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The SignalNED Device is intended to record and store EEG signals for the statistical evaluation of the human electroencephalogram (EEG) and display Quantitative EEG (qEEG) measures intended to help the user analyze the EEG. These measures include relative band power (e.g., alpha, beta, delta, theta) and band power asymmetry (displayed as a z-score compared to a normative database). The SignalNED does not provide any diagnostic conclusion about the patient's condition. The device is intended to be used on adults by qualified medical and clinical professionals.

The SignalNED Model RE machine uses 10 patient electrodes (4 left, 4 right, 2 midline), which are used to form the 8 channels. The SignalNED machine requires the use of the SignalNED Sensor Cap, and the system includes the following components:

• Portable EEG machine (Device)
• Battery & External Battery Charger
• SignalNED Sensor Cap
• SignalNED Sensor Cap Cable

The primary function of the SignalNED Model RE is to rapidly record EEG and derive Quantitative EEG (qEEG) measurements. These measurements include Relative Band Power for multiple bands (e.g., alpha, beta, delta, theta) at each electrode and band power asymmetry (displayed as a z-score compared to a normative database). These measurements are intended to help the user analyze the underlying EEG. The SignalNED Model RE (client) achieves its intended without relying on wireless connectivity. The SignalNED RE does not provide any diagnostic conclusion about the patient's condition.

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The SignalNED Device is intended to record and display Quantitative EEG (qEEG) (relative band power, e.g., alpha, beta, delta, theta), which is intended to help the user analyze the EEG. The SignalNED does not provide any diagnostic conclusion about the patient's condition. The device is intended to be used on adults by qualified medical and clinical professionals.

The SignalNED is intended to be used in a professional healthcare environment.

The SignalNED Model RE machine uses 10 patient electrodes (4 left, 4 right, 2 midh are used to form the 8 channels. The SignalNED machine requires the use of the SignalNED Sensor Cap, and the system includes the following components:

• Portable EEG machine (Device)
• I Battery & External Battery Charger
• I SignalNED Sensor Cap
• I SignalNED Sensor Cap Cable

The primary function of the SignalNED Model RE is to rapidly record EEG and derive the Quantitative EEG (qEEG) measurement of Relative Band Power for multiple bands (e.g., alpha, beta, theta) at each electrode. These measurements are intended to help the user analyze the underlying EEG. The SignalNED Model RE (client) achieves its intended without relying on wireless comectivity. The SignalNED RE does not provide any diagnostic conclusion about the patient's condition.

This summary describes the acceptance criteria and the study that proves the SignalNED System (Model RE) meets those criteria, based on the provided FDA 510(k) summary.

1. Table of Acceptance Criteria and Reported Device Performance

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

The provided document does not specify the sample sizes (e.g., number of subjects, number of EEG recordings) used for the non-clinical performance testing. It only states that "All testing passed acceptance criteria and details are contained in the test report." The data provenance (e.g., country of origin, retrospective or prospective) is also not detailed in this summary.

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

The provided document describes non-clinical performance testing (lead-off detection, noise levels, ADC accuracy, QEEG calculation accuracy, electrical performance, biocompatibility). These tests do not typically involve human experts establishing ground truth in the way a clinical study for diagnostic accuracy would. The ground truth for these tests would be established through defined technical specifications, measurement standards, and validated testing protocols. Therefore, information about the number and qualifications of experts for establishing ground truth is not applicable in this context.

4. Adjudication Method for the Test Set

As the performance testing described is non-clinical and based on technical specifications and standards, an adjudication method (like 2+1 or 3+1) used in clinical studies for discrepancies in expert readings is not applicable here. The acceptance criteria for each test inherently define the "ground truth" to which the device's performance is 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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or described in the provided document. The SignalNED System is intended to record and display QEEG, which "is intended to help the user analyze the EEG." It explicitly states, "The SignalNED does not provide any diagnostic conclusion about the patient's condition." This indicates that the device is a tool for professional analysis rather than an AI-driven diagnostic aid for human readers. Therefore, an MRMC study comparing human readers with and without AI assistance is not described.

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

The document describes several standalone performance tests for the device's components and calculations (e.g., Lead Off Detection, Signal Acquisition Noise Levels, Software ADC Conversion Accuracy, Quantitative Electroencephalogram (QEEG) accuracy). These tests are conducted on the algorithm and hardware without human interpretation as part of the primary outcome assessment. For instance, the "Software ADC Conversion Accuracy" and "Quantitative Electroencephalogram (QEEG)" accuracy tests evaluate the algorithm's performance in generating calculated EEG measures.

7. The Type of Ground Truth Used

The ground truth used for the reported performance tests is based on:

• Defined Technical Specifications and Engineering Standards: For tests like Lead Off Detection, Signal Acquisition Noise Levels, Software ADC Conversion Accuracy, EC12:2020 Electrical Performance, and Essential Performance Tests (IEC 80601-2-26).
• Validated Calculation Methods: For the Quantitative Electroencephalogram (QEEG) Relative Band Power calculation, the ground truth would be based on established mathematical and signal processing principles for deriving these metrics from raw EEG data.
• International Biocompatibility Standards: For ISO 10993 series tests (Cytotoxicity, Sensitization, Irritation).

8. The Sample Size for the Training Set

The provided document describes performance testing for substantial equivalence, not the development or validation of a machine learning model with distinct training and test sets in the typical sense. While the device calculates QEEG, the details on how the underlying algorithms were developed or "trained" (if machine learning is involved beyond standard signal processing) are not provided. Therefore, a specific sample size for a "training set" is not mentioned.

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

As information about a distinct "training set" for machine learning algorithms is not provided, the method for establishing ground truth for such a set is also not described. The document focuses on performance testing against established engineering, electrical, and biocompatibility standards.

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The SignalHF System is intended for use by qualified healthcare professionals (HCP) managing patients over 18 years old who are receiving physiological monitoring for Heart Failure surveillance and implanted with a compatible Cardiac Implantable Electronic Devices (CIED) (i.e., compatible pacemakers, ICDs, and CRTs).

The SignalHF System provides additive information to use in conjunction with standard clinical evaluation.

The SignalHF HF Score is intended to calculate the risk of HF for a patient in the next 30 days.

This System is intended for adjunctive use with other physiological vital signs and patient symptoms and is not intended to independently direct therapy.

SignalHF is a software as medical device (SaMD) that uses a proprietary and validated algorithm, the SignalHF HF Score, to calculate the risk of a future worsening condition related to Heart Failure (HF). The algorithm computes this HF score using data obtained from (i) a diverse set of physiologic measures generated in the patient's remotely accessible pre-existing cardiac implant (activity, atrial burden, heart rate variability, heart rate, heart rate at rest, thoracic impedance (for fluid retention), and premature ventricular contractions per hour), and (ii) his/her available Personal Health Records (demographics). SignalHF provides information regarding the patient's health status (like a patient's stable HF condition) and also provides alerts based on the SignalHF HF evaluation. Based on an alert and a recovery threshold on the SignalHF score established during the learning phase of the algorithm and fixed for all patients, our monitoring system is expected to raise an alert 30 days (on median) before a predicted HF hospitalization event.

SignalHF does not provide a real-time alert. Rather, it is designed to detect chronic worsening of HF status. SignalHF is designed to provide a score linked to the probability of a future decompensated heart failure event specific to each patient. Using this adjunctive information, healthcare professionals can make adjustments for the patient based on their clinical judgement and expertise.

The score and score-based alerts provided through SignalHF can be displayed on any compatible HF monitoring platform, including the Implicity platform. The healthcare professional (HCP) can utilize the SignalHF HF score as adjunct information when monitoring CIED patients with remote monitoring capabilities.

The HCP's decision is not based solely on the device data which serves as adjunct information, but rather on the full clinical and medical picture and record of the patient.

Here's a summary of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for SignalHF:

Acceptance Criteria and Device Performance for SignalHF

The SignalHF device was evaluated through the FORESEE-HF Study, a non-interventional clinical retrospective study.

1. Table of Acceptance Criteria and Reported Device Performance

For ICD/CRT-D Devices:

For Pacemaker/CRT-P Devices:

2. Sample Size and Data Provenance for the Test Set

• Test Set (Clinical Cohort) Sample Size: 6,740 patients (comprising PM 7,360, ICD 5,642, CRT-D 4,116 and CRT-P 856 - Note: there appears to be a discrepancy in the total sum provided, however, "6,740" is explicitly stated as the 'Clinical cohort' which is the test set).
• Data Provenance: Retrospective study using data from the French national health database "SNDS" (SYSTÈME NATIONAL DES DONNÉES DE SANTÉ) and Implicity proprietary databases. The follow-up period was 2017-2021.

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state the number of experts used to establish ground truth or their specific qualifications (e.g., radiologist with 10 years of experience). However, the ground truth was "hospitalizations with HF as primary diagnosis" as recorded in the national health database, implying that these diagnoses were made by qualified healthcare professionals as part of routine clinical care documented within the SNDS.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method like 2+1 or 3+1 for establishing the ground truth diagnoses. The study relies on “hospitalizations with HF as primary diagnosis” from the national health database, suggesting that these are established clinical diagnoses within the healthcare system.

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

There is no indication that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to evaluate human reader improvement with AI assistance. The study focuses solely on the standalone performance of the SignalHF algorithm.

6. Standalone Performance

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done. The FORESEE-HF study evaluated the SignalHF algorithm's performance in predicting heart failure hospitalizations based on CIED data and personal health records.

7. Type of Ground Truth Used

The ground truth used was outcomes data, specifically "hospitalizations with HF as primary diagnosis" recorded in the French national health database (SNDS).

8. Sample Size for the Training Set

• Training Cohort Sample Size: 7,556 patients

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

The document states that the algorithm computes the HF score using physiological measures from compatible CIEDs and available Personal Health Records (demographics). It also mentions that the "recovery threshold on the SignalHF score established during the learning phase of the algorithm and fixed for all patients". This implies that the ground truth for the training set, similar to the test set, was derived from the same data sources: "hospitalizations with HF as primary diagnosis" documented within the SNDS database. The training process would have used these documented HF hospitalizations as the target outcome for the algorithm to learn from.

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The SignalMark Breast Marker is intended to provide accuracy in marking a surgical site and/or a biopsy location for visualization during surgical resection.

The SignalMark Breast Marker is a medical device used by a physician to percutaneously place a small implantable hydrogel marker in breast tissue to "mark" the location of the biopsy or surgical site. It is intended to be used on adults undergoing open surgical breast biopsy or percutaneous breast biopsy, in a surgical setting, such as a hospital or medical clinic with operating suites. The SignalMark Breast Marker consists of two components:

• . Applicator: Component made of plastic and stainless steel that pushes the marker into the tissue.
• Marker Pad: Component made of USP-grade porcine gelatin-based hydrogel with methylene blue-colored silicon dioxide microspheres. The marker aids in the visualization of tissue allowing surgeons to readily locate the biopsy site for subsequent tissue or tumor resection.

The provided document, a 510(k) summary for the SignalMark Breast Marker, describes non-clinical testing performed to demonstrate substantial equivalence to predicate devices, rather than a study designed to prove the device meets specific acceptance criteria in the context of clinical performance or diagnostic accuracy. Therefore, a table of acceptance criteria and reported device performance in those terms is not available from this document.

However, the document does detail other aspects of the testing performed, which can be extracted and summarized.

No clinical studies involving human patients, multi-reader multi-case (MRMC) comparative effectiveness studies, or standalone algorithm performance studies are described in this 510(k) summary, as it focuses on non-clinical (bench and animal) testing. The device is a physical marker and not an AI/algorithm-based diagnostic tool, so certain sections of your request (e.g., effect size of human readers improving with AI, standalone performance, training set details) are not applicable.

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

As mentioned, this document does not provide a table of acceptance criteria for diagnostic performance or clinical effectiveness, nor does it report device performance against such criteria. The testing focused on demonstrating equivalence to predicate devices through physical and biological testing.

The document lists the following non-clinical tests performed:

Note: The document states, "No FDA performance standards have been established for the SignalMark Breast Marker." This indicates that the equivalence was demonstrated against the predicate's known performance for these non-clinical aspects, rather than against specific regulatory performance metrics for this type of device.

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

• Sample Size: The document does not specify the exact sample sizes for each of the bench and animal tests. It only lists the types of tests conducted. For example, for "Biodistribution in rodents" and "Safety and efficacy in porcine," the specific number of animals used is not provided.
• Data Provenance: The document does not mention the country of origin of the data. The studies were non-clinical (bench and animal), so the terms "retrospective" or "prospective" as they apply to human clinical studies are not directly relevant. These were experimental 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 non-clinical (bench and animal) testing, not a diagnostic accuracy study requiring expert establishment of ground truth.

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

This section is not applicable as the document describes non-clinical (bench and animal) testing, not a diagnostic accuracy study requiring 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

This section is not applicable. The SignalMark Breast Marker is a physical implantable marker, not an AI or imaging diagnostic device. No MRMC study or AI assistance is mentioned or relevant to this device's 510(k) submission.

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

This section is not applicable. The SignalMark Breast Marker is a physical implantable marker, not an algorithm, and therefore does not have "standalone performance" in the context of AI or software.

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

For the non-clinical tests described:

• For Biocompatibility, the ground truth is adherence to the ISO 10993-1 standard.
• For Bench Performance tests (e.g., dimensional inspection, deployment, hydration, ultrasound visibility), the ground truth would be the pre-defined engineering specifications, material properties, and expected physical behaviors of the device, often compared to the predicate device's characteristics.
• For Animal Performance tests (biodistribution, safety, efficacy, biologic response), the ground truth is derived from the observed biological reactions, histology, and functional outcomes in the animal models, compared to established norms or the predicate device's effects.

8. The sample size for the training set

This section is not applicable. The SignalMark Breast Marker is a physical medical device, not a machine learning or AI model, and therefore does not have a "training set" in that context. The device's design and manufacturing processes are developed through traditional engineering and material science principles.

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 Signal Catheter is indicated for urological bladder drainage, with a maximum patient indwelling time of 29 days.

The Signal Catheter is a 16 French, 2-way silicone Foley catheter, designed to be inserted into the bladder through the urethra to drain urine. The unique signal balloon included in the catheter hub is designed to inflate during excessive pressure in the retention balloon. This typically occurs when the retention balloon is constricted and cannot be inflated at the nominal inflation pressure of the catheter. In this case, the signal balloon inflates to alleviate the fluid and resulting pressure in the retention balloon.

The Signal Catheter is a medical device for urological bladder drainage. Based on the provided 510(k) summary, here's an analysis of its acceptance criteria and the supporting study information:

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

The document acts as a 510(k) summary, which typically focuses on demonstrating substantial equivalence to a predicate device rather than explicitly stating acceptance criteria and direct performance metrics in a readily quantifiable "reported device performance" table format for a novel performance claim. However, it does indicate the studies performed and their objectives. The "acceptance criteria" here are implicitly linked to compliance with recognized standards and successful demonstration of substantial equivalence.

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. For such tests, the concept of "sample size for the test set" is usually described in terms of the number of tested devices or batches, which is not explicitly provided in this summary. The data provenance (country of origin, retrospective/prospective) is not applicable or provided for these types of non-clinical tests in this summary.

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

This information is not applicable to the non-clinical (bench) testing described. "Ground truth" established by experts is typically relevant for clinical studies or studies involving diagnostic accuracy, which were not performed here.

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

Not applicable to the non-clinical (bench) testing described. Adjudication methods are usually used in clinical studies for disagreement resolution among expert readers.

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 such study was done or is referenced. This device is a catheter, not an AI-powered diagnostic tool, so MRMC studies involving human readers and AI assistance are not relevant.

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

Not applicable. This device is a physical medical device (catheter), not an algorithm.

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

For the non-clinical tests performed:

• Biocompatibility: The "ground truth" is compliance with ISO 10993-1, which is a recognized international standard based on established scientific principles for biological evaluation.
• Dimensional Verification: The "ground truth" is the engineering design specifications and possibly comparative measurements to predicate devices.
• Functional and Performance Testing: The "ground truth" is the designed functional specification of the device (e.g., the signal balloon inflates under specific pressure conditions) and compliance with performance standards like ASTM F623 and EN 1616. These standards themselves define the "ground truth" for acceptable performance.

8. The sample size for the training set

Not applicable. This device is a physical medical device, not a machine learning algorithm that requires a training set.

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

Not applicable, as no training set was used.

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The SignalMark Lung Biopsy Site Marker is intended to provide accuracy in marking a biopsy location for visualization during surgical resection.

The SignalMark Lung Biopsy Site Marker is a medical device used by a physician to percutaneously place a small implantable hydrogel marker in lung tissue biopsy to "mark" the location of the biopsy site. It is intended to be used on adults undergoing percutaneous lung biopsies, in surgical settings, such as hospitals or medical clinics with operating suites. The SignalMark Lung Biopsy Site Marker consists of two components:

• Applicator: Component made of plastic and stainless steel that pushes the marker into the tissue.
• Marker: Component made of USP-grade porcine gelatin-based hydrogel with methylene blue-colored silicon dioxide microspheres. The marker aids in the visualization of tissue allowing surgeons to readily locate the biopsy site for subsequent tissue or tumor resection.

This document is a 510(k) summary for the SignalMark Lung Biopsy Site Marker. It details the process taken to demonstrate substantial equivalence to a predicate device, rather than proving the device meets specific acceptance criteria based on clinical performance in an AI/imaging context. Therefore, most of the requested information regarding acceptance criteria, specific study design (e.g., MRMC, standalone), ground truth establishment, expert qualifications, and sample sizes for training/test sets are not applicable or extractable from this document.

This device is not an AI/imaging device. It is an implantable marker used to physically mark a biopsy site for later surgical resection. The acceptance criteria and study detailed in this document are primarily focused on non-clinical performance (bench and animal testing) to demonstrate its safety and biological compatibility, and technical equivalence to a previously cleared predicate device.

However, I can extract information related to the device's performance testing from the "Summary of Non-Clinical Testing" section and interpret it in the context of "acceptance criteria" for this type of device.

Here's the relevant information that can be extracted, and where the requested information is not applicable:

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

For this device, "acceptance criteria" are implied by the non-clinical testing performed to demonstrate equivalence and safety. The document states "No FDA performance standards have been established for SignalMark Lung Biopsy Site Marker," meaning there aren't quantitative metrics like accuracy, sensitivity, specificity that need to be met. Instead, "acceptance" is demonstrated through successful completion of the listed tests, ensuring the device functions as intended and is safe.

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

• Test Set Sample Size: The document does not specify exact sample sizes for each of the bench or animal tests. It only lists the types of tests performed (e.g., "Biodistribution in rodents," "Safety and efficacy in porcine").
• Data Provenance: The document does not specify the country of origin of the data or whether the studies were retrospective or prospective. Given the nature of a 510(k) submission, these are typically pre-market studies conducted specifically for regulatory submission, implying they are prospective.

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

• Not applicable. This device is not an AI/imaging device, and thus there is no "ground truth" to be established by experts in the context of image interpretation or diagnostic accuracy. The "ground truth" for this device's performance testing would be the physical properties measured in bench testing and biological responses observed in animal studies.

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

• Not applicable. This applies to establishing ground truth for diagnostic accuracy in imaging studies, which is not relevant 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

• Not applicable. This type of study is relevant for AI-assisted diagnostic tools, not for an implantable biopsy site marker.

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

• Not applicable. This also applies to AI algorithms.

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

• The "ground truth" for this device's evaluation is based on engineering measurements and biological observations from bench and animal testing. This includes:
  • Physical dimensions and deployment success (bench testing).
  • Material biocompatibility (ISO 10993-1).
  • Biodistribution, safety, and efficacy in animal models.

8. The sample size for the training set

• Not applicable. This device does not involve machine learning or a "training set."

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

• Not applicable. See point 8.

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The Signal Gear Urethral Catheter Electrode is intended for mucosal surface stimulation/recording from the external urinary sphincter for use in conjunction with urodynamic evaluation of the patient.

The Signal Gear Urethral Catheter Electrode is a single patient use, disposable sterile device. It consists of an adhesive, highly flexible, polyurethane film substrate that wraps around the urethral (Foley) catheter. The conductive portion of the electrode is printed onto the polyurethane film substrate in a pattern that, when the electrode is wrapped around the catheter, leaves two conductive contact surfaces forming the two electrical contacts of the electrode. The remainder of the conductive pattern is overwrapped by an additional layer of polyurethane film during manufacture in order to insulate the traces. The conductive ink used is silver, with the exception of the two electrical contacts of the electrode, which have an additional printed layer of gold ink.

Lead wires are attached that terminate in a safety connector that cannot be connected to an AC power outlet. The Electrode connects to the user's electrodiagnostic equipment. The electrode is to be used under the supervision of a physician. When the urethral catheter with the applied electrode is inserted in the human urethra, the exposed two electrical contacts of the electrode contact the mucosal lining of the urethra in the location of the external urinary sphincter muscle, and the end of the electrode with leadwire attachments are outside of the urethral meatus.

The provided 510(k) summary for the Neurovision Medical Products Inc. Signal Gear Urethral Catheter Electrode (K113771) describes a non-clinical study to support substantial equivalence. It does not present acceptance criteria or detailed performance data for clinical use, nor does it describe a study involving human subjects or AI.

Here's an analysis based on your request, highlighting what information is not present in the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not present in the provided document. The 510(k) summary focuses on non-clinical testing to demonstrate substantial equivalence to a predicate device, rather than defining specific performance metrics or acceptance criteria related to its intended physiological function (mucosal surface stimulation/recording) in a clinical setting.

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

• Sample Size for Test Set: Not applicable, as this was a non-clinical study.
• Data Provenance: Not applicable, as this was a non-clinical study.

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

Not applicable, as this was a non-clinical study. The "ground truth" in this context would be engineering specifications and standards for electrical, biocompatibility, and sterilization testing.

4. Adjudication Method for the Test Set

Not applicable, as this was a non-clinical study.

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

No, an MRMC comparative effectiveness study was not done and is not mentioned. This type of study is relevant for diagnostic devices where human readers interpret images or data, often with AI assistance. The Signal Gear Urethral Catheter Electrode is a medical instrument for stimulation/recording, not a diagnostic imaging device.

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

No, a standalone (algorithm only) study was not done and is not mentioned. The device is an electrode for use in conjunction with urodynamic evaluation, implying human operation and interpretation of data. There is no mention of an "algorithm" as a component of this device's performance.

7. Type of Ground Truth Used

For the non-clinical testing, the "ground truth" was based on:

• Engineering specifications and quality standards for manufacturing.
• Established international and industry standards for biocompatibility (ISO 10993-5:2009, ISO 10993-10:2010).
• Standards for accelerated aging (ASTM F1980-07).
• Standards for packaging validation (UNI EN ISO 868-5, 11607-1, DIN 58953-6).
• Standards for bioburden and sterility (UNI EN ISO 11737-1, 11737-2).

8. Sample Size for the Training Set

Not applicable. This device does not involve a "training set" in the context of machine learning or AI.

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

Not applicable. As there is no training set mentioned, the ground truth for it was not established.

Summary of the Study that Proves the Device Meets Acceptance Criteria (Non-Clinical):

The Neurovision Medical Products Inc. Signal Gear Urethral Catheter Electrode underwent several non-clinical tests to demonstrate its safety and performance, and thus its substantial equivalence to the predicate device (Dantec 13L40 St Mark's Pudendal Electrode).

The study involved:

• Inspection for defects: Manufacturing, workmanship, and packaging.
• Electrical continuity testing: To ensure proper electrical function.
• Lead wire pull testing: To assess the mechanical strength of the lead wire attachment.
• Biocompatibility testing:
  • In vitro cytotoxicity: According to ISO 10993-5:2009.
  • Irritation and delayed-type hypersensitivity: According to ISO 10993-10:2010.
• Accelerated aging: Conducted according to ASTM F1980-07 to predict shelf life.
• Packaging validation tests: Conducted according to UNI EN ISO 868-5, 11607-1, and DIN 58953-6 to ensure package integrity and maintain sterility.
• Bioburden and sterility tests: Conducted according to UNI EN ISO 11737-1 and 11737-2 to confirm the device is sterile when packaged.

The document indicates that these non-clinical tests were sufficient to demonstrate that "No new questions of safety or effectiveness are raised," supporting its substantial equivalence to the predicate device. However, specific acceptance criteria for each test (e.g., minimum pull strength, maximum cytotoxicity score) and the quantitative results are not provided in this summary. The FDA's 510(k) clearance implies that the agency found these non-clinical tests adequate for demonstrating substantial equivalence based on the provided data.

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Signal is an electronic device intended to support the physician in the evaluation of heart sounds, lung sounds, bowel sounds and other acoustic signals in patients. The device allows for the recording of multiple, simultaneous acoustic signals and includes a single-lead electrocardiograph that produces a visual display of the electrocardiogram (ECG) on the PC monitor to enable the healthcare practitioner to synchronize the phonocardiogram with the beginning of the heart cycle. The ECG is not intended for diagnostic use. The device also allows the sharing of data through the Internet and provides a means to attach patient information to the stored sound files. Signal is intended for use by healthcare professionals only.

Signal™ is an electronic auscultation device intended to acquire, record, and display heart sounds and other body sounds. Signal includes a single-lead electrocardiograph to produce an ECG on the laptop monitor to enable the health care professional to synchronize the phonocardiogram with the beginning of the heart cycle. The device also allows the sharing of data through the Internet and provides a means to attach patient information to the stored sound files.

The provided text describes the Zargis Medical Corporation's Signal™ electronic stethoscope, but it does not contain the detailed information required to fill out all the columns in the requested table or answer all the specific questions about acceptance criteria and a study proving those criteria.

The 510(k) summary focuses on demonstrating substantial equivalence to predicate devices based on design, technological characteristics, indications for use, and performance characteristics. It outlines general categories of testing (biocompatibility, electrical safety/EMC, software verification/validation) and states that "All product specifications were met." However, it does not provide specific quantitative acceptance criteria or the results of a study designed to demonstrate those criteria were met in the way that would typically be expected for a detailed performance study.

Here's a breakdown of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided text, specific quantitative acceptance criteria and detailed reported device performance are NOT available. The document generically states that "All product specifications were met" following non-clinical performance testing.

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

• Sample size for test set: Not specified in the provided document.
• Data provenance: Not specified in the provided document (e.g., country of origin, retrospective or prospective). The document only mentions "Non-clinical performance testing."

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

• Not applicable as no clinical study with expert-established ground truth is described. The testing mentioned is "non-clinical performance testing."

4. Adjudication method for the test set

• Not applicable as no clinical study with expert adjudication is described. The testing mentioned is "non-clinical performance testing."

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, a multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned in the provided document. The device is described as "non-interpretive" and intended to "support the physician in the evaluation," implying it's a tool for obtaining data rather than an AI for interpretation.

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

• The document implies that the device itself (Signal™) was tested for its performance. However, there is no specific mention of an "algorithm only" standalone performance as would be done for an AI diagnostic algorithm detached from human use. The device is an electronic stethoscope that acquires, records, and displays sounds, and includes an ECG for synchronization; it's not described as having an interpretive algorithm that would be tested in a "standalone" fashion.

7. The type of ground truth used

• Given that the described testing is "non-clinical performance testing" for device specifications, the "ground truth" would likely refer to engineering specifications, reference standards, and established test protocols for electrical safety, biocompatibility, and software functionality, rather than clinical ground truth (e.g., pathology, expert consensus, outcomes data).

8. The sample size for the training set

• Not applicable/Not specified. The document describes a medical device (electronic stethoscope), not an AI algorithm that would typically require a "training set."

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

• Not applicable/Not specified. As above, this document describes device testing, not AI algorithm training.

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Signal™ is a visual and rapid test for the qualitative determination of human chorionic gonadotropin (hCG) in urine to aid in early detection of pregnancy. This test is intended for over-the-counter use.

Signal™ detects the presence of hCG in urine by using a combination of polyclonal and monoclonal antibodies on the reagent pad and the test strip. The assay begins functioning with the addition of urine onto the absorbent wick. Through capillary action the urine is pulled along the membrane to the reagent pad where the hCG reacts with the conjugate. Positive results are determined by two colored lines in the window. The absence of hCG produces only one colored line in the window.

Here's an analysis of the provided information, structured to address your specific points:

The provided text contains limited details regarding the acceptance criteria and the full study methodology. Based on the available summary, here's what can be extracted:

Acceptance Criteria and Device Performance

Note: The exact numerical values for specificity, sensitivity, and accuracy are not provided, only that they exceeded "99% acceptance." It's unclear if "acceptance" refers to meeting a pre-defined threshold or simply indicating a high performance.

Study Information

The document describes a "Laboratory Comparison Study" and mentions other performance studies.

1. Sample sizes used for the test set and the data provenance:

   • Test Set Sample Size: For the "Laboratory Comparison Study," a sample size of 100 test devices was used.
   • Data Provenance: Not explicitly stated. Given it's a laboratory comparison study of a diagnostic device, it likely involved collected human urine samples, but no specifics on country of origin or whether it was retrospective or prospective are provided.

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

   • This information is not provided in the document. For a pregnancy test, the "ground truth" would typically be established through a reference method (e.g., a laboratory-grade hCG assay with known performance characteristics) or clinical confirmation of pregnancy. The document only mentions comparison against another device (SAS™ Pregnancy Strip Test Kits).

3. Adjudication method for the test set:

   • This information is not provided in the document. It's unlikely that "adjudication" in the sense of expert review of ambiguous cases would be required for a one-step qualitative pregnancy test unless there were discrepancies between the device and the reference standard that needed resolution.

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

   • No, an MRMC comparative effectiveness study was not done. This device is a rapid, one-step visual test intended for over-the-counter home use, meaning the "reader" is typically the end-user. As such, there is no AI component or human assistance model relevant to an MRMC study for this device.

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

   • Partially applicable. This device is a standalone diagnostic test. Its performance metrics (specificity, sensitivity, accuracy) were assessed for the device itself without explicit human "in-the-loop" assistance. However, it's a visual test, meaning a human always interprets the result (the presence or absence of colored lines). Therefore, the "standalone" performance here refers to the chemical and physical reaction of the device, assuming correct interpretation by the user.

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

   • The primary ground truth for the "Laboratory Comparison Study" was another device: SAS™ Pregnancy Strip Test Kits.
   • For the "Specificity, Sensitivity & Accuracy" studies, the ground truth would have been established by confirming the presence or absence of hCG in urine samples, likely using a laboratory reference method (e.g., quantitative hCG assay). The document does not specify the exact method for this initial ground truth.

7. The sample size for the training set:

   • This information is not provided in the document. For non-AI/ML devices like this pregnancy test, there isn't a "training set" in the computational sense. The device's design is based on established biological and chemical principles, and its performance is validated through testing on various samples, not through iterative training.

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

   • As there is no "training set" in the AI/ML context for this device, this question is not applicable. The ground truth for validating the device's performance would be established as described in point 6.

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