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The ReSound® Loudness Growth in Octave Bands (LGOB) Loudness Scaling System is a new indication for use for the ReSound Digital 5000 hearing device family. The control for the system is incorporated into ReSound ReSource II fitting application software. The testing procedure is intended to be performed only by qualified hearing healthcare professionals.

ReSound's Digital 5000 series hearing devices are built using a custom software programmable digital signal processor. The DSP includes two analog to digital converters (ADC) at the input stage and one digital to analog converter (DAC) at the output stage. Using a personal computer (PC) connected to the hearing device via standard CS45 cable, numerous signal processing and amplification parameters may be adjusted to help compensate for impaired hearing. In addition, proprietary software used only by hearing health care professionals can instruct the output DAC to produce specific sounds. These sound stimuli, when delivered at different amplitudes and frequencies, can be used for loudness scaling measurements helpful in fitting the hearing device more precisely to the patient's residual hearing dynamic range.

The LGOB test equipment is comprised of proprietary PC software and a ReSound Digital 5000 series hearing device. Similar to the Loudness Growth in Octave Bands (LGOB) test provided with the original ReSound® P3 System, this new hearing device fitting method is designed to subjectively measure loudness growth in hearingimpaired subjects using the subject's own hearing aid, instead of using insert phones. The test is to be performed only by qualified hearing healthcare professionals. When the hearing aids are disconnected from the fitting computer, they function normally.

Here's an analysis of the provided text regarding the acceptance criteria and study for the ReSound LGOB Test using Digital Hearing Devices:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document, a 510(k) Premarket Notification, focuses on demonstrating substantial equivalence to a predicate device rather than presenting specific numerical acceptance criteria and performance metrics for the new device. Therefore, a table in the traditional sense, with quantitative acceptance criteria and reported values, cannot be fully constructed from this document.

However, based on the principle of substantial equivalence, the implicit acceptance criteria are that the new device performs at least as well as and is as safe and effective as the predicate device for its intended use. The performance characteristics described are qualitative comparisons to the predicate.

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

The document does not provide information on a specific test set, its sample size, or data provenance (country of origin, retrospective/prospective). The filing is based on demonstrating substantial equivalence through comparison of design, intended use, and technical principles, rather than presenting a new clinical trial for performance validation. The comparison is conceptual, referring to the functionality and method of the predicate device (ReSound Portable Prescriptive Programming (P3) System, K912669).

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

No explicit "ground truth" establishment by experts for a specific test set is described. The validity of the LGOB method itself is referenced through a published scientific paper: "J. B. Allen. J. L. Hall, and P. S. Jeng. 'Loudness growth in 1/2-octave bands (LGOB) - A procedure for the assessment of loudness', Journal of the Acoustical Society of America - 1988 (2), 1990, pp. 745-753." This publication describes the scientific basis for the LGOB procedure, which is the foundation of both the predicate and the new device.

The document states the device is intended to be used by "qualified hearing healthcare professionals," implying that the interpretation and application of the results would be done by these experts.

4. Adjudication Method for the Test Set

Not applicable, as no specific test set requiring adjudication is described in the document.

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. The device described is not an AI-assisted diagnostic tool in the sense of image interpretation for human readers. It's an audiometric diagnostic device where the patient provides subjective responses, and the hearing healthcare professional uses this data for fitting. No MRMC study or AI assistance is mentioned.

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

The device is inherently not standalone in terms of interpretation or final action. While the software generates stimuli and collects data, a human hearing healthcare professional is explicitly required to perform the test, interpret the results, and adjust hearing aid settings. The patient's "human-in-the-loop" subjective response is central to the LGOB test itself.

The document states: "The software which controls the system is incorporated into ReSound® ReSource II software (K945750) that runs as a standalone application or can be integrated under the Noah operating system (K942749)." This refers to the software application running on a PC, not the diagnostic device itself performing standalone interpretation.

7. The Type of Ground Truth Used

The "ground truth" for the LGOB test is the patient's subjective perception of loudness. The test relies on the patient indicating their perceived loudness ("Too Loud, Very Loud, Loud, Comfortable, Soft, Very Soft or Inaudible") for various sound stimuli. This subjective data, combined with objective audiometric threshold data (entered separately by the professional), forms the basis for calculating fitting recommendations.

The scientific validity of the method (Loudness Growth in Octave Bands) is established by the referenced scientific literature (Allen, Hall, and Jeng, 1988/1990), which provides the theoretical and empirical underpinnings for using subjective loudness scaling.

8. The Sample Size for the Training Set

The document does not refer to a "training set" in the context of machine learning or AI models. This device is not described as utilizing a machine learning algorithm that requires a training set. The software likely implements established algorithms for calculating fitting recommendations based on the LGOB data and audiometric thresholds, rather than learning from a dataset.

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

Not applicable, as no training set for a machine learning model is mentioned. The "ground truth" for the device's function is rooted in audiology principles and the subjective responses of the patient as described in point 7.

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The indication for use of the audiometer in this submission would be for the detection and diagnosis of a suspected hearing loss.

Not Found

This is a medical device approval letter from the FDA to Bernafon-Maico, Inc. regarding their Maico MA42 audiometer. The letter states that the device is substantially equivalent to devices marketed prior to May 28, 1976, and can therefore be marketed.

However, the provided text does not contain any information about acceptance criteria or a study proving the device meets those criteria. It is a regulatory approval document confirming substantial equivalence, not a detailed technical report on performance or testing.

Therefore, I cannot fulfill your request for:

1. A table of acceptance criteria and the reported device performance
2. Sample size used for the test set and the data provenance
3. Number of experts used to establish the ground truth for the test set and their qualifications
4. Adjudication method for the test set
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done
6. If a standalone (algorithm only) performance was done
7. The type of ground truth used
8. The sample size for the training set
9. How the ground truth for the training set was established

The document focuses solely on the regulatory approval process based on substantial equivalence, and does not delve into the specific performance metrics or studies you're asking for.

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The Interacoustics Model AD229 Diagnostic Audiometer is indicated for use in conducting diagnostic hearing evaluations and assisting in the diagnosis of possible otologic disorders.

Because of its master hearing aid capability this device is also indicated for simulating a hearing aid during audiometric testing thus it may help in the selection and adjustment of a patient's hearing aid.

The Interacoustics Model AD229 Diagnostic Audiometer is an electroacoustic device that produces controlled levels of test tones and signals. The unit employs digital readouts and includes an external power supply that contains an isolation transformer. The tones and sound signals are directed to the patient by means of the following transducers: TDH39 Audiometric Headset (standard); EAR-Tone 3A Insert Phones (optional); and B71 Bone Conductor (standard). This device also has master hearing aid capability and utilizes either the TDH39 Headset or the Ear-Tone 3A Insert Phones for the acoustical outputs.

This 510(k) summary focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study report with specific acceptance criteria and performance metrics for a novel AI/software device. Therefore, many of the requested details are not available in the provided text.

Here is the information that can be extracted or reasonably inferred from the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The device is evaluated against recognized performance and safety standards, which serve as its "acceptance criteria" for basic functionality and safety. The reported device performance is its compliance with these standards.

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

This information is not provided in the 510(k) summary. The summary focuses on compliance with standards and comparison to a predicate device, not a clinical study on a specific test set of data.

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

This information is not provided. As this is a medical device (audiometer) and not an AI/software device requiring human interpretation of results, the concept of "ground truth" established by experts in the context of a test set for diagnostic accuracy does not directly apply here. The device itself generates the "result" (e.g., hearing thresholds).

4. Adjudication Method for the Test Set

This information is not provided. This concept is not relevant for the type of device and submission 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 provided. An MRMC study is not applicable to an audiometer, which is a diagnostic tool used by a clinician to measure hearing, not an AI system that interprets images or data requiring human-in-the-loop assistance.

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

This information is not provided. The device is an electroacoustic device that measures hearing; it's not an "algorithm only" device in the context of typical AI/software. Its performance is inherent in its measurement capabilities, which are assessed against standards.

7. The Type of Ground Truth Used

The "ground truth" for an audiometer is its ability to accurately and reliably produce and measure sound levels and frequencies according to established audiological standards. Therefore, the "ground truth" would be established by compliance with scientific and engineering standards (IEC 645-1-1991 Type 2, ANSI 3.6-1996 Type 2), verified through calibration and performance testing.

8. The Sample Size for the Training Set

This information is not provided. Audiometers are not typically developed using "training sets" in the way AI/Machine Learning models are. Their design and calibration are based on physical principles and established audiological standards.

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

This information is not provided and is not applicable for this type of medical device submission.

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Moderate to severe conductive hearing losses. Particularly useful for conductive losses compounded by congenital or secondary obstruction of auditory air conduction mechanisms

The EC-1000 Second Ear ' is a bone conduction-type hearing aid. Unlike conventional products which depend upon acoustic coupling through air, the EC-1000 Second Earl is based on bone conduction technology. Sound is transmitted directly through the bones of the skull to the cochlea, bypassing the outer and middle ear.

The EC-1000 Second Ear offers an innovative tone control system, providing flexible audio control adjustments. Also, features such as a voice filter which when selected, reduces additional exterior sound. As an added feature an auxiliary input jack allows the user to connect Search Eas ® directly to TV, CD players, portable radios, tape players or even telephones.

State of the art nickel-metal-hydride rechargeable battery technology is used for maximum battery life while minimizing size and weight.

Transducer

The EC-1000 Second Ear consists of a transducer unit, a shirt pocket sized audio processor/driver module and a wire cable for connecting the transducer with the audio processor/driver. The transducer is a convenient sized unit that is held against the head and is driven electrically to transmit audio vibrations to the underlying bones of the skull. It is a lowimpedance device, similar to a loudspeaker.

Audio Processor/Driver

The audio processor/driver module connects the transducer through the wire cable and usually would be carried in the users pocket. The wire cable connecting the audio processor/driver and the transducer can be placed comfortably at sensitive location such as behindear.

The audio processor/driver module is completely self-contained and is battery powered. It also has a built-in microphone for picking up sound. A rechargeable battery pack is the power source.

Battery Pack

The battery pack can be charged in place in the unit or snapped out for easy exchanging. By purchasing a spare battery pack, the user can be using one battery while charging the other, guaranteeing uninterrupted use of the EC-1000. The user should be able to obtain 24 to 48 hours of operation in normal use from fully charged batteries. Volume setting and environmental conditions affect battery life.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the Second Ear® Bone Conduction Hearing Aid (K953872):

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state formal acceptance criteria. Instead, it relies on demonstrating substantial equivalence to legally marketed predicate devices. The "acceptance criteria" are implicitly met if the device's characteristics and performance are comparable to the predicates, indicating it's as safe and effective.

The provided "FEATURE COMPARISON TABLE" serves as the primary evidence for this comparison.

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

The document does not describe a specific "test set" or a clinical study with real patient data used to demonstrate performance. The entire basis for establishing equivalence is through a comparison of technical specifications and features with already marketed predicate devices.

Therefore:

• Sample size for the test set: Not applicable (no test set described).
• Data provenance: Not applicable (no patient data used for comparison/testing). The data is derived from the technical specifications of the Second Ear® and its identified predicate devices.

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

Since no clinical test set or study is described, there's no mention of experts establishing ground truth in this context. The "ground truth" for substantial equivalence is effectively the established performance and safety profile of the predicate devices as determined by their prior FDA clearances.

4. Adjudication Method for the Test Set

Not applicable, as no test set or clinical study requiring adjudication is described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done or described in this 510(k) summary. This type of study would involve human readers (e.g., audiologists, patients) assessing performance with and without the device, and potentially comparing it to other devices. This 510(k) relies purely on technical specifications.

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

The device is a hardware hearing aid; it does not involve an "algorithm only" component. Performance is inherent to the physical device. The document does not describe any standalone performance testing in the sense of a formal clinical trial or laboratory study with performance metrics beyond the technical specifications listed. The "standalone performance" is implicitly established by the technical specifications presented in the comparison table.

7. The Type of Ground Truth Used

The "ground truth" used for this 510(k) submission is the established performance and safety profiles of the legally marketed predicate devices. The new device's specifications are compared against these known, cleared devices to argue for substantial equivalence. There is no pathology, expert consensus, or outcomes data presented for the Second Ear® itself in this document.

8. The Sample Size for the Training Set

Not applicable. The device is a hardware hearing aid; there is no machine learning "training set" involved.

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

Not applicable, as there is no training set.

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