponding performance metrics like sensitivity, specificity, accuracy, or other statistical measures, as would be common for AI/ML diagnostic tools. Instead, the "acceptance criteria" are implied by successful completion of various engineering, safety, and regulatory compliance tests. The "reported device performance" is that the device met these criteria.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document lists various types of tests (bench testing, software V&V, human factors, biocompatibility, safety, EMC) but does not specify the sample sizes for these tests. For instance, for mechanical integrity or software testing, there's no mention of the number of devices or iterations tested. Similarly, no information is provided regarding data provenance (e.g., country of origin, retrospective/prospective). This type of detail is typically contained within the full test reports referenced by the 510(k) summary, but not elaborated here.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in a way that is relevant to AI/ML ground truthing. The "ground truth" for an insulin pump's performance is typically established through engineering specifications, regulatory standards, and clinical outcomes, rather than expert consensus on diagnostic interpretations. The document states that the safety assurance case and human factors validation involved identifying and mitigating risks (suggesting expert input in risk analysis), but it doesn't specify the number or qualifications of experts in the context of "ground truth" as it would for image-based diagnostic AI.

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

This detail is not mentioned. Adjudication methods like "2+1" or "3+1" are usually associated with establishing ground truth in clinical studies, particularly for diagnostic imaging. For an insulin pump, validation often relies on meeting predetermined engineering and safety specifications through direct measurement and testing, rather than a consensus-based adjudication process for interpretations.

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

No MRMC study was performed or is referenced. This type of study is specifically designed for evaluating the impact of AI on human readers in diagnostic tasks, which is not applicable to the Omnipod DASH™ Insulin Management System as it is an insulin delivery device, not a diagnostic AI.

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

The device is an insulin pump, and its "algorithm" (the insulin dose calculator) is inherently designed for human-in-the-loop operation, where the user makes decisions based on the device's calculations and their own blood glucose readings. Therefore, a standalone "algorithm-only" performance study in the context of removing human interaction is not relevant or described. The document does mention "PDM software algorithm error results in errant insulin infusion program on the Pod," indicating that the software's performance is tested, but not as a replacement for human decision-making.

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

For this medical device, the "ground truth" is largely defined by:

• Engineering Specifications: The device must deliver insulin accurately, reliably, and safely according to its design parameters.
• Regulatory Standards: Compliance with international standards (e.g., ISO 14971 for risk management, ISO 10993 for biocompatibility, IEC 60601 series for electrical safety and performance, IEC 62304 for software).
• Safety Assurance Cases: Demonstrating that identified hazards are adequately mitigated and the system is acceptably safe for its intended use.
• Intended Use Validation: Human factors studies confirm that the device can be used safely by the target population.
• Clinical Efficacy (implied by predicate): The device's primary function (insulin delivery for diabetes management) is well-established therapy. The device demonstrates substantial equivalence to a predicate device, implying similar clinical effectiveness.

It is not based on expert consensus, pathology, or outcomes data in the typical sense of a diagnostic AI.

8. The sample size for the training set

Not applicable. The Omnipod DASH™ Insulin Management System is not an AI/ML device that undergoes "training" based on a dataset. It is a programmed medical device that operates based on predefined algorithms and user input.

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

Not applicable, as there is no "training set" for this type of medical device.

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The OmniPod Insulin Management System is intended for subcutaneous delivery of insulin at set and variable rates for the management of diabetes mellitus in persons requiring insulin and for the quantitative measurement of glucose in fresh whole capillary blood (in vitro) from the finger.

The glucose measurements should not be used for the diagnosis of or screening for diabetes. The PDM glucose meter is intended for single-patient use and should not be shared.

Abbott FreeStyle test strips are used with the built-in FreeStyle meter for the quantitative measurement of blood glucose in fresh whole capillary blood from the finger, upper arm and palm. Abbott Freestyle Control Solutions are used to verify that the meter and test strips are working together properly and that the test is performed correctly.

The proposed device provides for the management of insulin therapy and blood qlucose monitoring by patients with diabetes mellitus. It is comprised of two primary components: the disposable insulin infusion pump (Pod) and an associated wireless remote controller referred to as the Personal Diabetes Manager (PDM) with an embedded blood glucose meter. The PDM incorporates a suggested bolus calculator which aids the user in determining the insulin bolus dosage needed based on carbohydrates ingested, most recent blood glucose reading, programmable correction factor, insulin to carbohydrate ratio, target blood glucose value and Insulin on Board (IOB).

The provided text does not contain information about an AI/ML-powered device. Instead, it describes a substantial equivalence determination for the OmniPod Insulin Management System, which is an infusion pump with a built-in glucose meter. The document details modifications to this existing medical device and the performance testing conducted to ensure it remains substantially equivalent to a previously cleared predicate device.

Therefore, I cannot populate the requested table and answer the questions related to AI device acceptance criteria, human reader improvement with AI, or AI ground truth establishment.

The document focuses on the safety and performance of an insulin pump and glucose meter, with no mention of artificial intelligence or machine learning components.

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The t:slim® Insulin Delivery System is indicated for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin, for individuals 6 years of age and greater. The device is indicated for use with NovoLog® or Humalog® U-100 insulin.

The t:flex™ Insulin Delivery System is indicated for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin, for individuals 12 years of age and greater. The device is indicated for use with NovoLog® or Humalog® U-100 insulin.

The Tandem® Device Updater™ System consists of software which allows for communication between a computer and a t:slim® or t:flex™ Insulin Delivery System. It allows for remote software installation and update of a Tandem Insulin Delivery System.

The t:slim® and t:flex™ Insulin Delivery Systems facilitate the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin.

The accessory Tandem Device Updater™ System is comprised of a personal computer application. a web server, an embedded firmware application, and a Tandem pump (t:slim or t:flex). The goal of the Tandem Device Updater System is to provide a secure process for software installation and update.

This document is regarding a 510(k) premarket notification for an insulin delivery system and its updater. It primarily focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a study with specific acceptance criteria and detailed performance data in the typical sense of a diagnostic or screening device.

The "acceptance criteria" and "device performance" in this context are related to the regulatory process of demonstrating safety and effectiveness for substantial equivalence, particularly for software verification/validation and hardware changes.

Here's an attempt to extract and interpret the information based on the provided text, acknowledging that it's a regulatory submission for device changes and not a clinical study report for AI performance.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding device performance metrics in a quantitative format typical for a diagnostic study. Instead, "acceptance criteria" are implied by successful completion of various verification and validation activities. The "reported device performance" is a statement that these tests were performed and supported substantial equivalence.

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

• Test Set Sample Size: Not explicitly stated for any specific test. The document refers to "testing" and "studies" but does not quantify the number of devices or participants used for each.
• Data Provenance: Not explicitly stated as country of origin. The studies were conducted by Tandem Diabetes Care, Inc. for submission to the FDA (USA). The nature of most tests described (software V&V, hardware verification, human factors) suggests a mix of in-house testing and possibly external human factors studies. The document does not specify if the human factors studies were prospective or retrospective, but typically, these are prospective.

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

There is no mention of "experts" in the context of establishing a ground truth for a test set, as this is not a diagnostic device relying on expert interpretation for its output. The "ground truth" for the device's function is its ability to correctly deliver insulin as programmed and function safely according to its specifications.

4. Adjudication Method for the Test Set

Not applicable. This is not a study requiring adjudication of expert interpretations or clinical outcomes.

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

Not applicable. This device is an insulin delivery system and its updater, not an AI-assisted diagnostic or screening tool. Therefore, MRMC studies and "human reader improvement with AI" are not relevant to this submission.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done

The "Tandem® Device Updater™ System" consists of software, and "Software verification and validation testing" was performed. This implies standalone testing of the software component, although the details of such testing are not provided beyond the statement that it was done per FDA guidance. The insulin delivery systems themselves are hardware-software integrated devices, so "standalone" in the AI sense isn't directly applicable.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

The ground truth for this device's performance is based on:

• Engineering Specifications: Successful operation within pre-defined hardware and software specifications.
• Safety Standards: Compliance with relevant safety standards (e.g., electrical safety, EMC, hazard analysis).
• User Performance: Demonstrated ability of users (including pediatric users) to safely and effectively interact with the device as assessed through human factors studies.
• Regulatory Guidance: Adherence to FDA guidance documents for infusion pumps and software V&V.

8. The Sample Size for the Training Set

Not applicable. This document describes a medical device undergoing 510(k) clearance, not a machine learning or AI model that typically requires a "training set" in the computational sense. The software is part of an embedded system, thoroughly tested for compliance with specifications, rather than "trained" on data.

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

Not applicable, as there is no "training set" in the context of an AI/ML model for this device's clearance.

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