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The WaveSense Jazz Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood from the finger stick, palm and/or forearm. Testing is done outside of the body (in vitro diagnostic use). It is indicated for use at home (over the counter (OTC)) by persons with diabetes, as an aid to monitor the effectiveness of diabetes control.

The WaveSense Jazz Blood Glucose Monitoring System includes a meter with batteries, compact carrying case, lancing device lancets, control solution and instructions for use. Test Strips are necessary for testing but are sold separately.

The WaveSense Jazz Blood Glucose Monitoring System is intended for the quantitative measurement of blood glucose levels in fresh capillary whole blood samples drawn from the fingertips, palm or forearm. The WaveSense Jazz Test Strips are for in vitro diagnostic (outside of the body) use only. The WaveSense Jazz System is not intended for use with neonates.

The provided text describes a 510(k) premarket notification for the WaveSense Jazz Blood Glucose Monitoring System. The document focuses on establishing substantial equivalence to a predicate device (K072413), rather than presenting a de novo clinical study with detailed acceptance criteria and performance data as typically seen for novel devices, especially those incorporating AI.

Therefore, the information required to fully answer the request regarding acceptance criteria and a study proving device performance (especially for an AI/ML context) is largely absent from this specific 510(k) summary. The document primarily discusses the intended use, technological comparison to a predicate, and the modifications made (new colors, new data management feature), implying that much of the performance data would have been established for the original predicate device.

However, I can extract the relevant information that is present and indicate where information is not available from the provided text.

Here's an attempt to answer based on the provided document, acknowledging its limitations for an AI/ML-centric request:

Acceptance Criteria and Device Performance (based on the provided 510(k) Summary for a Glucose Monitoring System)

It's crucial to understand that this 510(k) pertains to a Blood Glucose Monitoring System, which is a hardware-based diagnostic device, not an AI/ML-powered software or imaging device. Therefore, many of the typical questions regarding AI/ML clinical studies (MRMC, expert consensus for ground truth, training set details, etc.) are not applicable to this type of submission.

The "study" referenced in the provided text is primarily focused on verification and validation (V&V) of the modifications made to an existing predicate device, rather than a large-scale clinical trial to establish novel performance.

1. Table of Acceptance Criteria and Reported Device Performance

For a Blood Glucose Monitoring System, acceptance criteria usually relate to accuracy standards (e.g., ISO 15197 for point-of-care testing), precision, and other analytical performance characteristics. The provided 510(k) summary does not explicitly list these numerical acceptance criteria or the specific performance results in a table. It instead states that "verification and validation results" were sufficient to establish substantial equivalence.

However, based on typical FDA requirements for Blood Glucose Monitoring Systems, the implicit acceptance criteria would relate to:

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

The document mentions "verification and validation results" and "firmware functional testing and usability engineering evaluations" but does not specify the sample size for any test sets.

• Data Provenance: Not explicitly stated, but typically for such devices, the data would be collected from human subjects (e.g., finger-stick blood samples). The document does not specify country of origin or whether it was retrospective or prospective. Given the nature of a 510(k) for a modified device, it's likely a combination of bench testing (prospective), and potentially limited human use testing (prospective) to validate the specific changes.

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

This is not applicable in the context of a Blood Glucose Monitoring System where the "ground truth" for glucose concentration is established by a laboratory reference method (e.g., hexokinase method on a central laboratory analyzer), not by human expert interpretation (like a radiologist for imaging).

4. Adjudication Method for the Test Set

This is not applicable as there is no human interpretation or subjective assessment that would require adjudication for a glucose reading.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size of Human Improvement with AI vs. Human without AI Assistance

This is not applicable. This is a hardware-based diagnostic device for measuring glucose, not an AI-powered system designed to assist human readers in, for instance, interpreting images or making clinical diagnoses.

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

This is not applicable. The device is the "algorithm" and measurement system. Its performance is measured directly against a reference method. It's not an AI algorithm that produces an output that then needs to be compared to human performance.

7. The Type of Ground Truth Used

For a Blood Glucose Monitoring System, the primary ground truth for glucose concentration is laboratory reference methods (e.g., YSI 2300 STAT Plus Glucose & Lactate Analyzer, or similar enzymatic methods traceable to national/international standards), typically using venous blood plasma samples.

8. The Sample Size for the Training Set

This concept of a "training set" is primarily relevant for machine learning/AI models. For a traditional electrochemical glucose sensor, there isn't a "training set" in the same sense. The device is calibrated during manufacturing based on known glucose concentrations, and its accuracy is verified and validated. No information on a "training set" is provided or applicable here.

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

As above, the concept of a "training set" and its associated ground truth is not applicable for this type of device.

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The WAVES is intended to be used for the pulsatile hypothermic machine perfusion of kidneys for preservation, transportation, and eventual transplantation into a recipient.

The WAVES is a transportable, self-contained renal preservation system, designed to support static monitoring and transportation of kidneys. The WAVES system provides controlled pulsatile kidney perfusion using oxygenated hypothermic physiologic solutions, and monitors, displays, trends, and saves important perfusion parameters, including: perfusate flow, temperature, pressure, and renal resistance. The WA VES system can be configured to signal an audio and visual alarm for user-selected limits.

The WAVES is a two-part system comprising a 'control unit' for perfusion and monitoring of a single kidney, and a sterile, single-use, disposable 'cassette module' used to contain, refrigerate, and circulate perfusate to and through the kidney.

The provided document describes a medical device called WAVES, a renal preservation system, and discusses its substantial equivalence to predicate devices for 510(k) premarket notification. However, it does not contain information about acceptance criteria, device performance metrics, or study details in the format requested, such as test set size, data provenance, expert-established ground truth, adjudication methods, MRMC studies, standalone algorithm performance, or training set details.

The document focuses on demonstrating substantial equivalence through a comparison of intended use, principle of operation, and specific control aspects (perfusion and hypothermic) with predicate devices. It mentions that certain aspects like pump performance, cooling system performance, and software validation were evaluated. Sterilization and biomaterial testing were also conducted.

Therefore, I cannot populate the requested table and answer the study-related questions based on the provided text.

The document primarily asserts substantial equivalence based on:

• Intended Use: All systems perform pulsatile hypothermic machine perfusion of kidneys for preservation, transportation, and eventual transplantation.
• Principle of Operation: Similar design and function for maintaining kidneys.
• Perfusion Control: WAVES perfusion control (perfusate parameters, pressure, automated features, cassette priming) is "substantially equivalent" and "evaluated through a pump performance testing and a software validation testing."
• Hypothermic Control: WAVES hypothermic control (cassette mounting, cooling method/duration/temperature, heat exchanger) is "substantially equivalent" and "evaluated through a cooling system performance testing and a software validation testing."
• Disposable Cassette Module: Sterilization validated per ISO 11135-1, and shelf life supported by accelerated aging testing.
• Biomaterials: ISO 10993 compliant testing for new biomaterials.
• User Interface: Software validation testing showed user interface operates as expected.

The FDA's letter explicitly states that they "reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent... to legally marketed predicate devices." This substantial equivalence determination means a comparative effectiveness study in the sense of AI performance metrics (like sensitivity, specificity, AUC) against human readers or a pathology-confirmed ground truth is not typically required or performed for such a device, as it's a renal preservation system, not a diagnostic imaging AI.

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The WaveSense Diabetes Manager (WDM) application (app) is intended for use in the home and professional settings to aid individuals with diabetes and their healthcare professionals; in the review, analysis and evaluation of blood glucose readings to support an effective diabetes management program. The WaveSense Diabetes Manager application is a digital logbook and diabetes management tool designed to operate using the iPhone Operating System platform. The application can be used alone or with the WaveSense Direct Connect Cable and a WaveSense-enabled blood glucose meter (BGM) with a mini-USB port.

The WaveSense Diabetes Manager (WDM) application (app) is a digital logbook and diabetes management tool for the iPhone operating system platform. The application can be used alone or with the WaveSense Direct Connect Cable and a WaveSense-enabled Blood Glucose Meter (BGM) with a mini-USB port.

Here's an analysis of the provided text regarding the acceptance criteria and study for the AgaMatrix WaveSense Diabetes Manager application:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text focuses on demonstrating substantial equivalence to a predicate device rather than explicitly defining and meeting specific analytical or clinical performance acceptance criteria for the WaveSense Diabetes Manager application itself. The study's focus was on the ease of use and functional equivalence as a data management tool.

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

• Sample Size: Not explicitly stated. The text mentions "Clinical setting by persons with diabetes," but does not provide a number for the participants in this evaluation.
• Data Provenance: The study was conducted "in house and in a Clinical setting." The country of origin is not specified but is presumed to be the USA, given the submission to the FDA. The study appears to be prospective in nature, as it involved actively evaluating the device.

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. The study primarily focused on user experience and functional equivalence rather than a diagnostic performance evaluation requiring expert ground truth establishment.

4. Adjudication Method for the Test Set

This information is not provided. Given the nature of the study (ease of use and functional equivalence), a formal adjudication method for diagnostic accuracy would likely not be relevant or necessary.

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. The WaveSense Diabetes Manager application is a data management tool, not an AI-powered diagnostic device, and therefore this type of study is not applicable.

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

The device is a standalone application in the sense that it collects and displays data. However, it's not an "algorithm-only" device for diagnostic or predictive purposes without human interaction. Its function is to facilitate human review and analysis of blood glucose data. The performance assessment was based on its operational ease and functional equivalence.

7. The Type of Ground Truth Used

The concept of "ground truth" as it applies to diagnostic accuracy (e.g., pathology, expert consensus) is not applicable to this device. The "ground truth" for this application would be the accurate transfer and display of blood glucose readings, which are generated by an external BGM, and the user's ability to easily navigate and utilize the app's features. The study implicitly evaluated the functional correctness and user experience as its "ground truth."

8. The Sample Size for the Training Set

This information is not applicable/not provided. The WaveSense Diabetes Manager is an application for data management, not a machine learning or AI model that requires a "training set" in the conventional sense.

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

This information is not applicable. As stated above, this device does not utilize a "training set" in the context of an AI/ML model.

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The Wavestate Neuromonitor System is intended to collect, record, and store up to 24 channels of adult EEG data for up to 24 hours. The System also can perform a post review of adult EEG data and identify burst suppression pattern in the stored EEG. The device displays the mean interburst interval reviewed up to that time point and the probability that the displayed value is within +/- 2 seconds of the mean of the interburst intervals for the entire dataset for that patient. The Wavestate Neuromonitor System does not provide any diagnostic conclusion about the patient's condition to the user. The Wavestate Neuromonitor is to be used under the guidance and interpretation of a licensed medical practitioner.

Wavestate, Inc. has created a new application for the TrackIt-2, an FDA-approved ambulatory EEG hardware unit manufactured by Lifelines, Ltd (UK). Our proprietary software analyzes EEG data files recorded with the TrackIt-2. Data are displayed on an Xplore touch-screen tablet computer using Microsoft Windows XP.

Our application is used to quantify the inter-burst interval with 95% statistical confidence the duration of the interval within +/- 2 seconds.

The Trackit-2 system is FDA approved.

FDA-approved EEG electrodes will be bought separately by the end user.

The Wavestate Neuromonitor is an application for an FDA-approved ambulatory EEG hardware unit. Its proprietary software analyzes EEG data, primarily identifying burst suppression patterns and quantifying the inter-burst interval with 95% statistical confidence.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the software verification and validation summary, where the accuracy of specific features is tested against defined thresholds.

2. Sample Size for the Test Set and Data Provenance

The provided document does not specify a sample size for the test set in terms of actual patient data or real EEG recordings. Instead, the testing appears to be based on simulated or synthesized data.

• For burst detection, "40-ms-duration spikes of varying amplitude are inserted into digitized EEG files consisting of background activity."
• For multiple channel detection, "10.5 microvolt spikes identified independently and simultaneously in each EEG of the 19 channels tested."
• For suppression detection, "Spikes of 10.5 microvolt amplitude are inserted, at increasing interval length, into an EEG file consisting of baseline background activity."
• For mean interburst interval calculation, "Spikes of 10.5 microvolt amplitude are inserted at increasing intervals into an EEG file."
• For statistical confidence, "A series of interburst intervals are constructed with 10.5 microvolt spikes."

This suggests the data provenance is synthetic/simulated, not derived from a specific country or retrospective/prospective patient studies.

3. Number of Experts and their Qualifications for Ground Truth

The document does not mention the use of human experts to establish ground truth for the test set. The ground truth for the verification and validation appears to be based on the known parameters of the artificially inserted spikes and constructed intervals.

4. Adjudication Method for the Test Set

Since human experts were not used to establish ground truth, there was no adjudication method described. The validation relied on the algorithm's ability to accurately detect or calculate pre-defined synthetic events.

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

No MRMC comparative effectiveness study was mentioned in the provided summary. The device's validation focuses on its standalone algorithmic performance rather than its impact on human reader performance.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone study was performed. The entire "Software Verification and Validation Summary" describes the testing of the algorithm itself, without human-in-the-loop. The tests focused on the accuracy of burst detection, channel logic, suppression duration, interburst interval calculation, and statistical confidence, all as performed by the algorithm with synthetic data.

7. Type of Ground Truth Used

The ground truth used was synthetic/known parameters based on artificially injected spikes and constructed EEG patterns. For example, spikes of a known amplitude (e.g., 10 microvolts) were inserted, and the algorithm's ability to detect these known events was evaluated. Similarly, when testing the interburst interval and statistical confidence, known sequences of intervals were constructed.

8. Sample Size for the Training Set

The document does not provide information on the sample size used for a training set. Given the nature of the validation (inserting spikes into EEG files), it's possible the algorithm was developed based on theoretical EEG signal characteristics or a separate, unmentioned dataset. However, no specific training set size or methodology is presented in this 510(k) summary.

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

The document does not provide information on how the ground truth for a training set was established, as it doesn't mention a distinct training set. If such a set was used, its ground truth establishment method is not described here.

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AgaMatrix WaveSense™ KeyNote Codeless Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood from the finger stick, palm, and/or forearm. 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 a clinical setting by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.

The AgaMatrix WaveSense™ KeyNote Codeless Blood Glucose Monitoring System includes a meter with batteries, compact carrying case, lancing device, lancets, control solution and owner's booklet. Test Strips are sold separately. The meter is a portable, battery-operated instrument.

The provided text describes the AgaMatrix WaveSense™ KeyNote Codeless Blood Glucose Monitoring System, which states that it complies with ISO 15197:2003. This standard specifies the requirements for blood glucose monitoring systems for self-testing in managing diabetes mellitus. While the summary states compliance, it does not explicitly provide a table of acceptance criteria or reported device performance against specific targets from ISO 15197. Therefore, the following information is based on the general understanding of ISO 15197 for blood glucose meters, as the specific performance data is not detailed in the provided document.

1. Table of Acceptance Criteria and Reported Device Performance

As specific performance data against ISO 15197:2003 criteria is not provided in the document, a general representation based on the standard's requirements for accuracy is used.

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

The document states compliance with ISO 15197:2003, which typically requires a minimum number of subjects and blood samples for accuracy evaluation. However, the exact sample size used for the test set in the study confirming compliance and the data provenance (e.g., country of origin, retrospective or prospective) are not specified in the provided text.

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

The document does not provide information on the number of experts used or their qualifications to establish ground truth for the test set. For blood glucose monitoring systems, ground truth is typically established by a laboratory reference method (e.g., YSI analyzer) operated by trained laboratory personnel, rather than expert clinicians.

4. Adjudication Method for the Test Set

Not applicable. For blood glucose meter accuracy studies, ground truth is typically a reference laboratory measurement, not subject to clinical adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, If So, What Was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

Not applicable. This is a blood glucose monitoring system, which does not involve human readers interpreting medical images or data that would typically be evaluated in an MRMC study with AI assistance.

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

The device is a standalone blood glucose monitoring system. Its performance is evaluated intrinsically, meaning the system's output (blood glucose reading) is directly compared to a reference method, without human intervention as an interpretive step.

7. The Type of Ground Truth Used

Based on the nature of blood glucose monitoring systems and the reference to ISO 15197, the ground truth used for evaluating device performance would be laboratory reference measurements, typically from a highly accurate and precise instrument like a YSI glucose analyzer, applied to the same blood samples as tested by the device.

8. The Sample Size for the Training Set

The document does not provide information about a "training set" or its sample size. Blood glucose meters are typically calibrated during manufacturing rather than "trained" with data in the way an AI algorithm would be. The design and manufacturing process would be validated against established standards.

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

Not applicable, as a "training set" in the context of machine learning for an algorithm is not relevant to this type of device. Calibration and validation for blood glucose meters rely on precisely prepared glucose solutions and comparison to reference methods.

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The VISX WaveScan™ Wavefront System is a diagnostic instrument indicated for the automated measurement and analysis of refractive errors of the eye including hyperopia and myopia from +6.00 to -8.00 diopters spherical, and astigmatism from 0.00 to -6.00 diopters.

The WaveScan™ Wavefront System Model HS 1 autorefractor device is a diagnostic instrument designed to measure refractive error of the eye automatically by use of wavefront technology. Light travels in a procession of flat sheets known as wavefronts. As these wavefronts pass through an imperfect refractive medium including the cornea and the lens, the aberrations which are created by the irregular surfaces "wrinkle" the light rays and create wavefront errors or distortions. The instrument contains tiny sensors which measure the gradient, or slope, of the wavefront which emanate from the eye. After light travels through the eye's optical system and out again, the sensors accurately detect slight variations of wavefront irregularities as they exit the eye. The sensors then provide additional information within the confines of the instrument through a series of lenses and apertures which are subject to mathematical algorithms and software. Once analyzed by the computer, a refractive error read-out is provided to the user. This analysis is made from multiple points of light which precisely pinpoint variations in refractive status across the entrance pupil of the eye. This allows for the high level of accuracy of the instrument thus providing the user with very precise readings of refractive error.

Here's an analysis of the provided text, broken down by your requested categories:

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

The document doesn't explicitly state "acceptance criteria" with numerical targets for accuracy, reproducibility, or other performance metrics. Instead, it describes a
comparison study against a predicate device (Canon R-50m) to demonstrate equivalence. The reported performance is relative to this predicate.

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

• Test Set Sample Size: The tests were conducted using a "model test eye developed by VISX, Inc. and modeled after the Gullstrand Standard Test Eye Model." Each test condition (combinations of myopic, hyperopic, and astigmatic errors) was repeated five times. The exact number of "test conditions" or specific refractive error combinations is not quantified, so a precise sample size for the test set in terms of individual measurements cannot be determined from the provided text.
• Data Provenance:
  • Origin: The model test eye was "developed by VISX, Inc."
  • Retrospective/Prospective: Neither. The testing was a bench study using a physical eye model, not human 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)

Not applicable. The ground truth for the test set was established by the design of the "model test eye" which was constructed to represent specific refractive errors. There were no human experts involved in establishing the ground truth for this bench testing.

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

Not applicable. There was no human adjudication as the testing was done against a physical model with known characteristics.

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 document describes the performance of an automated diagnostic instrument (autorefractor) directly measuring refractive error. It is a standalone device, and no human-in-the-loop or MRMC study comparing human readers with and without AI assistance was performed or described.

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

Yes, a standalone performance evaluation was conducted. The WaveScan™ Wavefront System Model HS 1 is an "autorefractor device" designed to "automatically measure refractive error of the eye." The testing described is directly evaluating the device's ability to measure refractive errors on its own using a test eye model.

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

The ground truth used was known, precisely engineered refractive errors as embodied in the "Gullstrand Standard Test Eye Model" on which the VISX model eye was based.

8. The sample size for the training set

Not applicable. This device is an autorefractor, which uses optical principles and mathematical algorithms to determine refractive error. It is not described as a machine learning or AI device that requires a "training set" in the conventional sense. Its algorithms are based on established physics and optics.

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

Not applicable, as there is no mention of a training set for a machine learning algorithm.

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