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510(k) Data Aggregation
(124 days)
Fetal Doppler is intended to detect fetal heart rate, and play the fetal heart sound. Fetal Doppler is indicated for use by health care professionals in hospital, clinic, community and home for singleton pregnancies after 12 weeks gestation.
Fetal Doppler is intended to detect fetal heart beats, display fetal heart rate, and play the fetal heart sound. Fetal Doppler is indicated for use by used by health care professionals in hospital, clinic, community and home for singleton pregnancies after 12 weeks gestation. It is comprised of an ultrasonic signal transmitter and receiver, analog signal processing unit, FHR calculating unit, and LCD/TFT display control unit. It has audio output and can be connected with headphones or to a recorder with audio input. The Fetal Doppler is powered by a standard 1.5 V DC alkaline battery.
This document seems to be a 510(k) summary for a Fetal Doppler device, not a study evaluating a device's performance against specific acceptance criteria in the way a clinical trial or algorithm validation study would. Therefore, much of the requested information regarding acceptance criteria for an AI/ML algorithm or a comparative effectiveness study is not available in the provided text.
The document instead focuses on demonstrating substantial equivalence to a predicate device through conformity with standards and basic performance tests.
Here's a breakdown of what is available and what is not:
1. A table of acceptance criteria and the reported device performance
The document does not provide a table with "acceptance criteria" and "reported device performance" in the context of an AI/ML algorithm's effectiveness. Instead, it outlines performance data provided to support substantial equivalence, primarily focusing on safety and basic operational characteristics.
Here's a summary of the performance testing conducted, which might be interpreted as meeting certain "acceptance criteria" for basic functionality:
| Acceptance Criteria Category | Reported Device Performance / Evaluation Method |
|---|---|
| Biocompatibility | Met ISO 10993-1, 10993-5, 10993-10 standards. Testing included Cytotoxicity, Skin Sensitization, and Irritation. |
| Software Verification & Validation | Conducted and completed as per FDA guidance "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May 11, 2005) for a moderate software level of concern. |
| Electromagnetic Compatibility & Electrical Safety | Conformed to relevant requirements of ANSI/AAMI ES 60601-1, IEC 60601-1-2:2014, and IEC 60601-1-11 Edition 2.0 2015-01. |
| Basic Performance Testing | - Use Life Testing- Battery Life Testing- Battery Indicator Testing- Testing per IEC 60601-2-37 Edition 2.1 2015 (ultrasonic medical diagnostic and monitoring)- Acoustic output measurement as per FDA guidance "Marketing Clearance of Diagnostic Ultrasound Systems and Transducers" (June 27, 2019) for Track 1 devices. |
| FHR Measuring Range | 50 – 240 BPM (Matches predicate) |
| FHR Accuracy | ±2BPM (Matches predicate) |
| FHR Resolution | 1BPM (Matches predicate) |
| Iob | <10 mW/cm² (Better than predicate's <20 mW/cm²) |
| Ispta | <50 mW/cm² (Better than predicate's <100 mW/cm²) |
| Isata | <20 mW/cm² (Matches predicate) |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not provided in the document. The performance tests mentioned (biocompatibility, software V&V, electrical safety, use/battery life, acoustic output) are engineering tests, not typically clinical studies involving patient data or test sets in the context of an AI/ML algorithm.
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/not provided. The document describes testing of a medical device's physical and software components, not an AI/ML algorithm requiring expert ground truth for a test set.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not applicable/not provided. As above, this document does not describe an AI/ML algorithm validation study that would involve expert adjudication of a test set.
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 applicable/not provided. This document is for a Fetal Doppler, which is not an AI-assisted diagnostic device. Therefore, no MRMC study or AI assistance evaluation was conducted or reported.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This information is not applicable/not provided. The Fetal Doppler is a standalone device; the question about "algorithm only" performance applies to AI/ML devices, which this is not. The device itself performs the function of detecting and displaying FHR.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
This information is not applicable/not provided. The "ground truth" for a Fetal Doppler would be the actual fetal heart rate, which is derived directly from its acoustic processing, not from expert consensus or pathology in a diagnostic sense. The accuracy of the FHR measurement is stated as ±2BPM.
8. The sample size for the training set
This information is not applicable/not provided. This device is not an AI/ML algorithm that requires a "training set."
9. How the ground truth for the training set was established
This information is not applicable/not provided. This device is not an AI/ML algorithm that requires a "training set" with ground truth.
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