(155 days)
The DeRoyal Esophageal Stethoscope with Temperature Sensor is to be used for routine monitoring of core body temperature, as well as heart and respiratory sounds in an anesthetized patient by inserting the stethoscope tube into the esophagus.
The DeRoyal Esophageal Stethoscope with Temperature Sensor is inserted into the esophagus of anesthetized patients to monitor core body temperature as well as heart and respiratory sounds. The probe consists of a small diameter plastic tube upon which a blue cuff is placed. When inserted in the esophagus, the cuff facilitates transmission of sound to an ear piece attached at the proximal end of the tube. Additionally, a wire set is placed inside the plastic tube. This wire set contains a thermistor chip at the distal end that passively modifies the electrical current traveling through the probe. A blue connector at the proximal end of the wire set interfaces with a cable that is connected to an independent temperature monitor that is used to display the temperature readings.
The probe is sterile and individually packaged. It is available in 9, 12, 18, and 24 French sizes and may come with 400 or 700 series sensors. The stethoscope has a male luer fitting for attachment to standard ear pieces.
The provided text describes a 510(k) submission for a medical device, the "DeRoyal Esophageal Stethoscope with Temperature Sensor". This submission is a premarket notification to the FDA to demonstrate that the new device is substantially equivalent to a legally marketed predicate device.
Crucially, this document is NOT about proving an AI/ML algorithm's performance. It's about a hardware medical device (a stethoscope with a temperature sensor). Therefore, many of the requested points, such as "multi reader multi case (MRMC) comparative effectiveness study," "standalone (i.e. algorithm only without human-in-the loop performance)," "ground truth for the test set," and "training set," are not applicable to this type of device submission.
The document discusses performance tests for manufacturing quality and safety, not for an AI model's diagnostic accuracy.
However, I can extract the relevant information regarding the device's acceptance criteria and the proofs provided within the scope of this hardware device submission.
Acceptance Criteria and Device Performance (for a Hardware Device)
The device in question is the DeRoyal Esophageal Stethoscope with Temperature Sensor. Its primary functions are:
- Monitoring core body temperature.
- Monitoring heart and respiratory sounds.
The submission aims to prove substantial equivalence to a predicate device, the DeBusk Temperature Systems Esophageal Stethoscope (K925789).
Here's a table summarizing the acceptance criteria (as implicitly defined by equivalence to the predicate and relevant standards) and the reported device performance:
| Acceptance Criteria (Relevant to Hardware Device) | Reported Device Performance (Adherence to Standards/Predicate Equivalence) |
|---|---|
| Temperature Accuracy | ± 0.2°C (Matches predicate, tested per ISO 80601-2-56) |
| Stethoscope Functionality | Acoustic responsiveness tested; proposed change (encapsulation) did not affect stethoscope functions. |
| Material Biocompatibility | ISO 10993-1 compliant (Evaluated via biological risk assessment for proposed change; no new testing for non-patient contacting material). |
| Sterilization Method | Sterilized with ethylene oxide (Matches predicate). |
| Electrical Safety | Tested according to IEC 60601-1 and IEC 60601-1-2; leak current test performed. Met requirements. |
| Essential Performance | Accuracy and time response tested per ISO 80601-2-56; demonstrated safe and effective. |
| Mode of Operation | Direct according to ISO 80601-2-56. |
| Intended Use Environment | Hospital. |
| Sizes Available | 9, 12, 18, and 24 French sizes; 400 or 700 series sensors. |
| Prescription Use | Yes (Matches predicate). |
Study Details (Relevant to Hardware Device Testing)
Since this is a hardware device and not an AI/ML algorithm, many of the requested categories are not directly applicable. I will address the relevant ones and explicitly state when a category is not applicable for this context.
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A table of acceptance criteria and the reported device performance: (Provided above)
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Sample size used for the test set and the data provenance:
- Sample Size: The document repeatedly states "All testing was performed on a final, finished device manufactured with the proposed modification." It does not specify a quantitative sample size (e.g., N units). This is typical for engineering verification and validation of medical devices where tests are performed on a representative sample of finished products to ensure design specifications are met.
- Data Provenance: The testing was performed internally by the manufacturer (DeRoyal Industries, Inc.) or by contracted labs following specified standards (e.g., ISO, IEC). No information about "country of origin of the data" or "retrospective/prospective" studies in the clinical sense is provided as it's not a clinical trial of a diagnostic algorithm.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not Applicable. This is a hardware device; "ground truth" in the AI/ML sense (e.g., expert labels on images) is not established. Device performance is measured against established engineering standards (e.g., temperature accuracy to ± 0.2°C, electrical safety limits). The "ground truth" is typically defined by metrological standards and calibrated reference instruments.
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Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- Not Applicable. Adjudication methods are relevant for subjective interpretations (like medical image reading). Hardware device testing involves objective measurements against predefined specifications.
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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 is a hardware medical device, not an AI/ML diagnostic system. No human reader studies or AI assistance are involved.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Not Applicable. There is no algorithm. The device's performance is inherently "standalone" in that its physical functions (temperature sensing, sound transmission) are tested directly.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Ground Truth: For temperature accuracy, the "ground truth" would be established by measurements using a highly accurate, calibrated reference thermometer in a controlled environment (e.g., a water bath at a known temperature). For electrical safety, the "ground truth" refers to compliance with the limits set by standards like IEC 60601-1. For acoustic responsiveness, it would be measured against expected sound transmission properties. This is about physical properties and engineering standards, not medical diagnoses.
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The sample size for the training set:
- Not Applicable. There is no AI/ML model, hence no training data or training set.
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How the ground truth for the training set was established:
- Not Applicable. As there is no training set.
§ 868.1920 Esophageal stethoscope with electrical conductors.
(a)
Identification. An esophageal stethoscope with electrical conductors is a device that is inserted into the esophagus to listen to a patient's heart and breath sounds and to monitor electrophysiological signals. The device may also incorporate a thermistor for temperature measurement.(b)
Classification. Class II (performance standards).