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The Accu-Chek Safe-T-Pro Plus lancing device is intended to produce a capillary blood sample for testing utilizing small amounts of blood.

The Accu-Chek Safe-T-Pro Plus lancing device is a sterile, single-use, disposable lancing device intended to be used by non-professional users 18 years and older and healthcare professionals. It is designed for capillary blood sampling from the fingertip of adults and children 1 year and older or, if the patient is a child under 1 year, from the heel. The Accu-Chek Safe-T-Pro Plus lancing device is a needle used for capillary blood sampling for use in diagnostic testing. The Accu-Chek Safe-T-Pro Plus lancing device contains a sharps injury prevention feature where the lancet is retracted and concealed before and after use. Once the lancet is used, it is rendered inoperative. The device is designed for single use only. It has a 23 Gauge needle with 3 depth levels by twisting cap: 1.3 mm, 1.8 mm, 2.3 mm. It is spring-driven and loading/priming is not required. Press release button to activate lancet mechanism.

The provided text is a 510(k) premarket notification for the "Accu-Chek Safe-T-Pro Plus Lancing Device". It focuses on establishing substantial equivalence to predicate and reference devices rather than presenting specific acceptance criteria and a detailed study proving the device meets them in the context of performance metrics like sensitivity, specificity, or reader improvement, which are typical for AI/ML devices.

However, based on the information provided, I can extract the non-clinical testing performed and infer the acceptance criteria and details about the study.

Here's a breakdown of the requested information based on the provided text:

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

The document does not explicitly state numerical acceptance criteria or performance metrics (like accuracy, sensitivity, specificity) for comparison within a table, as one would expect for an AI/ML device. Instead, it describes "nonclinical bench testing" and "design verification and validation testing" to ensure risk management and mechanical function. The "reported device performance" is broadly stated as "performs as well or better than the legally marketed predicate device and legally marketed reference devices."

However, we can infer acceptance criteria related to safety and functionality from the types of testing mentioned and the characteristics of the device.

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 primarily describes non-clinical bench testing. It does not provide details on a "test set" in the context of clinical data for performance metrics. For the "nonclinical bench testing," the sample size of devices tested is not specified. The data provenance is also not specified, but it would typically be internal testing conducted by the manufacturer. Since it's bench testing, concepts of retrospective or prospective data usually don't apply in the same way as clinical studies.

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 section is not applicable as the document describes a physical medical device (lancing device) and its mechanical and safety performance, not an AI/ML algorithm that requires expert-established ground truth for a test set. There is no mention of experts or ground truth in this context.

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

This section is not applicable for the same reasons as point 3. Adjudication methods are typically used in clinical studies involving interpretation of data, often by multiple readers, which is not the case here.

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

This section is not applicable. The device is a "lancing device," a physical tool for blood sampling. It does not involve AI or software for diagnostic interpretation, and therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is entirely outside the scope of this device.

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

This section is not applicable as the device is a lancing device and does not involve any algorithm or software with standalone performance.

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

This section is not applicable. For a lancing device, the "ground truth" for its performance would be its physical properties, sterility, mechanical reliability, and safety features, which are evaluated through engineering tests and quality control, not clinical "ground truth" like pathology or outcomes data in the sense of diagnostic accuracy.

8. The sample size for the training set

This section is not applicable. The device is not an AI/ML product and does not have a "training set."

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

This section is not applicable for the same reason as point 8.

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The Accu-Chek Safe-T-Pro Uno lancing device is intended to produce a capillary blood sample for testing utilizing small amounts of blood.

The Accu-Chek Safe-T-Pro Uno lancing device is a sterile, single-use, disposable lancing device intended to be used by non-professional users 18 years and older and healthcare professionals. It is designed for capillary blood sampling from the fingertip of adults and children 1 year and older or, if the patient is a child under 1 year, from the heel.

The provided text is an FDA 510(k) premarket notification for the Accu-Chek Safe-T-Pro Uno Lancing Device. It does not describe a study with acceptance criteria in the manner typically found in clinical trials or AI/software validation studies. Instead, it details a "non-clinical bench testing" approach to demonstrate substantial equivalence to a predicate device.

Here's an analysis based on your request, highlighting what is and isn't available in the provided text:

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

The document does not explicitly present a table of acceptance criteria alongside reported device performance for specific metrics. Instead, it refers to "design verification and validation testing" performed per "applicable FDA Guidance documents (Sharps Injury Prevention Features) and special controls (878.4850)." The conclusion states the device "performs as well or better than the legally marketed predicate device and legally marketed reference devices."

The closest approximation to "device performance" mentioned is:

• The Accu-Chek Safe-T-Pro Uno lancing device is designed for a single use only and has a sharps injury prevention feature where the lancet is retracted and concealed before and after use, and is rendered inoperative after use.
• It uses a 28 Gauge needle with a 1.5 mm depth and is spring-driven.
• Loading/priming is not required; activation is via a press release button.

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

The document does not specify a "sample size used for the test set" or "data provenance" in terms of subject populations or data collection methods (retrospective/prospective). This is because the described testing is "non-clinical bench testing," meaning it was likely conducted in a laboratory setting on the device itself, rather than on human subjects or clinical data.

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

This is not applicable as no "ground truth" established by experts for a test set is mentioned. The testing involves mechanical and design verification, not expert evaluation of results from human or image data.

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

Not applicable. No adjudication method is mentioned as there's no expert review of outcomes.

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

Not applicable. A MRMC comparative effectiveness study is relevant for AI or diagnostic imaging devices evaluating human performance. This document is for a medical device (lancing device) and does not involve AI or human readers.

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

Not applicable. This is not an AI or algorithm-based device.

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

Not applicable in the conventional sense of clinical ground truth. The "ground truth" for the non-clinical bench testing would be engineering specifications, regulatory standards (like ISO or FDA special controls for sharps injury prevention), and the performance characteristics of the legally marketed predicate and reference devices.

8. The sample size for the training set

Not applicable. This is not an AI/machine learning device that requires a training set.

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

Not applicable. This is not an AI/machine learning device.

Summary of what is present:

• Type of Study: Non-clinical bench testing (design verification and validation testing).
• Purpose: To demonstrate the mechanical functions, safety (sharps injury prevention), and performance are suitable for use and are substantially equivalent to legally marketed predicate and reference devices.
• Applicable Standards: FDA Guidance documents (Sharps Injury Prevention Features) and special controls (878.4850).
• Conclusion: The device is "safe and effective for its intended use, and performs as well or better than the legally marketed predicate device and legally marketed reference devices."
• Clinical Testing: "Not applicable; risk analysis confirmed that all identified risks were addressed and mitigated appropriately."

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The Accu-Chek Safe-T-Pro Plus lancing device is a sterile, single-use, disposable lancing device intended to be used by healthcare professionals. It is designed for capillary blood sampling from the fingertip of adults and children 1 year and older or, if the patient is a child under 1 year, from the heel.

The Accu-Chek Safe-T-Pro Plus lancing device is a sterile, single-use, disposable lancing device intended to be used by healthcare professionals. It is designed for capillary blood sampling from the fingertip of adults and children 1 year and older or, if the patient is a child under 1 year, from the heel.

The provided text is a 510(k) summary for a medical device called the "Accu-Chek Safe-T-Pro Plus Lancing Device." It describes the device, its intended use, and substantial equivalence to a predicate device.

However, the document does not contain any information about acceptance criteria for an AI-powered device, nor does it describe a study involving an AI algorithm.

The document specifically states under "Non-Clinical and/or Clinical Tests Summary & Conclusions":

"Clinical Testing is not applicable; risk analysis confirmed risks were addressed and mitigated appropriately. All residual risks after mitigation were acceptable, and communicated in the instructions for use as warnings. There were no special performance or safety concerns identified. See Risk documents provided in Biocompatability section."

Therefore, I cannot provide the requested information about an AI device's acceptance criteria and study proving it meets those criteria based on this document. The device in question is a physical lancing device, not an AI software.

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The IWL2020 Blood Glucose Monitoring System is comprised of the IWL2020 meter and IWL2020 test strips. The IWL2020 Blood Glucose Monitoring System is intended to quantitatively measure glucose in fresh capillary whole blood from the fingertip as an aid in monitoring the effectiveness of glucose control.

The IWL2020 Blood Glucose Monitoring System is intended for in vitro diagnostic single patient use by people with diabetes at home.

The IWL2020 Blood Glucose Monitoring System is intended to be used by a single person and should not be shared. This system is not for use in diagnosis or screening of diabetes mellitus, nor for neonatal use.

The IWL2020 Blood Glucose Monitoring System consists of the following components:

• IWL2020 Meter
• IWL2020 Blood Glucose Test Strips
• IWL2020 Control Solutions

The IWL2020 Blood Glucose Monitoring System is a handheld device that incorporates features to aid in self-monitoring of blood glucose. The blood glucose results are displayed on the screen and stored in the meter's memory, and may also be transmitted via Bluetooth Low Energy (BLE) wireless communication. Our blood glucose monitoring system creates a glucose result from an amperometric reaction. Capillary whole blood from the user's fingertip reacts with the chemicals in the test strip to create a harmless electrical current in the test strip. The blood glucose meter reads the current and gives a blood glucose result.

The provided text describes the acceptance criteria and a study to prove that the device (IWL2020 Blood Glucose Monitoring System) meets these criteria. The study focuses on demonstrating the substantial equivalence of the IWL2020 system to a legally marketed predicate device (Accu-Chek Guide Blood Glucose Monitoring System).

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text outlines performance metrics rather than explicitly listing acceptance criteria as a separate table. However, the "Clinical Performance" section presents accuracy targets which can be interpreted as acceptance criteria based on standard glucose monitoring device expectations.

Additional Non-Clinical Performance Criteria and Results (from "Non-Clinical & Clinical Testing Summary and Conclusions"):

• Measurement Range: 20-600 mg/dL (Supported by Linearity Evaluation)
• Precision (Within-Run Evaluation):
  • Glucose Level 1 (20 mg/dL): 1.1 mg/dL SD
  • Glucose Level 2 (40 mg/dL): 1.2 mg/dL SD
  • Glucose Level 3 (80 mg/dL): 1.8 mg/dL SD
  • Glucose Level 4 (130 mg/dL): 2.1 %CV
  • Glucose Level 5 (200 mg/dL): 2.3 %CV
  • Glucose Level 6 (325 mg/dL): 2.7 %CV
  • Glucose Level 7 (450 mg/dL): 2.6 %CV
  • Glucose Level 8 (550 mg/dL): 2.6 %CV
• Precision (Intermediate Precision Evaluation):
  • Glucose Level 1 (27.8 mg/dL): 1.3 mg/dL SD
  • Glucose Level 2 (44.4 mg/dL): 1.3 mg/dL SD
  • Glucose Level 3 (113.6 mg/dL): 2.6 %CV
  • Glucose Level 4 (291.9 mg/dL): 2.3 %CV
  • Glucose Level 5 (495.1 mg/dL): 2.0 %CV
  • Glucose Level 6 (541.4 mg/dL): 2.2 %CV
• Linearity (R-squared for All Lots): 0.9985 (indicating strong linearity across the measurement range)
• Interference: All compounds met acceptance criteria except abnormally high concentrations of ascorbic acid (> 5 mg/dL) and xylose.
• Flex Studies: System operated within specified ranges and displayed errors correctly when outside operating ranges.
• Electromagnetic Interference and Electrical Safety: Passed according to national and international standards.

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

• Sample Size for Clinical Performance Test Set: 350 results (implicitly, as 346/350, 336/350, etc., are reported).
• Data Provenance:
  • Country of Origin: United States.
  • Retrospective or Prospective: Prospective. The text states: "A clinical (user evaluation) study was conducted with IWL2020 Blood Glucose Monitoring System in the intended user population, i.e. lay persons who perform self-testing using capillary whole blood, in the United States." This implies a specifically designed study for evaluation.

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

• The text states the clinical performance was "compared to the Roche/Hitachi cobas c 501 PCA-HK reference method." It also mentions "blood glucose readings obtained by trained technicians."
• This suggests the ground truth was established by a laboratory reference method, operated by "trained technicians." The number and specific qualifications (e.g., years of experience) of these technicians, or the experts overseeing the laboratory method, are not explicitly stated.

4. Adjudication Method for the Test Set

• The text does not mention any adjudication method (e.g., 2+1, 3+1) for the clinical performance data. The comparison is directly between the device readings and the laboratory reference method readings.

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

• No, an MRMC comparative effectiveness study was not performed as described.
• The study was a user evaluation (clinical performance) to assess how well "non-professional, inexperienced lay persons" obtained accurate readings with the IWL2020 system compared to a laboratory reference. It does not compare human readers with AI assistance versus without AI assistance. The IWL2020 is a blood glucose monitoring system, not an AI-powered image analysis tool for diagnostic imaging, which is where MRMC studies are typically applied.

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

• The primary clinical performance study described is the user evaluation, which involves "lay persons who perform self-testing." This inherently includes human-in-the-loop performance.
• However, the "Non-Clinical & Clinical Testing Summary and Conclusions" section also includes various non-clinical evaluations (e.g., Within-Run Precision, Intermediate Precision, Linearity, Interference, Flex Studies, Electromagnetic Interference, Electrical Safety). These tests assess the device's technical performance in a controlled setting, which can be seen as standalone testing of the algorithm/device's core functionality, without user variability being the primary focus. Specifically, the "System Accuracy" refers to the accuracy of the device's readings when compared to a reference method, which is an assessment of the device's inherent capability.

7. Type of Ground Truth Used

• Laboratory Reference Method: The ground truth for the clinical performance study was established using the "Roche/Hitachi cobas c 501 PCA-HK reference method." This is a highly accurate laboratory instrument.

8. Sample Size for the Training Set

• The document describes testing and validation, but it does not explicitly state the sample size used for a training set. Blood glucose monitoring systems typically derive their algorithms from fundamental electrochemical principles and calibrations, rather than machine learning models that require distinct training sets in the same way as, for example, an AI imaging diagnostic tool. The "linearity evaluation" used blood samples from eleven glucose concentration levels run on 36 IWL2020 meters using strips from three lots, which would contribute to the device's calibration or "training" in a broad sense, but not in the context of a 'training set' for a deep learning model.

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

• As a training set is not explicitly mentioned in the context of a machine learning model, the method for establishing its ground truth is not detailed. For the device's fundamental calibration and performance, the "linearity evaluation" involved preparing blood samples at "eleven blood glucose concentration levels." These concentrations would typically be precisely measured by a highly accurate laboratory reference method (like the Roche/Hitachi cobas c 501 PCA-HK or similar primary standards) to serve as the ground truth for establishing the device's internal calibration curve.

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The Accu-Chek FastClix Blood Lancing System is intended for the hygienic collection of capillary blood for testing purposes from the side of a fingertip and from alternative sites, such as the palm. upper arm, and the forearm. Six sterile, sincets are in a drum. The lancet drum is to be used with the reusable lancing device that is to be cleaned and disinfected between each use, and then the lancets are to be disposed of. This system is for use only on a single patient in a home setting. This system is not suitable for use by healthcare professionals with multiple patients in a healthcare setting.

The Accu-Chek FastClix Lancing Device uses compatible Accu-Chek FastClix Lancets to obtain a drop of blood from a fingertip or alternative sites. The Accu-Chek FastClix Blood Lancing System consists of three components: 1. Accu-Chek FastClix Lancing Device 2. Accu-Chek FastClix Lancets 3. Accu-Chek FastClix Alternative Site Testing (AST) Cap

This document is a 510(k) premarket notification decision letter from the FDA for the Accu-Chek FastClix Blood Lancing System. It declares the device substantially equivalent to a predicate device.

The provided document does not contain any information about acceptance criteria or a study proving the device meets those criteria, particularly not in the context of an AI/ML powered device. The device described is a mechanical blood lancing system for capillary blood collection, not an AI/ML system.

Therefore, I cannot fulfill your request for:

• A table of acceptance criteria and reported device performance (for an AI/ML system).
• Sample size and data provenance for a test set (for an AI/ML system).
• Number and qualifications of experts for ground truth (for an AI/ML system).
• Adjudication method (for an AI/ML system).
• MRMC comparative effectiveness study details (for an AI/ML system).
• Standalone performance details (for an AI/ML system).
• Type of ground truth used (for an AI/ML system).
• Sample size for the training set (for an AI/ML system).
• How ground truth for the training set was established (for an AI/ML system).

The document explicitly states: "Clinical Testing is not applicable; risk analysis confirmed that all identified risks were addressed and mitigated appropriately." This further confirms that no clinical study, of the type relevant to AI/ML device performance validation, was conducted or required for this mechanical device.

The "Nonclinical bench testing" mentioned refers to mechanical and validation testing related to sharps injury prevention and mechanical functions, not AI/ML performance.

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The Accu-Chek Safe-T-Pro Uno Lancing Device is a sterile, single-use, disposable lancing device intended to be used by healthcare professionals. It is designed for capillary blood sampling from the fingertip of adults and children 1 year and older or, if the patient is a child under 1 year, from the heel.

The Accu-Chek Safe-T-Pro Uno lancing device is a needle used for capillary blood sampling for use in diagnostic testing. The Accu-Chek Safe-T-Pro Uno lancing device contains a sharps injury prevention feature where the lancet is retracted and concealed before and after use. Once the lancet is used, it is rendered inoperative. The device is designed for single use only.

The provided text describes a 510(k) summary for the Accu-Chek Safe-T-Pro Uno Lancing Device. However, it does not contain specific acceptance criteria, reported device performance metrics, or details about a study that proves the device meets those criteria, such as sample size, data provenance, expert qualifications, or adjudication methods.

The document outlines the device's indications for use, its description, and compares it to a predicate device (SurgiLance Safety Lancets) to establish substantial equivalence. It states that "Nonclinical bench testing was performed per the applicable FDA Guidance documents (Sharps Injury Prevention Features) and special controls (878.4850). This includes (mechanical) design verification & validation testing in order to ensure the risks were appropriately managed, in addition to verifying that the device's mechanical functions are suitable for use over the lifetime of the device." A "Verification Summary" is mentioned as an attachment, which presumably contains the detailed test results, but this summary is not provided in the given text.

Therefore, based only on the provided text, I cannot complete the requested information. The document focuses on regulatory approval based on substantial equivalence to a predicate device rather than detailing a specific performance study with acceptance criteria and results.

Disclaimer: Without the "Verification Summary" mentioned in the document, it is impossible to provide the requested details about acceptance criteria and reported performance. The information below is based solely on what is explicitly stated or can be inferred from the provided text, which is limited regarding specific performance metrics.

Based on the provided text, the following information can be extracted or inferred:

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

The provided text does not explicitly state specific acceptance criteria or reported device performance metrics in a quantifiable manner. It mentions "nonclinical bench testing" and "design verification & validation testing" were performed to ensure risks were appropriately managed and verify mechanical functions are suitable for use over the lifetime of the device. The conclusion states the device "performs as well or better than the legally marketed predicate device," which is a general statement rather than specific performance data with corresponding acceptance criteria.

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

The text does not provide any information about the sample size used for the test set, data provenance, or whether the study was retrospective or prospective. It only refers to "nonclinical bench testing" and "design verification & validation testing."

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

This information is not applicable as the provided text describes non-clinical bench testing for a lancing device, not a study evaluating human interpretation or a diagnostic algorithm requiring expert ground truth establishment.

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

This information is not applicable as the provided text describes non-clinical bench testing for a lancing device and does not involve adjudication of results from multiple reviewers.

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

This information is not applicable as the device is a lancing device and does not involve AI assistance or human readers.

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

This information is not applicable as the device is a lancing device and does not involve an algorithm.

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

This information is not applicable in the traditional sense of medical image analysis or diagnostic algorithms. For a lancing device, "ground truth" would refer to established engineering and mechanical standards, as implied by "design verification & validation testing" and compliance with "FDA Guidance documents (Sharps Injury Prevention Features) and special controls (878.4850)."

8. The sample size for the training set

This information is not applicable as the device is a lancing device and does not involve machine learning or a training set.

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

This information is not applicable as the device is a lancing device and does not involve machine learning or a training set.

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The Accu-Chek Softclix Blood Lancing System is intended for the hygienic collection of capillary blood for testing purposes from the side of a fingertip and from alternative sites, such as the palm, the upper arm, and the forearm.

The sterile, single-use lancets are to be used with the reusable lancing device that is to be cleaned and disinfected between each use, and then the lancets are to be disposed of.

This system is for use only on a single patient in a home setting.

This system is not suitable for use by healthcare professionals with multiple patients in a healthcare setting.

The Accu-Chek Softclix Lancing Device uses compatible Accu-Chek Softclix Lancets to obtain a drop of blood from a fingertip or alternative sites. The Accu-Chek Softclix Blood Lancing System consists of three components:

1. Accu-Chek Softclix Lancing Device
2. Accu-Chek Softclix Lancets
3. Accu-Chek Softclix Alternative Site Testing (AST) Cap

The provided text describes the regulatory clearance of a blood lancing system and focuses on the substantial equivalence to a predicate device, rather than detailed acceptance criteria and a study proving those criteria.

Therefore, many of the requested elements for a study proving acceptance criteria cannot be extracted as they are not present in this regulatory document. This document emphasizes non-clinical (bench) testing to ensure functional equivalence and risk mitigation for a medical device.

However, based on the information provided, here's what can be extracted and what cannot:

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

The document does not explicitly present a table of acceptance criteria with corresponding performance results. It states that "Nonclinical bench testing was performed per the applicable FDA Guidance documents (Sharps Injury Prevention Features) and special controls (878.4850). This includes (mechanical) design verification testing in order to ensure the risks were appropriately managed, in addition to verifying that the device's mechanical functions are suitable for use over the lifetime of the device." This implies that the device met certain mechanical and safety standards but doesn't list specific quantitative criteria or their outcomes.

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 text only mentions "nonclinical bench testing" and "design verification testing" without specifying sample sizes for these tests or the origin of the data.

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

This information is not applicable and not provided. As this is a mechanical blood lancing device, "ground truth" in the context of expert review for diagnostic accuracy is not relevant. The testing focuses on mechanical function, safety, and performance as a lancing device.

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

This information is not applicable and not provided. Adjudication methods are typically associated with qualitative or diagnostic assessments, which are not the focus of this device's testing described.

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

This information is not applicable and not provided. An MRMC study is relevant for diagnostic imaging or AI-assisted diagnostic devices. This device is a blood lancing system, which does not involve human readers interpreting AI results.

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

This information is not applicable and not provided. The device described, a blood lancing system, does not involve an algorithm for standalone performance assessment in the way AI systems do. Its performance is mechanical and safety-related.

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

The document explicitly states: "Clinical Testing is not applicable: risk analysis confirmed that all identified risks were addressed and mitigated appropriately." Therefore, ground truth, as often defined for diagnostic accuracy, was not established through clinical data, pathology, or expert consensus on patient outcomes. Instead, the "ground truth" for this device's performance would be adherence to engineering specifications, safety standards (e.g., sharps injury prevention), and mechanical functionality confirmed through bench testing.

8. The sample size for the training set

This information is not applicable and not provided. There is no mention of a "training set" as this is not an AI/ML device that requires training data.

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

This information is not applicable and not provided, as there is no training set mentioned for this device.

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The Accu-Chek Guide Blood Glucose Monitoring System is comprised of the Accu-Chek Guide meter and the Accu-Chek Guide test strips.

The Accu-Chek Guide Blood Glucose Monitoring System is intended to quantitatively measure glucose in fresh capillary whole blood from the fingertip, palm, and upper arm as an aid in monitoring the effectiveness of glucose control.

The Accu-Chek Guide Blood Glucose Monitoring System is intended for in vitro diagnostic single-patient use by people with diabetes.

The Accu-Chek Guide Blood Glucose Monitoring System is intended to be used by a single person and should not be shared.

This system is not for use in diagnosis or screening of diabetes mellitus, nor for neonatal use.

Alternative site testing should be done only during steady-state times (when glucose is not changing rapidly).

Accu-Chek Guide Control Solutions are for use with the Accu-Chek Guide Blood Glucose Monitoring System to check that the meters and test strips are working together properly and that the test is performing correctly.

The Accu-Chek Guide System consists of the following:

• Accu-Chek Guide meter
• Accu-Chek Guide test strips
• Accu-Chek Guide control solutions (previously cleared in K043474)

The Accu-Chek Guide Blood Glucose Monitoring System makes use of the Accu-Chek Guide test strips, the Accu-Chek Guide meter, and the Accu-Chek Guide control solutions. This system is a single-patient use blood glucose monitoring system intended to be used to quantitatively measure glucose in fresh capillary whole blood from the fingertip, palm, and upper arm as an aid in monitoring the effectiveness of glucose control. It should be noted that the Accu-Chek Guide control solutions are identical to the Accu-Chek Aviva control solutions that were cleared previously in K043474. The name has simply been updated to reflect their use with the Accu-Chek Guide system.

The provided document describes the ACCU-CHEK Guide Blood Glucose Monitoring System and its performance. However, it does not involve an AI or algorithmic device in the context of medical imaging or diagnosis. Instead, it concerns a blood glucose meter, a device that performs a direct chemical measurement. Therefore, many of the requested categories, such as "number of experts used to establish ground truth," "adjudication method," "MRMC comparative effectiveness study," and "sample size for the training set," are not applicable as they relate specifically to AI/ML or image interpretation studies.

Here's a breakdown of the relevant information from the document regarding acceptance criteria and performance:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for blood glucose monitoring systems are generally based on accuracy standards, often requiring a certain percentage of results to fall within specified variations from a reference method. The document provides performance data based on differences from a reference measurement. The provided data is not explicitly labeled as "acceptance criteria," but rather as "user performance data." However, these percentages directly describe how well the device performs against established accuracy thresholds. I will present the provided performance data as demonstrating how the device met implied or industry-standard acceptance levels for accuracy.

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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.

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