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ENLIGHT 2100 is a non-invasive, radiation free medical device that provides information from impedance variation from a cross-section of a patient's thorax. This information is presented to the clinician user as an adjunctive tool to other clinical information in order to support the user's assessment of variations in regional air content within a cross section of a patient's thorax.

ENLIGHT 2100 also provides respiratory parameters based on spirometric monitoring.

It is intended for mechanically ventilated adult and pediatric patients in a hospital setting, whose thorax perimeter is within the range of 37.5 - 134 cm.

ENLIGHT 2100 does not measure regional ventilation of the lungs

ENLIGHT 2100 is a Ventilatory electrical impedance tomograph that uses several electrodes (usually between 16 and 32) placed around the patient's thorax to assess regional impedance variation in a lung slice (tomography). It provides a relative measurement, so it only provides information on variations in local impedance. ENLIGHT 2100 estimates Local Impedance Variation, occurring in a cross section of the thorax during a respiratory cycle, and which are linearly related to Variations in Regional Air Content within the lung.

The ENLIGHT 2100 is a ventilatory electrical impedance tomograph that also provides respiratory parameters based on spirometric monitoring. It is intended for mechanically ventilated adult and pediatric patients in a hospital setting. The device provides information on impedance variation from a cross-section of a patient's thorax as an adjunctive tool to support the user's assessment of variations in regional air content. It does not measure regional ventilation of the lungs. The device relies on a primary predicate (ENLIGHT 2100 - K211135) for its Electrical Impedance Tomography (EIT) data and a secondary predicate (Philips NM3 Respiratory Profile Monitor with VentAssist – K103578) for its spirometric monitoring capabilities. The data provided focuses on non-clinical/bench testing to demonstrate substantial equivalence to these predicates.

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria for the ENLIGHT 2100 are based on its performance characteristics in comparison to its predicate devices for both EIT parameters and spirometric parameters. The study conducted was non-clinical bench testing.

Table 1: Acceptance Criteria and Reported Device Performance

The "Explanation of Differences" column in the provided tables typically serves as the primary source for the reported device performance and the proof that it meets the acceptance criteria (i.e., demonstrating substantial equivalence to the predicates based on the non-clinical testing). For the EIT parameters, the performance is reported as "Same" as the predicate, with the rationale that the hardware and EIT algorithm are identical. For the spirometric parameters, specific accuracy metrics are provided for the subject device and its accuracy relative to the secondary predicate (NM3).

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

The document explicitly states that "Bench Testing - We have performance tests to check the automatic calculation of the parameters shown at the Trend Screen." and lists "Non-clinical testing" and "Bench Test" as the method. Therefore, the data provenance is bench test data. No information is provided regarding the specific sample size (e.g., number of test points, simulated cases, or repetitions) used for this bench testing. The data does not specify the country of origin, but given the sponsor's location (Brazil) and the nature of bench testing, it would likely be laboratory/engineering data. It is inherently prospective as it involves controlled testing to confirm performance.

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

The study described is non-clinical bench testing. It involves evaluating the device's numerical calculations and performance against specified ranges and accuracy criteria, likely using simulated physiological signals or validated reference equipment. Therefore, no human experts were used to establish ground truth in the way they would be for image interpretation or diagnosis. The ground truth for this type of testing is established by the specifications of the signals generated or the reference standards of the testing equipment.

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

Given that this was a non-clinical bench study focused on numerical calculation accuracy and signal acquisition performance, no human adjudication method was employed. The "ground truth" was inherently defined by the test setup and reference measurements, not by human interpretation or consensus.

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

No MRMC comparative effectiveness study was done. The device (ENLIGHT 2100) functions as a sensor and data display unit for physiological parameters, not an AI-assisted diagnostic tool that would directly assist human readers in interpreting complex medical images or data where a reader study would be applicable. The document describes it as an "adjunctive tool to other clinical information," implying it provides data for clinicians to interpret, but not in a way that necessitates an AI-assistance reader study.

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

Yes, a standalone (algorithm only) performance evaluation was implicitly done through the bench testing. The document states, "We have performance tests to check the automatic calculation of the parameters shown at the Trend Screen." This testing evaluates the device's ability to accurately measure and calculate the specified EIT and spirometric parameters within defined ranges and accuracies, independent of human interaction or interpretation beyond setting up the test and recording results.

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

The ground truth used for this non-clinical bench testing was established through engineered inputs/simulations and/or comparisons to a validated reference standard. For example, for spirometric parameters like Tidal Volume or Respiratory Rate, the system would be fed precisely controlled and measured airflow/pressure signals, and the device's output would be compared to the known input values of these signals. For EIT parameters, the ground truth would similarly come from controlled electrical impedance variations generated under laboratory conditions.

8. The sample size for the training set

The document describes non-clinical bench testing for the purpose of demonstrating substantial equivalence. It does not mention any machine learning or AI components that would require a separate training set. Therefore, information regarding a training set sample size is not applicable or provided in this context. The device's algorithms are likely based on established physiological and electrical impedance principles, not trained on a large dataset of patient measurements requiring specific "training set" ground truth establishment.

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

As there is no mention of a training set or machine learning/AI model training, the question of how its ground truth was established is not applicable based on the provided information.

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ENLIGHT 2100 is a non-invasive, non-radiation medical device that provides information of local impedance variation within a cross-section of a patient's thorax. This information is presented to the clinician user as an adjunctive tool to other clinical information in order to support the user's assessment of variations in regional air content within a cross section of a patient's lungs.

It is intended for mechanically ventilated adult and pediatric patients in a hospital setting, whose thorax perimeter is within the range of 37.5 - 134cm.

ENLIGHT 2100 does not measure regional ventilation of the lungs.

ENLIGHT 2100 is a Ventilatory electrical impedance tomograph that uses several electrodes (usually between 16 and 32) placed around the patient's thorax to assess regional impedance variation in a lung slice (tomography). It provides a relative measurement, so it only provides information on variations in local impedance.

ENLIGHT 2100 estimates Local Impedance Variation, occurring in a cross section of the thorax during a respiratory cycles, and which are linearly related to Variations in Regional Air Content within the lung.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter for the Timpel S.A. Enlight 2100 device:

Important Note: The provided document is a 510(k) clearance letter from the FDA. 510(k) clearance is based on demonstrating substantial equivalence to a previously legally marketed device (predicate device), not necessarily on rigorous clinical effectiveness studies or extensive AI model validation metrics typically seen for novel AI/ML devices. Therefore, the details around specific acceptance criteria, test sets, ground truth establishment, and MRMC studies for AI performance, as might be expected for an AI/ML software as a medical device (SaMD), are not explicitly detailed in this type of regulatory document. The focus here is on demonstrating that the new device (Enlight 2100) is sufficiently similar to the predicate (Enlight 1810) in terms of safety and performance.

Analysis of Acceptance Criteria and Device Performance

The provided document describes bench testing to demonstrate substantial equivalence, rather than clinical studies with human readers or AI-specific performance metrics like sensitivity/specificity for a diagnostic task. The acceptance criteria are implicit in the comparison table (Table 5.1) between the subject device (Enlight 2100) and the predicate device (Enlight 1810). The "Explanation of Differences" column directly indicates whether the performance attributes are similar or if one is slightly better, implying the new device meets or exceeds the predicate's performance.

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria (Implied: Performance comparable or better than Predicate Device)

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

• Sample Size: The document does not specify a "test set" in terms of patient data or a specific number of unique tests for each performance characteristic. Instead, it refers to "bench testing" which implies testing on a controlled system or phantom, not human subjects, for these specific performance metrics. The comparison table focuses on instrument performance rather than AI model performance on a clinical dataset.
• Data Provenance: Not specified, as these are bench test results of the device itself rather than clinical data from a specific region or patient cohort. It is implicitly "prospective" bench testing of the new device.

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

• Not applicable for this type of submission. The "ground truth" for the performance characteristics listed in the table comes from verifiable engineering measurements on the device, often using calibrated equipment or phantoms, not human expert consensus. This is a technical performance evaluation, not a clinical diagnostic assessment.

4. Adjudication Method for the Test Set

• Not applicable. As noted above, the "test set" refers to technical performance characteristics measured through bench testing, not a dataset requiring human adjudication of clinical findings.

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

• No, an MRMC comparative effectiveness study was not done as described for AI assistance. This submission is for an Electrical Impedance Tomograph (EIT) device, which provides "information of local impedance variation" as an "adjunctive tool." It does not involve AI for interpretation or diagnosis, nor does it directly assist human readers in image interpretation in the way an AI-powered diagnostic tool would. It provides raw data/images for clinicians to interpret.

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

• Yes, in spirit, the bench testing results represent a "standalone" evaluation of the device's technical performance. The reported performance characteristics (SNR, Voltage Accuracy, Drift, etc.) are inherent properties of the device and its internal algorithms, independent of human interaction or interpretation beyond setting up the test. However, this is not an "algorithm-only" study in the typical sense of a diagnostic AI algorithm that produces a specific output (e.g., classifying a disease). The device itself is the "algorithm" and hardware working together to produce data.

7. Type of Ground Truth Used

• The ground truth for the "Performance Characteristics" in Table 5.1 is based on engineering measurements and calibrations using established methods for evaluating electronic medical devices. It is not expert consensus, pathology, or outcomes data. For example, SNR is measured against a known signal input, and position error might be measured using phantoms with known, precisely located impedance changes.

8. Sample Size for the Training Set

• Not applicable. The Enlight 2100 is an EIT device that provides direct physical measurements and derived images based on those measurements, not a machine learning model that requires a "training set" of data in the typical sense. Its "algorithm" is based on the physics of electrical impedance tomography.

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

• Not applicable, as there is no "training set" for an AI model. The device's operation is based on established physical principles and engineering design.

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