Search Results

The ARKRAY Diabetes Management Software is an optional accessory for use with compatible blood glucose meters, such as ARKRAY Glucocard Vital Blood Glucose Meter with data management capabilities. The ARKRAY Diabetes Management Software transfers data from the meter's memory into a secured sever for enhanced data management. ARKRAY ARK Care™ Diabetes Management System is intended for use in home and clinical settings via the internet to assist people with diabetes and their healthcare professionals in uploading, storing, analyzing, and communicating about historical blood glucose test results and other biological statistics to support diabetes management.

ARK Care system serves as an interface between the software in personal glucose monitoring devices and a general purpose health management database to assist in the review, analysis and evaluation of blood glucose test results. The secure system is compliant with HIPAA and HITECH standards.

ARK Care is designed for home use and professional healthcare settings. It is an accessory device to most manufactured model home-use blood glucose monitors, including glucose monitoring devices by ARKRAY, Roche, Bayer, LifeScan, and Abbott. The list of supported devices is located at www.arkcare.net.

The purpose of the electronic diabetes management system is to help users and healthcare teams manage blood glucose information to better regulate diabetes treatments and control blood glucose for better health outcomes. The ARK Care system holds a convenience function, as the user can upload blood glucose data from a variety of currently marketed blood glucose monitors into one location for viewing. After the user transmits and stores blood glucose data to the secure database, the ARK Care system allows family members and/or healthcare professionals to view and monitor the user's data and reports. Family and healthcare team members must receive permission from the primary user and create a password protected login before viewing data. ARK Care provides a safe communication portal for the user, family, and healthcare team to send email-like entries, improving patient care and disease management.

The user and approved healthcare team members can view the blood glucose data in different formats such as logbooks, charts, and graphs. The data can be viewed through selected time intervals and these intervals can be compared over time to track disease management.

The subject can also enter and track other health-related information such as body weight, blood pressure, lab values, and exercise activities. Groups of profiles can be queried for bulk reporting on data related to tracking and trending of outcomes, supporting diabetes disease management in individuals and in managed care organizations.

The provided document is a 510(k) summary for the ARKRAY ARK Care Diabetes Management System. It primarily focuses on the device description, intended use, and substantial equivalence determination based on functional and safety testing and consumer studies. However, it does not contain detailed information about specific acceptance criteria, reported device performance metrics in a tabular format, sample sizes for test sets, data provenance, ground truth establishment methods, or the qualifications of experts that would be expected for a rigorous study proving device performance against acceptance criteria for a diagnostic AI system.

Therefore, I cannot extract the full range of information requested in your prompt based on the provided text. I will address what is available and indicate when information is missing.

Here's an analysis of the available information:

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

This information is not explicitly provided in the document in a tabular format with specific criteria and corresponding performance metrics for a diagnostic AI system. The document mentions "Functional and Safety Testing" and "consumer studies that demonstrated the systems ability to be easily operated by in-home users," but it does not detail specific acceptance criteria or quantitative performance results related to the system's analytical capabilities (e.g., accuracy, sensitivity, specificity for analyzing glucose data patterns or predicting outcomes).

The conclusion states: "Labeling, validation testing results and consumer studies results support the Indications for Use and the claim of substantial equivalence to the predicate (K073699)." This indicates that some form of testing was done to demonstrate the system met its intended purpose and was comparable to the predicate, but the specific performance results against pre-defined acceptance criteria are not elaborated.

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

This information is not provided in the document. The document mentions "consumer studies" and "validation testing results," but it does not specify the sample size for these studies or the provenance of the data used (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 provided. The document describes the system as one to "assist people with diabetes and their healthcare professionals in uploading, storing, analyzing, and communicating about historical blood glucose test results and other biological statistics to support diabetes management." It's a data management and communication system, not a diagnostic AI that generates interpretations requiring expert ground truth in the traditional sense. Therefore, the concept of "ground truth" and expert adjudication for a test set, as might be applied to image analysis or diagnostic algorithms, doesn't directly apply here based on the description of the device.

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

This information is not provided and is likely not applicable given the nature of the device as a data management system rather than a diagnostic AI that generates interpretations needing 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

There is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study. The device is described as assisting users and healthcare teams in managing blood glucose information. It provides tools for viewing data in different formats and a communication portal. It does not appear to be an "AI" in the diagnostic interpretation sense that would involve human readers making diagnoses with and without AI assistance for a study of this type.

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

The document describes the "ARK Care system" as an "interface between the software in personal glucose monitoring devices and a general purpose health management database" and a system "to assist in the review, analysis and evaluation of blood glucose test results." This implies a strong human-in-the-loop component for review and decision-making. The concept of "standalone" algorithm performance, as typically applied to an autonomous diagnostic AI, is not applicable here based on the device description. If there are specific analytical algorithms within the system, their standalone performance is not detailed.

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

This information is not applicable or provided in the context of the device's function as a data management system. For a system that transmits, stores, and presents blood glucose data, the "ground truth" would implicitly be the accurate values reported by the connected blood glucose monitors. The document does not describe any higher-level diagnostic or predictive functions that would necessitate expert consensus, pathology, or outcomes data as ground truth for evaluating its performance.

8. The sample size for the training set

This information is not provided. As the device is described as a data management system and not explicitly as a machine learning/AI model that undergoes a typical training process with labelled data, the concept of a "training set" in that context may not apply or is not detailed.

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

This information is not provided, and as explained above, the concept of a "training set" and its associated ground truth in the context of an AI model is not evident from the description of this device.

In summary, the provided 510(k) summary for the ARK Care™ Diabetes Management System details its intended use as a data management and communication platform for blood glucose data. However, it lacks the specific technical details, performance metrics, and study designs (e.g., sample sizes, ground truth establishment, expert qualifications, adjudication methods) that would be expected when describing the acceptance criteria and a study proving performance for a diagnostic AI device. The document focuses more on the administrative and regulatory aspects of device clearance, particularly establishing substantial equivalence to a predicate device, and general testing for functionality, safety, and usability.

Ask a specific question about this device

The ARKRAY Diabetes Data Management Software (SMBG Viewer) is an optional accessory for use with ARKRAY blood glucose meters with data management capabilities. The ARKRAY Diabetes Data Management Software transfers data from the meter's memory into a computer for enhanced data management. The ARKRAY Diabetes Data Management Software is intended for use in home and clinical settings to assist people with diabetes and their health care professionals in review, analysis and evaluation of historical blood glucose test results to support diabetes management.

The ARKRAY Diabetes Data Management Software is an optional accessory for use with ARKRAY blood glucose meters with data management capabilities. The subject device consists of a USB data transfer cable and software. The system allows the user to download blood glucose results from their glucose meter to their computer, maintain a history of their glucose test results, and convert them into graphs, charts and reports. The software does not recommend any medical treatment or medication dosage level.

The provided document describes the ARKRAY Diabetes Data Management Software (SMBG Viewer), an accessory for ARKRAY blood glucose meters. This software facilitates the transfer, historical tracking, and analysis of blood glucose results.

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

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state specific quantitative acceptance criteria or detailed performance metrics for the software itself, beyond its functional capabilities. The summary focuses on the software's role in data management and its substantial equivalence to a predicate device.

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

The document mentions "consumer studies that demonstrated the systems ability to be easily operated by in-home users." However, it does not specify the sample size for these consumer studies or the data provenance (e.g., country of origin, retrospective or prospective).

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

This information is not provided in the document. Given that the device is data management software for blood glucose meters, the "ground truth" for its primary function (transferring and displaying data accurately) would likely be the data produced by the blood glucose meters themselves, and its accurate visual representation. The consumer studies likely evaluated usability rather than clinical diagnostic accuracy.

4. Adjudication Method for the Test Set

This information is not provided in the document. Adjudication methods are typically relevant for studies where expert interpretation or consensus is required (e.g., image analysis). For data management software, the "adjudication" would likely involve verifying that the software accurately displays the data it receives from the meter.

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 or mentioned. This type of study is usually conducted for diagnostic aids where human readers interpret medical images or complex data, and the AI is designed to assist in that interpretation. This device is a data management tool, not an AI-assisted diagnostic tool.

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

A "standalone" performance evaluation, in the context of diagnostic algorithms, typically refers to the algorithm's performance in isolation. For this device, the "standalone" performance would be its ability to correctly download, store, and display blood glucose data. The document states that "A full array of in-house testing was done consistent with relevant FDA guidance's for blood glucose monitoring systems. Testing included validation of the systems hardware (USB data transfer cable) and software..." This implies that the software's functional performance (without human interaction for its core data handling tasks) was evaluated. However, specific metrics for this standalone performance aren't detailed, beyond successful validation.

7. The Type of Ground Truth Used

For the functional aspects of the software (data transfer, storage, display), the "ground truth" would be the raw data from the blood glucose meters themselves. The validation would verify that the software accurately receives, stores, and presents that exact data. For the "consumer studies," the ground truth was likely subjective user feedback regarding ease of use and satisfaction.

8. The Sample Size for the Training Set

This information is not applicable/not provided. The ARKRAY Diabetes Data Management Software is described as a data management tool, not a machine learning or AI algorithm that requires a training set. Its functionality is based on programmed logic for data transfer and display.

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

This information is not applicable/not provided as the device does not appear to utilize a machine learning model requiring a training set.

Ask a specific question about this device

The Assure Platinum Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.

The Assure Platinum Blood Glucose Monitoring System is not intended for the diagnosis of or screening for diabetes mellitus, and is not intended for use on neonates.

Assure Platinum Blood Glucose Meter:

The Assure Platinum Blood Glucose Meter is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter (OTC)) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.

Assure Platinum Blood Glucose Test Strips:

Assure Platinum test strips are intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips when used with the Assure Platinum Blood Glucose Meter. Testing is done outside the body (In Vitro diagnostic use). They are indicated for use in home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.

Assure Dose CONTROL:

For use with Assure Platinum Blood Glucose Meter and Assure Platinum Blood Glucose Test Strips as a quality control check to verify the accuracy of blood glucose test results. Control solutions are available in two levels - Normal (Level 1) and High (Level 2).

The Assure Platinum Blood Glucose Monitoring System consist of a meter, test strips, and control solution for use as an aid to monitor the effectiveness of diabetes control.

I am sorry, but I lack the ability to answer your request. The provided text is a 510(k) summary for a medical device (Assure Platinum Blood Glucose Monitoring System) and an FDA clearance letter. It describes the device, its intended use, and the fact that functional and safety testing (including clinical testing for accuracy) was performed to support its substantial equivalence to a predicate device. However, the text does not contain detailed information about the specific acceptance criteria for the clinical study, the reported device performance against those criteria, sample sizes used, data provenance, number or qualifications of experts, adjudication methods, or any information regarding MRMC studies, standalone performance, or training set details.

Therefore, I cannot extract the requested information to fill in the table or answer the specific questions about the study design and results.

Ask a specific question about this device

The GLUCOCARD® Vital™ Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips and palms. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.
The GLUCOCARD® Vital™ Blood Glucose Monitoring System is not intended for the diagnosis of or screening for diabetes mellitus, and is not intended for use on neonates.

The GLUCOCARD® Vital™ Blood Glucose Monitoring System consist of a meter, test strips, and control solution for use as an aid to monitor the effectiveness of diabetes control.

The provided text describes the GLUCOCARD® Vital™ Blood Glucose Monitoring System, a device intended for the quantitative measurement of glucose in fresh capillary whole blood.

Here's an analysis based on the provided information:

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

The document only broadly states that "A full array of in-house and clinical testing was done consistent with relevant FDA guidance's for blood glucose monitoring systems." While it mentions evaluations for interferences, dynamic range and linearity, hematocrit effects, altitude effects, control solution functionality, and analytical precision, it does not explicitly list specific numerical acceptance criteria or the reported performance for these criteria. The conclusion states that "clinical testing results support the Indications for Use and the claim of substantial equivalence to the predicate," but no hard numbers are given.

Therefore, a table of acceptance criteria and reported device performance cannot be created from the provided text.

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 mentions "clinical testing included evaluation of accuracy for finger stick and palm test sites for GLUCOCARD® Vital™." However, it does not specify the sample size used for this clinical testing or the data provenance (e.g., country of origin, retrospective or prospective nature of the study).

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

The document does not provide any information about the number or qualifications of experts used to establish ground truth for the test set. For blood glucose monitoring systems, ground truth is typically established using a reference laboratory method, not expert consensus on images.

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

The document does not describe any adjudication method. This is generally not applicable to blood glucose monitoring systems where comparison is made against a laboratory reference method.

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 device is a blood glucose monitoring system, not an AI-assisted diagnostic imaging device that involves human readers interpreting cases. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not applicable and was not performed.

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

The device itself is a standalone blood glucose monitoring system. The accuracy evaluation performed in clinical testing is inherently a "standalone" performance assessment of the device against a reference method. The document does not explicitly state "standalone performance study," but the nature of the device and testing implies it.

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

For a blood glucose monitoring system, the ground truth is typically established by comparing the device's readings to those obtained from a laboratory reference method (e.g., a glucose analyzer in a clinical lab). While the document states "clinical testing included evaluation of accuracy," it does not explicitly name the type of ground truth used, though it can be inferred to be a laboratory reference method common for such devices.

8. The sample size for the training set:

This device is a traditional medical device (a glucose meter) and does not inherently involve machine learning algorithms that would require a "training set" in the computational sense. Therefore, the concept of a training set sample size is not applicable to this device.

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

As the concept of a training set is not applicable, this question is not relevant.

Ask a specific question about this device

The PocketChem EZ Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips and palms. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.

The PocketChem EZ Blood Glucose Monitoring System is not intended for the diagnosis of or screening for diabetes mellitus, and is not intended for use on neonates.

The PocketChem EZ Blood Glucose Monitoring System consists of a meter, test strips, and two levels of control solutions for use as an aid to monitor the effectiveness of diabetes control.

Here's a breakdown of the acceptance criteria and study information for the PocketChem EZ blood glucose monitoring system, based on the provided FDA 510(k) summary:

Please note: The provided document is a 510(k) summary, which is a high-level overview. It typically does not contain the granular detail found in a full study report or detailed FDA review. Therefore, some specific details, particularly around statistical methods, exact sample sizes for each test type, and expert qualifications for ground truth determination, are not explicitly stated in this summary.

Acceptance Criteria and Reported Device Performance

The 510(k) summary indicates that "Bench testing included evaluation of interferences, linearity, linear range, hematocrit effects, altitude effects, control solution functionality, and analytical precision. Clinical testing included evaluation of accuracy for finger stick and palm test sites."

However, the provided text does not explicitly list the quantitative acceptance criteria for these tests (e.g., specific bias limits, precision CVs, or accuracy percentages within certain zones). It only states that the "bench testing results and clinical testing results support the Indications for Use and the claim of substantial equivalence to the predicate."

Without the specific numerical acceptance criteria, it's impossible to create a table with "Reported Device Performance" alongside defined "Acceptance Criteria." The summary implies that the device met internal and FDA-recommended criteria for blood glucose monitoring systems, but these criteria are not detailed here.

Study Details

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

   • Not explicitly provided in the document. The 510(k) summary states that "A full array of in-house and clinical testing was done consistent with relevant FDA guidance's for blood glucose monitoring systems." This implies that the device met the generally accepted performance standards for blood glucose meters at the time for accuracy (e.g., ISO 15197 or FDA guidance documents), precision, linearity, and interference. However, the specific numerical targets and results are not included in this summary.

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

   • Sample Size: Not explicitly stated for specific tests. The summary mentions "clinical testing," which typically involves patient samples. FDA guidance for blood glucose meters usually requires a minimum number of subjects (e.g., 100-150 subjects) for accuracy studies.
   • Data Provenance: Not explicitly stated. Given ARKRAY Factory USA, Inc. is based in Minneapolis, MN, it is likely that at least some, if not all, clinical data was collected in the USA, but this is an inference.
   • Retrospective or Prospective: Not explicitly stated, but clinical accuracy studies for medical devices are generally 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)

   • Not applicable in the same way as imaging studies. For blood glucose meters, the "ground truth" (reference method) is typically established by laboratory-based analyzers (e.g., YSI analyzer) operated by trained laboratory technicians, not by expert consensus or interpretations of images by clinicians. The summary does not specify the number or qualifications of the individuals performing the reference measurements, but it's assumed they were trained laboratory personnel.

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

   • Not applicable. Adjudication methods like 2+1 or 3+1 are typically used in studies where human readers are making subjective interpretations (e.g., radiologists reviewing images) and their disagreements need to be resolved. For objective measurements like blood glucose, a highly accurate laboratory reference method (the "ground truth") is used against which the device's readings are compared.

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. This is not relevant for a blood glucose monitoring system. MRMC studies are used for diagnostic imaging devices where human interpretation plays a role and the AI might assist that interpretation. The PocketChem EZ is a direct measurement device.

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

   • Yes, implicitly. The accuracy and precision studies performed for blood glucose meters are standalone evaluations of the device's performance against a reference method. The device provides a quantitative measurement directly, without requiring human interpretation of an output that needs to be "assisted." The clinical testing for "accuracy for finger stick and palm test sites" refers to this standalone performance.

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

   • Laboratory Reference Method: For blood glucose monitoring systems, the ground truth is established by a highly accurate laboratory-based reference method, such as a YSI glucose analyzer, which is considered the gold standard for glucose measurement in clinical laboratories. The summary doesn't explicitly name the reference method but "clinical testing included evaluation of accuracy" implies comparison to such a standard.

8. The sample size for the training set

   • Not applicable for this type of device. Blood glucose meters are not typically "trained" in the way AI algorithms are. Their underlying chemical/enzymatic reactions and electronic algorithms are designed and calibrated during development, not "trained" on large datasets of patient results in the sense of machine learning.

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

   • Not applicable. As above, there isn't a "training set" with ground truth in the machine learning sense for a blood glucose meter. The device's calibration and performance are validated against standard glucose solutions and patient samples measured by a reference method.

Ask a specific question about this device

The Assure Pro Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control. The Assure Pro Blood Glucose Monitoring System is not intended for the diagnosis of or screening for diabetes mellitus, and is not intended for use on neonates.

The Assure Pro Blood Glucose Monitoring System consists of a meter, test strips, and two levels of control solutions for use as an aid to monitor the effectiveness of diabetes control.

This document describes the Assure Pro Meter Test Strips and its associated system, cleared under K090332.

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

The provided document does not explicitly state numerical acceptance criteria in a table format for the Assure Pro Meter Test Strips. It broadly mentions "Functional and Safety Testing" and "Clinical testing included evaluation of accuracy for the finger stick". Without clear, quantifiable acceptance criteria from the 510(k) summary, specific reported performance against these criteria cannot be detailed.

However, the "Conclusion" states: "Labeling, bench testing results and clinical testing results support the Indications for Use and the claim of substantial equivalence to the predicate." This implies that the device's performance, as evaluated through these tests, was deemed acceptable by the FDA for its intended use.

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 mentions "clinical testing included evaluation of accuracy for the finger stick" but does not specify the sample size used for this clinical test set.

The data provenance (country of origin, retrospective/prospective) is also not explicitly stated in the provided text.

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. For blood glucose monitoring systems, ground truth is typically established by laboratory reference methods, not by human expert interpretation of images.

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

This information is not provided in the document. Adjudication methods are typically relevant for studies involving human interpretation, not direct quantitative measurements like blood glucose.

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 mentioned. This type of study is not applicable as the device is a blood glucose monitoring system, not an AI-powered diagnostic imaging tool requiring human reader interpretation.

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

The device is a standalone system – a blood glucose meter with test strips. Its performance ("accuracy for the finger stick") would inherently be evaluated without human interpretation of the measurement itself. The human-in-the-loop is the user performing the test and reading the numerical result.

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

The document does not explicitly state the specific type of ground truth used, but for blood glucose monitoring systems, the ground truth is typically established by laboratory reference methods for glucose measurement (e.g., from a central lab or a highly accurate analyzer) against which the device's readings are compared.

8. The sample size for the training set

The device is a traditional medical device (blood glucose meter and test strips), not an AI/machine learning algorithm that requires a "training set" in the computational sense. Therefore, this concept does not apply, and no training set size is mentioned.

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

As the device is not an AI/machine learning algorithm with a training set, this question is not applicable.

Ask a specific question about this device

The GLUCOCARD™ 01-mini Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, or palm. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.

GLUCOCARD™ 01-mini consists of a meter, test strips, and control solutions for use in measuring blood glucose as an aid to monitor the effectiveness of diabetes control.

The provided text describes the ARKRAY GLUCOCARD™ 01-mini Blood Glucose Monitoring System and mentions a clinical study to evaluate system accuracy. However, it does not provide specific acceptance criteria or detailed results of that study, nor does it include information about sample sizes, ground truth establishment, or expert involvement in the way an AI/ML device study would.

Therefore, many of the requested fields cannot be filled directly from the provided text. I will indicate where information is missing.

Here's a breakdown based on the provided text, with missing information noted:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size (Test Set): Not specified in the provided text. The text only mentions "A clinical study was done with persons with diabetes."
• Data Provenance: Not explicitly stated (e.g., country of origin). The study involved "persons with diabetes." Given the submitter's location (Minneapolis, MN, USA), it's highly likely the study was conducted in the USA, but this is an inference, not stated fact. The study was retrospective.

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

• Number of Experts: Not applicable/Not specified. For a blood glucose monitoring system, the "ground truth" is typically established by a laboratory reference method, not by expert consensus on images or interpretations.
• Qualifications of Experts: Not applicable/Not specified.

4. Adjudication method for the test set

• Adjudication Method: Not applicable/Not specified. This concept (e.g., 2+1, 3+1) is relevant for studies involving human readers and interpretations (like radiology), not for direct measurement devices where a reference standard provides the "truth."

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study: No. This is a blood glucose monitoring system, not an AI/ML diagnostic tool meant to assist human readers in interpreting complex data.
• Effect Size: Not applicable.

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

• Standalone Performance: The "system accuracy" evaluated in the clinical study likely refers to the standalone performance of the device (meter + test strips) against a laboratory reference method. The text states: "A clinical study was done... to evaluate system accuracy." Analytical verification also covered precision, dynamic range, and linearity, which relate to standalone performance characteristics.

7. The type of ground truth used

• Type of Ground Truth: For blood glucose monitors, the ground truth is typically a laboratory reference method (e.g., a YSI Analyzer or equivalent) that is highly accurate and precise, performed on the same blood samples as the device under test. The document does not explicitly name the reference method but implies one was used to determine "system accuracy."

8. The sample size for the training set

• Training Set Sample Size: Not applicable/Not specified. This device is not described as involving machine learning or AI that would require a distinct "training set" in the conventional sense. The "Functional and Safety Testing" likely refers to validation cohorts.

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

• Training Set Ground Truth: Not applicable. Since there's no mention of a training set for an AI/ML algorithm, this question is not relevant to the provided text.

Ask a specific question about this device

The GLUCOCARD™ 01 Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, or palm. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.

GLUCOCARD™ 01 Blood Glucose Meter:

The GLUCOCARD™ 01 Blood Glucose Meter is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, or palm. GLUCOCARD™ 01 SENSOR Blood Glucose Test Strips must be used with the GLUCOCARD™ 01 Blood Glucose Meter. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.

GLUCOCARD™ 01 SENSOR Blood Glucose Test Strips:

GLUCOCARD™ 01 SENSOR Blood Glucose Test Strips are intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, or GLUCOCARD™ 01 SENSOR Blood Glucose Test Strips must be used with the palm. GLUCOCARD™ 01 Blood Glucose Meter. Testing is done outside the body (In Vitro diagnostic use). They are indicated for use at home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.

CIECOCARD" 01 CONTROLA

For use with GLUCOCARD™ 01 Blood Glucose Meter and GLUCOCARD™ 01 SENSOR Blood Glucose Test Strips as a quality control check to verify the accuracy of blood glucose test results. Control solutions are available in three levels - Low (L), Normal (N) and High (H).

The GLUCOCARD 01 Blood Glucose Monitoring System consists of a meter, sensor test strips, and control solutions for use as an aid to monitor the effectiveness of diabetes control.

The ARKRAY GLUCOCARD™ 01 Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples. The acceptance criteria and supporting study details are as follows:

1. Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state numerical acceptance criteria in a table format that compares against device performance. However, it indicates "a full array of in-house and clinical testing was done consistent with relevant FDA guidance's for blood glucose monitoring systems." The conclusion states that "bench testing results and clinical testing results support the Indications for Use and the claim of substantial equivalence to the predicate," the Advance Micro-draw BGM.

Based on typical FDA expectations for blood glucose monitoring systems, common accuracy criteria include:

• ISO 15197:2003 (older standard applicable around 2008):
  • 95% of results must be within ±15 mg/dL of the laboratory reference method for glucose concentrations

Ask a specific question about this device

The GLUCOCARD X-METER Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, forearm, upper arm or palm. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.

The GLUCOCARD X-METER Blood Glucose Monitoring System consists of a meter, sensor test strips, and control solutions for use as an aid to monitor the effectiveness of diabetes control.

The provided text describes the GLUCOCARD X-METER Blood Glucose Monitoring System. However, it does not contain the specific details regarding acceptance criteria, study design, sample sizes, expert qualifications, or ground truth establishment that would be needed to fully answer your request.

Here's what can be extracted and what information is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

The document mentions "Functional and Safety Testing" including "clinical testing included evaluation of accuracy for both finger stick and Alternate Site Testing." However, it does not provide the specific acceptance criteria (e.g., ISO 15197 compliance, specific accuracy percentages at different glucose ranges) nor the actual reported device performance metrics (e.g., bias, precision, percentage of readings within a certain range of a reference method).

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

The document states "clinical testing included evaluation of accuracy." However, it does not specify the sample size (number of patients or readings) used for the test set, nor the data provenance (e.g., country of origin, retrospective or prospective nature of the data collection).

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

The document mentions "clinical settings by healthcare professionals" as an intended use but does not provide information on how many experts were used to establish the ground truth for the test set, nor their specific qualifications. For glucose meters, the "ground truth" (reference method) is typically a laboratory-based analyzer, not expert consensus on the reading itself.

4. Adjudication Method for the Test Set:

Given that the ground truth for glucose meters is typically a laboratory reference method, an adjudication method for the test set (e.g., 2+1, 3+1) is not applicable in the same way it would be for image-based diagnostic devices. The device reading is compared directly to the reference method.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study is not applicable to a blood glucose meter. This type of study is relevant for diagnostic devices that involve human interpretation of images or other complex data. Blood glucose meters provide a direct numerical output.

6. Standalone Performance Study:

Yes, a standalone performance study was implicitly done. The "clinical testing included evaluation of accuracy" refers to the device's performance in measuring glucose levels. This is a standalone evaluation of the algorithm and device without a human-in-the-loop scenario. The output is a direct glucose reading from the device.

7. Type of Ground Truth Used:

The type of ground truth used would almost certainly be a laboratory reference method (e.g., a YSI STAT PLUS Glucose & L-Lactate Analyzer or similar) for measuring glucose in blood samples. This is standard practice for blood glucose meter accuracy studies. The document does not explicitly state this, but it is the industry standard for this type of device.

8. Sample Size for the Training Set:

The document does not provide any information about a training set size. For a device like a blood glucose meter, the "training set" might refer to the data used during the development and calibration of the electrochemical sensor and algorithm. This information is typically proprietary and not included in a 510(k) summary.

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

Similar to the training set size, the document does not provide information on how the ground truth for the training set was established. This would relate to the methods used during the device's development and calibration, likely involving comparisons to laboratory reference methods with known concentrations.

In summary, while the document confirms clinical testing for accuracy was performed, it lacks the detailed quantitative information on acceptance criteria, specific performance metrics, sample sizes, and the exact nature of the ground truth required to fully answer your request. This level of detail is often found in the full 510(k) submission and supporting study reports, which are more extensive than the summary provided.

Ask a specific question about this device