Search Results

Earscan 3 is a peripheral used with a computer that creates an air conduction, bone conduction and speech audiometer, as defined by the standards shown in the labeling of the About Screen and the Splash Screen of the Earscan 3 software.

The Earscan 3 is similar to and predicated upon the Micro Audiometrics Corp. Earscan and Earscan II, and Decibel Instruments, Inc. ProDigit 2000. Earscan 3 is a computer peripheral whose purpose is to test the condition of the auditory pathway through manual and automatic air conduction audiometry, bone conduction audiometry and speech audiometry. Earscan 3 is a lower level of concern device and is intended to be used by trained personnel in the industrial, school and medical environment.

The provided text is a 510(k) summary for the Micro Audiometrics Corp. Earscan 3, an audiometer. It focuses on demonstrating substantial equivalence to predicate devices rather than proving performance against specific acceptance criteria through a dedicated study with a test set, ground truth, or statistical analysis that are typically found in more recent, high-risk device submissions, especially those involving AI/ML.

Here's an analysis based on the provided text, addressing your points where possible and noting where information is absent:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not present a formal table of acceptance criteria with corresponding performance results in the way typically seen for AI/ML devices or novel high-risk medical devices. Instead, it relies on demonstrating substantial equivalence to existing predicate audiometers by meeting established electrical and mechanical standards.

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

The provided 510(k) summary does not describe a specific test set, sample size, or data provenance in the context of an accuracy or performance study. The submission relies on demonstrating substantial equivalence to its predicate devices by showing it meets recognized industry standards and performs the same functions. There is no mention of a clinical or technical study using a specific dataset to prove its effectiveness in the way one would for an AI/ML device.

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

Given that no specific test set or performance study is described, there is no information provided regarding experts used to establish ground truth.

4. Adjudication Method for the Test Set

As no specific test set or performance study is described, there is no adjudication method provided.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its effect size.

No MRMC study was performed or mentioned in the provided document. The device is an audiometer, which typically measures auditory responses directly, rather than interpreting complex medical images or data that would necessitate human reader interaction for diagnostic interpretation. The submission is focused on the device's technical specifications and functional equivalence.

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

The Earscan 3 is a medical device, specifically an audiometer. While it uses "firmware check" and "software" to prevent certain conditions, these are internal device functions to ensure safety and compliance, not an "algorithm" in the sense of an AI/ML prediction model. Therefore, the concept of a "standalone" or "human-in-the-loop" performance study for an algorithm as you might find for AI/ML devices is not applicable to this type of traditional audiometer submission and is not mentioned.

7. The Type of Ground Truth Used

No explicit "ground truth" is used in the context of a performance study as described in your question. The "ground truth" in this context is implicitly the compliance with established industry standards (ANSI S3.6, IEC 601-1.1992, UL2601, USB Spec. 1.0) and the functional equivalence to predicate audiometers. The device's "correctness" is determined by its adherence to these standards and its ability to perform audiometry as expected, not by comparing its outputs against a diagnostically confirmed truth derived from a patient dataset.

8. The Sample Size for the Training Set

Since the Earscan 3 is a traditional medical device (an audiometer) and not an AI/ML system, the concept of a "training set" is not applicable and therefore no sample size for a training set is mentioned.

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

As there is no training set, there is no description of how any ground truth for a training set was established.

In Summary:

The provided 510(k) summary for the Earscan 3 focuses on demonstrating substantial equivalence to predicate devices by asserting compliance with relevant electrical, mechanical, and performance standards. It does not contain the detailed performance study information (test sets, ground truth, expert adjudication, MRMC studies) typically associated with more complex diagnostic devices or AI/ML systems where objective performance metrics against a defined ground truth are required for clearance. The "effectiveness" is primarily described in terms of its ability to perform similar functions to its predicates and meet safety standards.

Ask a specific question about this device

Intended Use: The Articulate Fitting System™ is used with the ProDigit 2000™ Personal Digital Audiometer to fit and program Articulate Hearing Devices. The ProDigit 2000™ Audiometer produces controlled levels of test tones and signals intended for use in conducting diagnostic hearing evaluations and assisting in the diagnosis of possible otologic disorders.

The Articulate Fitting System™ is used with the Decibel Instruments ProDigit 2000™ Personal Digital Audiometer for audiometric testing, real ear measurements, and fitting and programming the commercially available Decibel Instruments Articulate Hearing Devices. The System has four main components:

• The legally marketed Decibel Instruments ProDigit 2000™ Personal Digital Audiometer, a computer-based audiometry system;
• The Decibel Instruments IntraCanal Probe™ (ICP), a transducer placed in the ear canal for audiometric testing;
• The Decibel Instruments IntraCanal Probe™ Interface, which provides the communication link between the ICP and the ProDigit 2000™ Audiometer and allows real ear measurements; and
• The commercially available HI-PRO hearing aid programmer with the Articulate Fitting software utilizes automated fitting algorithms and visual tools to program the Articulate Hearing Devices following audiometric evaluation.

Here's a breakdown of the requested information based on the provided text, though it's important to note that this document is a 510(k) summary for an audiometer system from 1997, and details on modern AI-driven device studies are not present.

The document describes an "Articulate Fitting System™" primarily for audiometric testing and fitting/programming hearing aids. The performance data provided is for the IntraCanal Probe (ICP) component, which is a transducer.

Acceptance Criteria and Device Performance

There is no explicit table of acceptance criteria or reported device performance in the format of specific metrics (sensitivity, specificity, AUC) in this document. The performance data focuses on the electroacoustic characteristics of the IntraCanal Probe (ICP).

Study Details

Given this is a 510(k) submission from 1997 for an audiometer system, the concept of a "test set" and "training set" for AI/ML algorithms, as well as multi-reader multi-case studies, are not applicable in the modern sense. The "performance data" section focuses on hardware component validation.

1. Sample size used for the test set and the data provenance: Not applicable in the context of an algorithmic test set. The testing was conducted on the physical device component (ICP). The provenance of the data refers to "Decibel Instruments. Inc." as the conductor of the tests.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. The "ground truth" for the ICP's electroacoustic performance would be established by engineering and audiometric standards, measured with calibrated equipment, not by expert consensus on clinical cases.

3. Adjudication method for the test set: Not applicable.

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: Not applicable. This device is an audiometer system, not an AI diagnostic tool that assists human readers.

5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: The document describes the "Articulate Fitting System™" as having "automated fitting algorithms and visual tools" and "auto-fit calculations." However, it clarifies that this system is intended to be used with the ProDigit 2000™ Personal Digital Audiometer by "trained and qualified audiologists or other audiometric professionals." Therefore, while there are automated elements, it's explicitly designed to operate with a "human-in-the-loop," and no standalone algorithm performance study is described. The performance data section focuses on the physical transducer (ICP), not the fitting algorithms.

6. The type of ground truth used: For the ICP, the ground truth was based on engineering and audiometric standards for electroacoustic performance (frequency response, linearity, maximum undistorted output) measured against specified limits.

7. The sample size for the training set: Not applicable. There is no mention of a "training set" for machine learning.

8. How the ground truth for the training set was established: Not applicable.

Ask a specific question about this device