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GLP systems Track:

The GLP systems Track is a modular laboratory automation system designed to automate pre-analytical and post-analytical processing, including sample handling, in order to automate sample processing in clinical laboratories. The system consolidates multiple analytical instruments into a unified workflow.

Alinity i Total β-hCG Reagent Kit:

The Alinity i Total β-hCG assay is a chemiluminescent microparticle immunoassay (CMIA) used for the quantitative and qualitative determination of beta-human chorionic gonadotropin (ß-hCG) in human serum and plasma for the early detection of pregnancy on the Alinity i analyzer.

Alinity i system:

The Alinity i System is a fully automated analyzer allowing random and continuous access, as well as priority and automated retest processing using chemiluminescent microparticle immunoassay (CMIA) technology is used to determine the presence of antibodies, and analytes in samples.

Alinity ci-series:

The Alinity ci-series is intended for in vitro diagnostic use only.

The Alinity ci-series is a System comprised of inity i or Alinity c analyzers/processing modules that may be arranged into individual or multimodule configurations including up to four Alinity i processing modules, up to four Almity c processing modules, or a combination of up to four of Alinity c processing modules with a shared system control module to form a single workstation.

The Alinity c System is a fully automated, random/continuous access, climical chemistry analyzer intended for the in vitro determination of analytes in body fluids.

The Alinity i System is a fully automated analyzer allowing random and continuous access, as well as priority and automated retest processing using chemiluminescent microparticle immunoassay (CMIA) technology is used to determine the presence of antibodies, and analytes in samples.

The GLP systems Track is a modular laboratory automation system (LAS) used to perform multiple pre-analytical and post-analytical steps to automate sample preparation and distribution processes in clinical laboratories. These processes include bar code identification of samples, centrifugation, aliquoting of samples, decapping of samples, transport of samples between processes (modules), delivery of samples to 1 or more Abbott and Third Party commercially available laboratory analyzer(s), capping of samples, and storage of samples. Due to the modular nature of the LAS, customers may select modules and configurations to fit their laboratory needs.

The provided text describes the 510(k) premarket notification for the GLP systems Track and the Alinity i Total β-hCG Reagent Kit. The focus of the acceptance criteria and study detailed in the document is on the GLP systems Track laboratory automation system, and its ability to maintain the performance of connected analyzers, specifically exemplified with the Alinity i Total β-hCG assay. The document does not provide specific acceptance criteria or performance data for the Alinity i Total β-hCG Reagent Kit as a standalone diagnostic assay; instead, it focuses on the GLP systems Track's compatibility and non-inferiority when integrated with such assays.

Here's a breakdown of the information based on your request:

Acceptance Criteria and Reported Device Performance

The document describes a method comparison study to demonstrate that the GLP systems Track does not negatively impact the performance of connected assays. The acceptance criteria are implicitly defined by the results of this method comparison.

Table of Acceptance Criteria and Reported Device Performance (Implicit for the GLP systems Track):

* **Slope:** 0.99
* **Correlation Coefficient:** 1.00                                                                                                                                                                       |

| Ensure acceptable performance for a representative immunoassay. | Demonstrated with the Alinity i Total β-hCG assay. |

Study Details

1. Sample Size Used for the Test Set and Data Provenance:

   • Sample Size: Not explicitly stated as a number of samples. The range of mIU/mL for the tested samples is given as 4.78 to 14,965.80 mIU/mL, indicating a broad range of concentrations were tested.
   • Data Provenance: The study was described as "Nonclinical testing was performed on-site at Abbott." This indicates an internal, prospective study. Country of origin is implicitly the US, where Abbott Laboratories is located.

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

   • Not applicable. This was a method comparison study for a laboratory automation system, not a diagnostic study requiring human expert interpretation of results to establish ground truth. The "ground truth" was established by comparing direct loading (comparator method) to processing via the GLP systems Track (investigational method) using established laboratory procedures.

3. Adjudication Method for the Test Set:

   • Not applicable. As this was a method comparison of automated systems, there was no human adjudication process involved. The comparison was based on quantitative measurements.

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

   • No. An MRMC study is typically for image-based diagnostic aids where human readers interpret cases. This study focused on the performance of a laboratory automation system.

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

   • Yes, in essence. The study assessed the performance of the GLP systems Track (an automated system) without human intervention in the analytical process, demonstrating its ability to deliver results comparable to direct sample loading. The Alinity i Total β-hCG assay itself is a standalone quantitative assay.

6. The Type of Ground Truth Used:

   • Reference Method Comparison/Comparator Method. The "ground truth" was established by testing specimens on the Alinity i Total β-hCG assay when front-loaded (the comparator method/reference) and comparing those results to specimens loaded using the GLP systems Track (investigational method). This essentially assumes that the front-loaded method provides the accurate measurement.

7. The Sample Size for the Training Set:

   • Not applicable. The GLP systems Track is a mechanical/software automation system designed for sample processing, not an algorithm that undergoes "training" with data in the typical machine learning sense to learn patterns or make predictions. Its "training" would be through engineering design, development, and testing processes. The document does not mention any machine learning or AI components that would require a training set.

8. How the Ground Truth for the Training Set was Established:

   • Not applicable. (See point 7).

In summary, the provided document focuses on demonstrating the substantial equivalence of the GLP systems Track to its predicate and its ability to integrate with and maintain the performance of an existing cleared assay (Alinity i Total β-hCG) through a nonclinical method comparison study.

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The GLP systems Track is a modular laboratory automation system designed to automate pre-analytical and postanalytical processing, including sample handling, in order to automate sample processing in clinical laboratories. The system consolidates multiple analytical instruments into a unified workflow.

The Alinity c System is a fully automated, random/continuous access, clinical chemistry analyzer intended for the in vitro determination of analytes in body fluids.

The Alinity c ICT (Integrated Chip Technology) is used for the quantitation of sodium, and chloride in human serum, plasma, or urine on the Alinity c analyzer.

Sodium measurements are used in the diagnosis and treatment of aldosteronism (excessive secretion of the hormone aldosterone), diabetes insipidus (chronic excretion of large amounts of dilute urine, accompanied by extreme thirst), adrenal hypertension, Addison's disease (caused by destruction of the adrenal glands), dehydration, inappropriate antidiuretic hormone secretion, or other diseases involving electrolyte imbalance.

Potassium measurements are used to monitor electrolyte balance in the diagnosis and treatment of diseases conditions characterized by low or high blood potassium levels.

Chloride measurements are used in the diagnosis and treatment of electrolyte and metabolic disorders such as cystic fibrosis and diabetic acidosis.

The GLP systems Track is a modular laboratory automation system (LAS) used to perform multiple pre-analytical and post-analytical steps to automate sample preparation and distribution processes in clinical laboratories. These processes include bar code identification of samples, centrifugation, aliquoting of samples, decapping of samples, transport of samples between processes (modules), delivery of samples to 1 or more Abbott and Third Party commercially available laboratory analyzer(s), capping of samples, and storage of samples. Due to the modular nature of the LAS, customers may select modules and configurations to fit their laboratory needs.

The provided text describes a 510(k) premarket notification for the "GLP systems Track" device. However, it does not contain the detailed acceptance criteria for performance, the study that proves the device meets those criteria, or information on sample sizes for test/training sets, expert qualifications, or adjudication methods.

The document states that "Nonclinical testing was performed on-site at Abbott to ensure the product met the requirements and aligned with the quality system. This testing included design verification, including both software and hardware verification, as well as design validation. Testing was performed for Chain of Custody of the sample ID, and a Method Comparison study comparing the use of the GLP systems Track to a manual method was also performed. Additionally, Electromagnetic Compatibility and Electrical Safety testing was completed."

This broadly indicates that testing was conducted, but the specific details requested in your prompt (Acceptance Criteria, reported performance, sample sizes, expert involvement, etc.) are absent from this regulatory summary.

Therefore, I cannot populate the table or answer most of your questions based on the information provided.

Here's what I can extract based on the limited information:

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

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

• Sample size for test set: Not specified.
• Data provenance: "Nonclinical testing was performed on-site at Abbott." The country of origin and retrospective/prospective nature are not specified.

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)

• Not specified. The testing mentions "Chain of Custody of the sample ID" and a "Method Comparison study comparing the use of the GLP systems Track to a manual method," but details on ground truth establishment and expert involvement are absent.

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

• Not specified.

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 specified. This device is a modular laboratory automation system, not typically a device that involves human readers interpreting results in the same way as, for example, a medical imaging AI. The "human-in-the-loop" aspect does not directly apply here in the context of interpretation improvement assisted by AI.

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

• The document presents the "GLP systems Track" as a modular laboratory automation system. Its performance evaluation would likely focus on its ability to automate pre-analytical and post-analytical processing steps accurately and efficiently, rather than "algorithm-only" performance in the sense of a diagnostic AI. A "Method Comparison study comparing the use of the GLP systems Track to a manual method" was performed, which implies a comparison of the automated system's output to a reference method, but details are not provided.

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

• For the "Method Comparison study comparing the use of the GLP systems Track to a manual method," the "manual method" likely serves as the reference or ground truth. No further details are provided on its establishment.

8. The sample size for the training set

• Not specified. (This device is a hardware/software system for lab automation, not an AI model in the common sense that requires a "training set" for machine learning, although its software components would certainly undergo extensive testing and validation.)

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

• Not applicable/Not specified, as there is no mention of a "training set" in the context of machine learning model development.

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The Glooko Mobile Insulin Dosing System (MIDS) is indicated for the management of type 2 diabetes by calculating appropriate long-acting basal insulin doses for titrating insulin levels based on configuration by a physician or healthcare provider knowledgeable in the care and management of diabetes. The physician or healthcare provider must activate the MIDS dose calculator and configure the patient-specific parameters. The system is not intended to provide treatment decisions or to be used as a substitute for professional healthcare advice.

The Glooko MIDS system will be an optional new module to support the titration of long acting basal insulin doses. Health care providers (HCPs) will be able to opt-in to this new MIDS module and use it with a subset of their patients. Although the Glooko App may interact with Blood Glucose (BG) meters, insulin pumps and Continuous Glucose Meters (CGM), the MIDS interface will get data ONLY from the BG meters.

Glooko MIDS consists of the following two interfaces:

• MIDS Provider Interface on the Glooko Web Application for use by HCP's to prescribe long acting insulin doses for their patients
• MIDS Patient interface on the Glooko mobile application for use by patients on compatible iOS and Android phones

Glooko MIDS provides directions to the patient based on a pre-planned treatment program as suggested by their HCP for titrating long acting insulin doses. Glooko MIDS is for titrating long acting insulin doses only.

The provided document is a 510(k) Summary for the Glooko Mobile Insulin Dosing System (MIDS). It describes the device, its indications for use, comparison to predicate and reference devices, and performance data to demonstrate substantial equivalence.

However, the document does not contain specific details about acceptance criteria or a clinical study proving the device meets those criteria in the format requested (e.g., a table of performance metrics, sample sizes, expert qualifications, etc.).

Instead, it states that:

• "The Glooko MIDS software was validated pursuant to the Major Level of Concern requirements."
• "Design validation testing and human factors study results confirmed that the Glooko MIDS software performs according to the stated intended use."
• "Software evaluation consisted of functional testing performed pursuant to Glooko's design verification protocol. All of the software tests were documented according to FDA's guidance document - Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices (May 11, 2005). All test results fell within the pre-determined specification parameters and acceptance criteria."

This indicates that internal functional and design validation testing, along with human factors validation, was performed, and acceptance criteria were met. However, the specific acceptance criteria, detailed performance metrics, sample sizes for these tests, information about ground truth establishment, or expert involvement are not explicitly provided in the public 510(k) summary.

Therefore, I cannot populate the requested table and details because the information is not present in the provided text. The document refers to "pre-determined specification parameters and acceptance criteria" but does not detail what those are, nor does it provide the results of any specific study (like an MRMC study or standalone performance study measuring clinical accuracy vs. human performance) that would typically prove such criteria were met.

Based on the provided text, I can state the following limitations in responding to your request:

• No explicit table of acceptance criteria and reported device performance is available. The document only states that "All test results fell within the pre-determined specification parameters and acceptance criteria," without listing them.
• Sample sizes for the test set are not specified.
• Data provenance (country of origin, retrospective/prospective) is not specified for any performance testing.
• Number and qualifications of experts for ground truth establishment are not specified.
• Adjudication method for the test set is not specified.
• No Multi-Reader Multi-Case (MRMC) comparative effectiveness study is described with effect sizes.
• No standalone (algorithm-only) performance metrics are detailed beyond stating functional tests were performed.
• The type of ground truth used is not specified. (It's a software for calculating insulin doses based on physician configuration, implying correctness would be against a predefined algorithm or manual calculation, not necessarily pathological or outcomes data in a clinical sense for this type of device).
• Sample size for the training set is not applicable/specified as this is not an AI/ML device that requires a training set in the typical sense. It states "Software evaluation consisted of functional testing," implying rule-based or logic-based software rather than a learned model.
• How ground truth for the training set was established is not applicable/specified.

The submission focuses on demonstrating substantial equivalence to a predicate device (Insulia Diabetes Management Companion) through similar indications for use, technological characteristics, and "functional testing." It is not a submission for a novel AI/ML device requiring extensive clinical validation against human performance or a complex ground truth.

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The Smart Dongle Blood Glucose Monitoring System consists of the Smart Dongle meter, single test strips, and the Smart Dongle mobile application as the display component of the Smart Dongle Blood Glucose Monitoring System. The Smart Dongle Blood Glucose Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood from the finger. This blood glucose monitoring system is intended to be used by a single person and should not be shared. Smart Dongle Blood Glucose Monitoring System is intended for selftesting outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. This system should not be used for the diagnosis of or screening for diabetes, nor for use on neonates.

The system consists of blood glucose meter, test strips and mobile platform (as a display of the system). And, the blood glucose meter is compatible to iPhone series, including iPhone 4, iPhone 4s, iPhone 5, iPhone 5s, iPhone 6, iPhone 6 plus, iPhone 6s plus. These products have been designed, tested, and proven to work together as a system to produce accurate blood glucose test results. Smart Dongle Blood Glucose Test Strips can be used only with the Smart Dongle Blood Glucose Monitoring System.

This document is a 510(k) premarket notification for the Smart Dongle Blood Glucose Monitoring System. It describes the device, its intended use, and performance characteristics to establish substantial equivalence to a predicate device.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Smart Dongle Blood Glucose Monitoring System appear to be based on accuracy performance metrics, specifically comparing the device's readings to reference measurements. While explicit "acceptance criteria" are not phrased as such, the results presented imply the thresholds the device aims to meet.

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The iHealth Wireless Gluco-Monitoring System consists of the iHealth Wireless Glucose meter (BG5), iHealth Blood Glucose Test Strips (AGS-1000), and the iHealth Gluco-Smart App mobile application. The iHealth Wireless Gluco-Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertip, palm, forearm, upper arm, calf, or thigh. The iHealth Wireless Gluco-Monitoring System is intended to be used by a single person and should not be shared.

The iHealth Wireless Gluco-Monitoring System is intended for self-testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The iHealth Wireless Cluco-Monitoring System should not be used for the diagnosis of or screening of diabetes or for neonatal use. Alternative site testing should be done only during steady-state times (when glucose is not changing rapidly).

The iHealth wireless Smart Gluco-Monitoring System(BG5) consist of blood qlucose meter, single use test strips, sterile lancets, lancing device and the control solutions.

They are based on an electrochemical biosensor technology (electrochemical) and the principle of capillary action. Capillary action at the end of the test strip draws the blood into the action chamber and the blood glucose result is displayed in 5 seconds. The control solution available is used to test the performance of the device. It uses the same technological characteristics for testing with its predicate device.

In order to use theiHealth wireless Smart Gluco-Monitoring System(BG5), a compatible Android or iOS mobile device with the necessary mobile application installed is required.

The provided text describes a glucose monitoring system, not an AI device. As such, the typical acceptance criteria and study elements associated with AI are not directly applicable.

However, I can extract information related to the performance criteria and validation study for the iHealth Wireless Smart Gluco-Monitoring System (BG5) as presented in the 510(k) summary. This device is a blood glucose meter, and its performance is assessed against accuracy standards for glucose measurements.

Here's an interpretation based on the provided document:

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

The document doesn't explicitly state "acceptance criteria" in a table format with "reported device performance." However, it implies that the device meets the performance of its predicate device, as claimed in section 8.0. For glucose meters, the typical acceptance criteria are established by standards like ISO 15197 (Accuracy requirements for blood glucose monitoring systems for self-testing in managing diabetes mellitus). While these specific ISO criteria are not directly listed as "acceptance criteria" here, the declaration of "substantial equivalence" implies adherence to such recognized performance standards.

The document states:

• Measurement Range: 20mg/dL-600mg/dL (1.1mmol/L~33.3mmol/L)
• Test Time: 5 seconds
• Sample Volume: Minimum 0.7 microliter
• Hematocrit Range: 20-60%

The comparison table (Section 7.0) indicates that the new device has the same characteristics as the predicate device (K123935) for these performance parameters, implying it meets the same performance as the predicate.

The key statement regarding meeting criteria is in Section 8.0: "the test in this submission provides demonstration that these small differences [connect to Android] do not raise any new questions of safety and effectiveness." This implies that the device's accuracy and performance are considered equivalent to the predicate, which would have met its own set of performance criteria, likely aligning with ISO 15197 for glucose meters.

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

The document does not explicitly state the sample size used for performance testing (e.g., number of subjects, number of blood samples). It also doesn't specify the country of origin of the data or whether the study was retrospective or prospective. It only mentions "the test in this submission."

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. For glucose meters, the "ground truth" is typically established by laboratory reference methods (e.g., YSI analyzer) rather than expert interpretation, so the concept of experts establishing ground truth in this context is less relevant.

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

Not applicable, as this is for a glucose meter and not an AI or imaging device requiring human expert 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:

Not applicable, as this is a standalone glucose meter, not an AI-assisted diagnostic tool.

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

Yes, the device itself is a standalone glucose monitoring system. Its performance is evaluated on its ability to accurately measure glucose levels independent of human interpretation or AI assistance. The output is a numerical glucose reading.

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

For a glucose meter, the ground truth is established by comparison to a laboratory reference method, typically a YSI glucose analyzer, which is considered highly accurate for quantitative glucose measurement in blood. This is standard for glucose meter validation, though not explicitly detailed in this summary.

8. The sample size for the training set:

Not applicable. This device is not an AI model requiring a "training set." It's a hardware medical device with electrochemical sensing technology.

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

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

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The iHealth Align Gluco-Monitoring System consists of the iHealth Align Glucose meter (BG1), iHealth Blood Glucose Test Strips (AGS-1000), and the iHealth Gluco-Smart App mobile application as the display component of the iHealth Align Gluco-Monitoring System. The iHealth Align Gluco-Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertip, upper arm, calf, or thigh. The iHealth Align Gluco-Monitoring System is intended to be used by a single person and should not be shared.

The iHealth Align Gluco-Monitoring System is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The iHealth Align Gluco-Monitoring System should not be used for the diagnosis of or screening of diabetes or for neonatal use. Alternative site testing should be done only during steady - state times (when glucose is not changing rapidly).

The iHealth Align Gluco-Monitoring System(BG1) consist of blood glucose meter, single use test strips, sterile lancets, lancing device and the control solutions.

They are based on an electrochemical biosensor technology (electrochemical) and the principle of capillary action. Capillary action at the end of the test strip draws the blood into the action chamber and the blood glucose result is displayed in 5 seconds. The control solution available is used to test the performance of the device. It uses the same technological characteristics for testing with its predicate device.

In order to use the iHealth Align Gluco-Monitoring system(BG1) , a compatible Android or iOS mobile device with the necessary mobile application installed is required.

The provided text describes the iHealth Align Gluco-Monitoring System (BG1) and its substantial equivalence to a predicate device. However, it does not include detailed acceptance criteria or a specific study proving the device meets those criteria in the format requested.

The document is a 510(k) summary for a glucose monitoring system, focusing on demonstrating substantial equivalence to a previously cleared device. It highlights the technological characteristics and intended use. While it mentions performance characteristics like measurement range and test time, it doesn't present a table of acceptance criteria with corresponding device performance for a specific study.

Therefore, I can extract information related to the device's characteristics and the submission's intent, but I cannot fulfill all sections of your request directly from the provided text as the specific clinical study data (including sample sizes, ground truth establishment, expert qualifications, and adjudication methods for acceptance criteria) is not present.

Here's a breakdown of what can be extracted and what cannot:

Information that CANNOT be extracted from the provided text:

• A table of acceptance criteria and the reported device performance because the document focuses on demonstrating substantial equivalence through technological comparison rather than presenting the results of a primary clinical validation study against predefined acceptance metrics.
• Sample sizes used for the test set and data provenance for a specific clinical validation study.
• Number of experts used to establish the ground truth and their qualifications.
• Adjudication method for the test set.
• Whether a multi-reader multi-case (MRMC) comparative effectiveness study was done or its effect size.
• Whether a standalone (algorithm only) performance study was done. (This device is a physical glucose meter, not an AI algorithm.)
• The sample size for the training set. (This is a medical device, not an AI algorithm that typically has a "training set" in the machine learning sense.)
• How the ground truth for the training set was established.

Information that CAN be extracted or inferred:

1. Acceptance Criteria and Reported Device Performance:

While a formal "acceptance criteria" table is not provided, the document lists key performance characteristics assumed to meet regulatory expectations. The predicate device's performance, which the new device is compared against, indirectly sets the "acceptance criteria" for substantial equivalence.

7. Type of Ground Truth Used:
The device measures chemical properties (glucose levels). The "ground truth" for such devices is typically established through a laboratory reference method (e.g., using a YSI glucose analyzer) on the same blood sample. While not explicitly stated, this is the standard for glucose meter validation.

The 510(k) summary explicitly states that the submission aims to demonstrate that "these small differences [referring to the expanded compatibility with Android devices] do not raise any new questions of safety and effectiveness," thus proving its substantial equivalence to the predicate device. This implies that the device performance for glucose measurement itself is considered equivalent to the predicate, which would have undergone its own rigorous testing against a reference standard.

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The iHealth Align Gluco-Monitoring System consists of the iHealth Align Glucose meter (BG1), iHealth Blood Glucose Test Strips (AGS-1000), and the iHealth Gluco-Smart App mobile application as the display component of the iHealth Align Gluco-Monitoring System. The iHealth Align Gluco-Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertip, palm, forearm, upper arm, calf, or thigh. The iHealth Alian Gluco-Monitoring System is intended to be used by a single person and should not be shared.

The iHealth Align Gluco-Monitoring System is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The iHealth Align Gluco-Monitoring System should not be used for the diagnosis of or screening of diabetes or for neonatal use. Alternative site testing should be done only during steady - state times (when glucose is not changing rapidly).

The iHealth BG5 wireless Smart Gluco-Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertip, palm, forearm, upper arm, calf or thigh. The iHealth BG5 wireless Smart Gluco-Monitoring System is intended to be used by a single person and should not be shared.

The iHealth BG5 wireless Smart Gluco-Monitoring System is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The iHealth BG5 wireless Smart Gluco-Monitoring System should not be used for the diagnosis of or screening of diabetes or for neonatal use. Alternative site testing should be done only during steady state times (when glucose is not changing rapidly).

The AGS-1000I test strips are for use with the iHealth BG5 meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips, palm, forearm, upper arm, calf or thigh.

The iHealth BG5L wireless Smart Gluco-Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertip, palm, forearm, upper arm, calf or thigh. The iHealth BG5L wireless Smart Gluco-Monitoring System is intended to be used by a single person and should not be shared.

The iHealth BG5L wireless Smart Gluco-Monitoring System is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The iHealth BG5L wireless Smart Gluco-Monitoring System should not be used for the diagnosis of or screening of diabetes or for neonatal use. Alternative site testing should be done only during steady state times (when glucose is not changing rapidly).

The AGS-1000I test strips are for use with the iHealth BG5L meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips, palm, forearm, upper arm, calf or thigh.

The iHealth Align Gluco-Monitoring System consists of a blood glucose meter, test strips, iHealth Gluco-Smart App, sterile lancets, lancing device and AGS-1000I Control Solutions (Level I. Level II and Level III). The iHealth Align Gluco-Monitoring System cannot display test results and must be used with an iPhone or iPod touch via an 3.5 mm auxiliary jack.

The iHealth BG5 wireless Smart and iHealth BG5L wireless Smart Gluco-Monitoring Systems consist of the BG5 and BG5L wireless Smart blood glucose meters, respectively, AGS-10001 Test Strips , sterile lancets, lancing device and the iHealth control solutions control solutions. (Control solutions provided are for Level 1, II, and III). iHealth BG5L uses Bluetooth 4.0 wireless radio technology; while iHealth BG5 uses Bluetooth 3.0 wireless radio technology. The iHealth BG5 and BG5L meters can display the test results and the test results can also be transmitted to an iPhone, iPod touch or iPad through blue tooth.

iHealth Gluco-Smart App is iOS- based software for use with the iHealth Align Glucose meter (BG1), iHealth BG5 meter, and iHealth BG5L meter. When used with these meters, iHealth Gluco-Smart App acts as a display and allows command and control of the meter. The App can transfer data from the device's memory, manage, and share the data.

Here's an analysis of the provided text, focusing on acceptance criteria and study details for the iHealth Gluco-Monitoring Systems:

The provided documents are a 510(k) premarket notification for the iHealth Align Gluco-Monitoring System, iHealth BG5 wireless Smart Gluco-Monitoring System, and iHealth BG5L wireless Smart Gluco-Monitoring System. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than undergoing extensive clinical trials typical of novel devices. Therefore, the "acceptance criteria" and "study" described are primarily focused on proving that the new devices perform comparably to the predicate device and meet relevant regulatory standards for glucose monitoring systems.

1. Table of Acceptance Criteria and Reported Device Performance

The documents do not explicitly state a table of "acceptance criteria" in the format of specific thresholds for metrics like sensitivity, specificity, or accuracy (e.g., within X% of a reference standard for Z% of readings). Instead, the acceptance criteria are implicitly tied to the performance characteristics of the predicate device and general regulatory expectations for glucose monitoring systems.

The performance is primarily summarized by stating that the new devices share key characteristics with the predicate and that "Software validation and user study has been performed to establish the performance, the functionality and the reliability characteristics of the new device."

Here's an attempt to infer and present the information in a table format based on the textual evidence:

2. Sample Size for the Test Set and Data Provenance

The document mentions "user study" but does not specify the sample size for any clinical or test set. It also does not explicitly state the country of origin of the data or whether it was retrospective or prospective. Given the nature of a 510(k) for a glucose monitoring system, user studies often involve a diverse cohort to assess performance across various glucose levels and user demographics. However, these specific details are absent from the provided text.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not mention the use of experts to establish ground truth for a test set. For glucose monitoring systems, "ground truth" is typically established by comparing the device's readings against a highly accurate laboratory reference method (e.g., YSI glucose analyzer), rather than expert adjudication of images or clinical reports. The general term "user study" is used, which implies participants used the device and its performance was evaluated against a reference.

4. Adjudication Method for the Test Set

As the document does not describe a process involving experts to establish ground truth for a test set in the traditional sense, there is no mention of an adjudication method like 2+1 or 3+1. Performance is likely assessed against a laboratory reference.

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

No. The provided text does not describe an MRMC comparative effectiveness study where human readers improve with AI vs. without AI assistance. This type of study is more common in diagnostic imaging or clinical decision support AI devices where human interpretation is a key component. The iHealth devices are standalone glucose meters; while an app is involved, it primarily acts as a display and data management tool, not an AI for interpretation.

6. Standalone (Algorithm Only) Performance Study

Yes, implicitly. The core performance of the glucose measurement algorithm itself (i.e., the meter and test strip system) is evaluated. The 510(k) process for glucose meters typically requires studies demonstrating the accuracy of the device's readings against a laboratory reference method. Although the document uses the broad term "performance summary," this usually entails standalone accuracy data. The phrase "Software validation and user study has been performed to establish the performance, the functionality and the reliability characteristics of the new device" suggests that the device's ability to accurately measure glucose without a human in the interpretative loop was a key part of the validation.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used. However, for glucose monitoring systems, the ground truth is almost universally established using a laboratory reference method (e.g., a YSI glucose analyzer) that is considered the gold standard for glucose measurement.

8. Sample Size for the Training Set

The document does not mention a "training set" sample size. This is expected because the iHealth Gluco-Monitoring Systems, as described, do not appear to be AI/Machine Learning devices that require a "training set" in the context of learning algorithms. They are likely electrochemical biosensors with pre-defined algorithms for glucose calculation. The app primarily handles data display and management.

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

As there is no mention of a training set, there is no information on how its ground truth was established. The device likely relies on established physical and chemical principles of glucose measurement rather than a trained AI model.

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The Gmate® SMART Blood Glucose Monitoring System is intended for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips, forearm, palm, thigh or calf. The Gmate® SMART Blood Glucose Monitoring System is intended to be used by a single person and should not be shared.

The Gmate® SMART Blood Glucose Monitoring System is intended for self-testing outside the body (in vitro diagnostic use) by people with diabetes at home to monitor the effectiveness of diabetes control. The Gmate® SMART should not be used for the diagnosis of or screening of diabetes or for neonatal use. Alternate site testing should be done only during steady state times (when glucose is not changing rapidly).

The Gmate® Blood Glucose Test Strips are with the GMATE® SMART meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips, forearm, upper arm, palm, thigh or calf.

The Gmate™ SMART App is a component of the Gmate® SMART Blood Glucose Monitoring System and is intended to be used by people with diabetes at home as an aid to monitor and track the effectiveness of their diabetes management. The Gmate™ SMART App allows the user to view their glucose test results and store a lifetime of results. The user may e-mail their glucose test results to their healthcare provider to help them review, analyze, and evaluate their glucose test results to support an effective diabetes management program. The user can also graph and trend their glucose test results to provide an outlook of their diabetes management.

The Gmate® SMART Blood Glucose Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood. The Gmate® SMART System is intended for self-testing outside the body (in vitro diagnostic use only) by people with diabetes at home as an aid to monitor the effectiveness of their diabetes management. The Gmate® SMART Blood Glucose Monitoring System consists of a glucose meter, test strips, and one control material (additional levels of control material are available upon request). The small Gmate® SMART meter does not require coding or calibration, no need of batteries, and no settings are required. The Gmate® SMART meter is powered on by plugging it into the headphone jack of the Apple/Android device. Insert the test strip into the meter, apply the blood or control solution to the strip and the meter will begin the 5 seconds count down of your test result. The Gmate™ SMART App converts the signal generated from the meter and test strip and displays the test result on the Apple/Android device. The Gmate® SMART Blood Glucose Monitoring System uses the smartphone technology, currently with Apple's iOS (with use of the Apple iPhone 3GS, iPhone 4, iPhone 4S, iPhone 5, iPod Touch 4th generation, iPad, and iPad2) and now with the following Android devices: Samsung Galaxy S3, S4, and S5; to view glucose test results. A simple download of the Gmate® SMART App, enables use of many functions. The Gmate™ SMART App is a component of the Gmate® SMART Blood Glucose Monitoring System and is intended to be used by people with diabetes at home as an aid to monitor and track the effectiveness of their diabetes management. The Gmate™ SMART App allows the user to view their glucose test results and store a lifetime of results. The user may e-mail their glucose test results to their healthcare provider or healthcare provider to help them review, analyze, and evaluate their glucose test results to support an effective diabetes management program. The user can also graph and trend their glucose test results to provide an outlook of their diabetes management. The test principle is: This device is an in vitro diagnostic only product intended for the measurement of glucose concentration in human blood. The principle of the test relies upon a specific type of glucose in the blood sample, the glucose oxidase that reacts to electrodes in the test strip. The test strip employs an electrochemical signal generating an electrical current that will stimulate a chemical reaction. The meter measures the current and calculates your blood glucose level. Combined with the Gmate™ SMART App, it displays the test result and stores them on your Apple/Android device.

Here’s an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

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

The document doesn't explicitly state quantitative acceptance criteria in a table format for accuracy. However, it does state an overall conclusion: "The results demonstrated that the Gmate® SMART met all the reliability requirements and performance claims." and "Based on the comparisons completed of the clinical and non-clinical tests performed, the devices passed all of the tests based on pre-determined Pass/Fail Criteria."

To infer the acceptance criteria for accuracy, we typically refer to ISO 15197 for blood glucose monitoring systems. While not directly stated, the study implies adherence to these standards by mentioning "FDA Guidance Documents and CLSI reference standards." The YSI 2300 Glucose Analyzer is the widely accepted reference method for accuracy in these types of studies. The performance is reported as meeting "all the reliability requirements and performance claims" and "passed all of the tests based on pre-determined Pass/Fail Criteria."

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

• Sample Size for Test Set: "Glucose levels were measured on 100 persons with diabetes and healthcare professionals at a clinic center."
• Data Provenance: The document does not explicitly state the country of origin where the clinical study took place. It mentions Philosys, Inc. has a New York Office and a Seoul Office (South Korea). It also states "clinical tests... were performed in accordance with FDA Guidance Documents and CLSI reference standards," which suggests the study was designed to meet US regulatory requirements. The study appears to be prospective as it involves performance evaluations on individuals, rather than analyzing existing datasets.

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):

• The ground truth was established using the YSI 2300 Glucose Analyzer, a laboratory instrument.
• The document mentions "healthcare professionals at a clinic center" were involved in measuring glucose levels, but it doesn't specify the number or specific qualifications of these professionals for establishing the ground truth. Their role was likely to operate the YSI 2300 and collect samples, not to interpret or adjudicate results in the way an "expert" would for image-based diagnostics.

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

• No adjudication method (like 2+1 or 3+1 consensus) is described. This is expected for a blood glucose monitoring system where the YSI 2300 Glucose Analyzer itself serves as the objective reference standard, not human interpretation.

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 comparative effectiveness study was done. This type of study is typically relevant for interpretative devices (e.g., radiology AI) where human readers make diagnoses. The Gmate® SMART is a quantitative measurement device, not an interpretative one in the sense of an MRMC study.

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

• Yes, a standalone performance evaluation was done. The device's accuracy was assessed by comparing its glucose readings directly against a laboratory reference instrument (YSI 2300 Glucose Analyzer). While users operate the device, the core performance being evaluated is the algorithm/system's ability to accurately measure glucose, independent of human interpretive bias. The study also included "user performance" which might incorporate human interaction, but the primary accuracy assessment is standalone.

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

• The ground truth used was reference laboratory measurement from a YSI 2300 Glucose Analyzer. This is considered a highly accurate and objective standard for glucose concentration.

8. The sample size for the training set:

• The document does not provide information about a training set. This is common for this type of device, where analytical and clinical performance studies are conducted, rather than machine learning model training in the typical sense. The device's underlying measurement principle is electrochemical, not a learned algorithm requiring a separate training set.

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

• As no training set is mentioned or implied, this question is not applicable based on the provided text.

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The ACCU-CHEK Connect Diabetes Management App is indicated as an aid in the treatment of diabetes. The software provides for electronic download of blood glucose meters, manual data entry, storage, display, transfer, and self-managing of blood glucose and other related health indicators which can be shown in report and graphical format.

The ACCU-CHEK Bolus Advisor, as a component of the ACCU-CHEK Connect Diabetes Management App, is indicated for the management of diabetes by calculating an insulin dose or carbohydrate intake based on user-entered data. Before its use, a physician or healthcare professional must activate the bolus calculator and provide the patient-specific target blood glucose. insulin-to-carbohydrate ratio, and insulin sensitivity parameters to be programmed into the software.

The ACCU-CHEK Connect Diabetes Management App is designed to facilitate efficient collecting, transmitting, and analyzing of blood glucose results and other diabetes management data. The App helps:

Wireless transfer of data from ACCU-CHEK Aviva Connect Blood Glucose Meter. Assist in general diabetes management through logging of contextual data. ACCU-CHEK Bolus Advisor support of mealtime insulin dosing calculations. Perform structured testing. Wireless transfer of data from mobile devices to ACCU-CHEK Connect Online Diabetes Management System and optionally share this data with healthcare provider (HCP) or caregiver.

The insulin bolus calculations provided by the app are meant for patients undergoing multiple daily injection therapy. Bolus calculators, such as the ACCU-CHEK Bolus Advisor, have been demonstrated to facilitate the optimization of glycemic control in patients who are trained in multiple daily insulin injection therapy and under the supervision of healthcare professional experienced in managing insulin-treated patients. Such calculators have also been shown to reduce patient fear of hypoglycemia and improve patient confidence in diabetes management.

The ACCU-CHEK Connect Diabetes Management App is not intended to serve as an accessory to an insulin pump.

The provided document describes a 510(k) premarket notification for the "ACCU-CHEK Connect Diabetes Management App" for iOS platform. The submission aims to demonstrate substantial equivalence to an existing predicate device, the Android OS version of the same app (K141929). The core of the argument for substantial equivalence relies on the fact that the bolus calculator algorithm and intended use have not changed, and the modifications are primarily related to adapting the app to the iOS operating system.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with numerical targets and corresponding performance metrics for the modified device. Instead, the acceptance is based on demonstrating that the iOS version performs equivalently to the already cleared Android version. The performance is assessed by confirming that the changes do not introduce new hazards or alter the core functionality.

• Electronic download of blood glucose meters
• Manual data entry
• Storage, display, transfer, and self-managing of blood glucose and other related health indicators
• ACCU-CHEK Bolus Advisor for insulin dose/carbohydrate intake calculation
• Structured testing
• Wireless transfer of data to ACCU-CHEK Connect Online Diabetes Management System
• Bolus calculator algorithm is unchanged from the predicate device.
• Bolus calculator activation prescription control process, activation, and patient training materials, and user interface screens (related to bolus calculator) are unchanged from the predicate device. |
  | Safety - Risk Assessment | A risk analysis according to ISO 14971 was carried out. Potential faulty conditions and hazards were systematically identified and evaluated using "Failure Mode Effect and Criticality Analysis." Adequate protection measures were implemented. The risk assessment for the iPhone version "relied heavily" on the risk assessment performed for the Android OS version. Post-launch monitoring of the Android version did not identify possible faulty conditions leading to hazards for the patient. |
  | Performance Requirements | "Design verification bench testing on the modification of ACCU-CHEK Connect Diabetes Management App demonstrated that the device meets the performance requirements for its intended use." (Specific metrics are not provided, as the claim is equivalence to the predicate). |
  | Human Factors | An "expert evaluation" (human factors) was performed to show that the predicate design validation can be used to support the iPhone version's design validation. This evaluation used side-by-side comparison of screenshots between Android and iOS versions to review changes.
• Changes reviewed were due to inherent differences between OS user interface standards.
• Enhancements based on results of Android version summative and iPhone version formative human factors study.
• Changes to validation study tasks.
  "No new use-related hazard was identified during the expert evaluation." |

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

• Test Set Sample Size: The document does not specify a numerical sample size for a "test set" in the traditional sense of a performance study with patient data.
  • For functional and performance requirements: "Design verification bench testing" was conducted, but no sample size for this testing is provided. The testing aimed to confirm that the device meets performance requirements, likely through technical validation.
  • For Human Factors: The human factors evaluation was an "expert evaluation" involving a comparison of screenshots and review of changes. It does not appear to be a study with user participants from a specific test set.
• Data Provenance: Not applicable in the context of this submission, as it largely focuses on the technical equivalence between two versions of the same software for different operating systems. The core bolus algorithm, if it was validated with patient data, would have been done for the predicate device.

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

• Human Factors: An "Human Factors expert" performed the evaluation. The exact number of experts (singular or plural often used generically) is not explicitly stated if it was more than one, nor are their specific qualifications (e.g., years of experience, specific certifications) detailed, beyond them being "Human Factors expert".
• Other Testing: The document does not describe the establishment of a "ground truth" for a test set in the context of clinical outcomes or diagnostic accuracy, as the device is a diabetes management app with an unchanged bolus calculator algorithm. The "ground truth" for the bolus calculation would have been established during the development and validation of the predicate device's algorithm, adhering to medical and physiological principles of insulin dosing.

4. Adjudication Method for the Test Set

Not applicable. There is no mention of an adjudication method, as the studies described (bench testing, expert human factors evaluation) do not involve subjective interpretation or a need for external consensus on a "ground truth" for clinical cases in this specific 510(k) submission.

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. An MRMC comparative effectiveness study was not done. This type of study is typically used for diagnostic imaging devices where human readers interpret medical images with and without AI assistance. The ACCU-CHEK Connect Diabetes Management App is a diabetes management software, not an imaging diagnostic device, and thus this methodology is not relevant to its validation as described here.

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

Yes, in essence, the predicate device's bolus calculator algorithm would have undergone standalone validation. The current submission explicitly states: "The insulin bolus calculator algorithm is unchanged as compared to the predicate device." This implies that the standalone performance of the algorithm itself was established during the predicate's clearance. The "Design verification bench testing" for the modified app would primarily verify that the implementation of this unchanged algorithm on the new platform correctly computes the same results.

7. The Type of Ground Truth Used

For the bolus calculator functionality (which is based on the unchanged algorithm from the predicate):

• The ground truth would be based on established medical and physiological principles for insulin dosing, as programmed into the algorithm's parameters (e.g., target blood glucose, insulin-to-carbohydrate ratio, insulin sensitivity, insulin action profiles). The accuracy of its calculations would be verified against these predefined parameters and typically, in a predicate device, against known physiological models or expert-derived reference calculations.

For the human factors evaluation:

• The "ground truth" was implicitly the established safety and usability of the predicate (Android) version, and the goal was to ensure the iOS version maintained this without introducing new hazards. The "expert evaluation" served as the primary method to assess this.

8. The Sample Size for the Training Set

The document does not mention a training set or its sample size. This submission is for a software modification (porting to a new OS) rather than the development of a new AI/ML algorithm that typically requires a large training set. The bolus calculator algorithm is based on predefined physiological equations and parameters, not on machine learning models trained on vast datasets.

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

Not applicable, as there is no mention of a training set for an AI/ML model in this submission. The bolus calculator algorithm's "ground truth" (i.e., its correctness) would have been established at the time of the predicate device's development through clinical and algorithmic validation against medical standards and physiological models, as it is a rule-based system, not a data-driven learning system.

Ask a specific question about this device

The ACCU-CHEK Connect Diabetes Management App is indicated as an aid in the treatment of diabetes. The software provides for electronic download of blood glucose meters, manual data entry, storage, display, transfer, and self-managing of blood glucose and other related health indicators which can be shown in report and graphical format.

The ACCU-CHEK Bolus Advisor, as a component of the ACCU-CHEK Connect Diabetes Management App, is indicated for the management of diabetes by calculating an insulin dose or carbohydrate intake based on user-entered data. Before its use, a physician or healthcare professional must activate the bolus calculator and provide the patient-specific target blood glucose. insulin-to-carbohydrate ratio, and insulin sensitivity parameters to be programmed into the software.

The ACCU-CHEK Connect Diabetes Management App is designed to facilitate efficient collecting, transmitting, and analyzing of blood glucose results and other diabetes management data. The App helps:

• Wireless transfer of data from ACCU-CHEK Aviva Connect Blood Glucose Meter.
• Assist in general diabetes management through logging of contextual data.
• ACCU-CHEK Bolus Advisor support of mealtime insulin dosing calculations.
• Perform structured testing.
• Wireless transfer of data from mobile devices to ACCU-CHEK Connect Online Diabetes Management System and optionally share this data with healthcare provider (HCP) or caregiver.

The insulin bolus calculations provided by the app are meant for patients undergoing multiple daily injection therapy. Bolus calculators, such as the ACCU-CHEK Bolus Advisor, have been demonstrated to facilitate the optimization of glycemic control in patients who are trained in multiple daily insulin injection therapy and under the supervision of healthcare professional experienced in managing insulin-treated patients. Such calculators have also been shown to reduce patient fear of hypoglycemia and improve patient confidence in diabetes management.

The ACCU-CHEK Connect Diabetes Management App is not intended to serve as an accessory to an insulin pump.

Here's an analysis of the provided text regarding the acceptance criteria and study for the ACCU-CHEK Connect Diabetes Management App, structured as requested:

Acceptance Criteria and Device Performance for ACCU-CHEK Connect Diabetes Management App

The provided FDA 510(k) summary (K141929) for the ACCU-CHEK Connect Diabetes Management App primarily focuses on demonstrating substantial equivalence to a predicate device (ACCU-CHEK Aviva Combo meter). While it mentions "performance requirements" and "algorithm validation," it does not explicitly state specific quantitative acceptance criteria (e.g., in terms of accuracy, sensitivity, specificity, or precision) with corresponding reported device performance values in a table. Instead, it refers to a qualitative assessment that the device "meets the performance requirements for its intended use" and "demonstrated that the device functions as intended."

The document emphasizes that the Bolus Advisor algorithm within the app is "unchanged as compared to the predicate device." Therefore, the performance of the algorithm is implicitly tied to the cleared performance of the predicate.

Here's an attempt to structure the available information, noting the absence of explicit quantitative criteria in the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for the Test Set and Data Provenance

The document mentions "software testing and performance testing of the device" and a "Human Factors clinical study."

• Software and Performance Testing: No specific sample size (e.g., number of test cases, specific data points) is provided for the device's main software and performance testing.
• Human Factors Clinical Study: No specific sample size (e.g., number of participants) is provided for the "Human Factors clinical study."
• Data Provenance: The document does not specify the country of origin of the data or whether the tests were retrospective or prospective. Given it's a 510(k) submission to the FDA, it's highly likely that the studies were conducted with data relevant to the US market or in a manner acceptable to the FDA.

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

This information is not provided in the given document. The submission focuses on demonstrating substantial equivalence to a predicate device whose bolus calculation algorithm is adopted directly. The Human Factors study involved "persons with diabetes mellitus or their caregivers," but these are considered users, not experts establishing ground truth for algorithmic performance.

4. Adjudication Method for the Test Set

This information is not provided in the given document. Given the nature of a software application for diabetes management and bolus calculation, adjudication might not be relevant in the same way it would be for diagnostic imaging where expert consensus is often used. For software functionality, ground truth often comes from predefined requirements and expected outputs based on established medical formulas.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done as described in the provided text. The device is a "Drug Dosing Calculator" and a "diabetes management app," not an AI-assisted diagnostic tool that would typically involve human readers interpreting cases. The document states that the Bolus Advisor algorithm is unchanged from the predicate device, implying that its effectiveness has already been established and accepted with that predicate. No effect size of human improvement with AI assistance is mentioned because this type of study was not conducted or reported.

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

Yes, implicitly, a standalone assessment of the algorithm was done. The document states: "The insulin bolus calculator algorithm is unchanged as compared to the predicate device." This means the algorithm's performance was previously validated in its standalone form within the predicate device (ACCU-CHEK Aviva Combo meter). The current submission leverages that prior validation. There's no new, separate standalone study explicitly described for the ACCU-CHEK Connect Diabetes Management App, beyond the confirmation that it uses the same algorithm.

7. The Type of Ground Truth Used

For the bolus calculation algorithm, the ground truth would be based on established medical formulas and diabetes management guidelines for insulin dosing and carbohydrate intake calculations. The accuracy of these calculations against the established formulas would have been the ground truth for the predicate device. For the ACCU-CHEK Connect App, the ground truth for its software functionality relies on validated software requirements and the expected output of its operations.

8. The Sample Size for the Training Set

This information is not applicable/not provided in the context of this device. The ACCU-CHEK Connect Diabetes Management App, particularly its Bolus Advisor, is a rule-based system employing an "unchanged" algorithm from a predicate device. It is not an AI/Machine Learning model that would typically have a "training set" in the conventional sense. The algorithm is based on well-defined clinical parameters (target blood glucose, insulin-to-carbohydrate ratio, insulin sensitivity, etc.) provided by a healthcare professional.

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

As noted above, this device does not appear to involve machine learning or AI that would require a "training set" with ground truth established through typical methods like expert annotation or pathology. The "ground truth" for the bolus calculation algorithm stems from established medical science and clinical practice guidelines for insulin dosing, which determine the correct output for given input parameters. The validation of such an algorithm would involve testing it against a wide range of clinically relevant scenarios, where the "correct" insulin dose is derived from these established medical principles.

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