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The Smart Wireless Stethoscope enables amplification, filtering, and transmission of auscultation sounds from the heart, lungs, bowel, arteries, and veins. A medical professional at one location on network can listen to the auscultation sounds of a patient on site or at a different location on the network. The device is intended for use on pediatric and adult patients. The device is intended to be used by professional users in a clinical environment or by lay users in a nonclinical environment. The device is not intended for self-diagnosis.

The proposed smart wireless stethoscope is made of a Bluetooth® stethoscope and a companion medical mobile application that runs on a smartphone or a tablet. The stethoscope picks human body sounds with an acoustic structure that is similar to the chest piece of a traditional stethoscope. Then the sounds are converted to electrical audio signals by a microphone in the device. The electrical audio signals are further processed and transmitted through Bluetooth® protocol. The device has three models STEMO300, STEMO500 and STEMO700. All the three models can work with the companion app running on a smartphone or tablet. When they work this way, the stethoscope transmits audio to the app and the sounds can be further processed in the app. The app provides functions such as amplification, filtering, recording, sharing, etc. STEMO500 and STEMO700 have an ambient noise cancelling option while STEMO300 not. Unlike STEMO300 or STEMO500, STEMO700 has an option to directly transmit audio to paired Bluetooth earphones.

The provided text is a 510(k) summary for the "Smart Wireless Stethoscope (Model: STEMO300, STEMO500, STEMO700)". This document details the device's characteristics and compares it to a predicate device to demonstrate substantial equivalence, a regulatory pathway for medical devices in the US.

However, it explicitly states:

"No clinical test is submitted in this 510(k)."

This means that the document does not contain information about acceptance criteria or a study proving the device meets those criteria through clinical performance. The FDA clearance for this device was based on demonstrating substantial equivalence to a legally marketed predicate device, rather than on new clinical effectiveness studies.

Therefore, I cannot provide the requested information regarding:

• A table of acceptance criteria and reported device performance.
• Sample sizes used for the test set or training set.
• Data provenance for the test set or training set.
• Number of experts or their qualifications for ground truth establishment.
• Adjudication methods.
• MRMC comparative effectiveness study or its effect size.
• Standalone performance study.
• Type of ground truth used.
• How ground truth for the training set was established.

The document focuses on:

• Biocompatibility Testing: Conducted according to ISO10993-1, including Cytotoxicity, Sensitization, and Irritation tests.
• Electrical safety and electromagnetic compatibility (EMC): Compliance with IEC 60601-1, IEC 60601-1-11, IEC 60601-1-2, ANSI IEEE C63.27-2017, and AAMI TIR69:2017/(R2020).
• Software Verification and Validation Testing: Conducted according to FDA guidance for software in medical devices.

These tests are primarily focused on safety and technical performance standards, not on clinical performance against specific acceptance criteria for diagnostic accuracy or effectiveness in a clinical setting.

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Accutension Smartphone Auscultatory Blood Pressure Kit is intended for professionals or home users to nonautomatically measure systolic and diastolic blood pressure on adults by detecting Korotkoff sounds and measure pulse rate on adults by detecting oscillometry. This device is not indicated for children, heart failure patients and critical patients.

The Accutension Smartphone Auscultatory Blood Pressure Kit (Model XYZ-110) is a non-invasive blood pressure measurement system for professionals and home users to nonautomatically measure systolic and diastolic blood pressure and pulse rate. It utilizes advanced pressure sensing module to transfer cuff pressure value to an iOS App via established Bluetooth connection between the module and the iOS device during measurement, meanwhile a stethoscope detects Korotkoff sounds and transfers the sound signal to the smartphone via its earphone jack. Both the cuff pressure and auscultatory sounds are visualized in the app and a user can determine the systolic and diastolic blood pressure by finding the cuff pressures on the first and last Korotkoff sounds. It automatically calculates the pulse rate based on cuff pressure oscillometry. This device consists of 5 parts, arm cuff, pressure sensing module, hand pump (bulb) with airflow valve, stethoscope with earphone plug and charging cable.

This document describes the 510(k) premarket notification for the Accutension Smartphone Auscultatory Blood Pressure Kit (Model XYZ-110). It demonstrates the device's substantial equivalence to predicate devices for blood pressure and pulse rate measurement. The core of the evidence relies on clinical and non-clinical testing, particularly adherence to ISO 81060-2:2013 for blood pressure accuracy.

Here's a breakdown of the requested information based on the provided text:

1. A table of acceptance criteria and the reported device performance

The document states that the device's accuracy was assessed against the criteria specified in ISO 81060-2:2013. While specific numerical acceptance criteria (e.g., mean difference, standard deviation) for blood pressure are not explicitly tabulated in the provided text, the conclusion section confirms that the device "satisfies the criteria specified in ISO 81060-2:2013" and is "as safe and effective (accurate) in a clinical environment".

For pulse rate, the document states: "Testing to demonstrate pulse rate accuracy" was performed, and the Conclusion claims the device demonstrates "the same level of safety, effectiveness and performance" as predicate devices. However, explicit numerical acceptance criteria for pulse rate accuracy are not provided.

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

• Sample Size for Clinical Test (Blood Pressure Accuracy): "255 pairs of data from 85 valid subjects were achieved following the clinical study protocol defined in ISO 81060-2:2013."
• Data Provenance: The text does not explicitly state the country of origin of the data. Since the submitting company is Shanghai Hulu Devices Co., Ltd, it is highly probable the data originated from China. The study is described as a "clinical study," which implies a prospective collection of data.

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

The document mentions that a "manual Mercury Sphygmomanometer was used as a reference device in the clinical testing." For the primary blood pressure determination, the device relies on a human user "listening to the Korotkoff sounds with human ear." However, the text does not specify the number of expert readers, their qualifications (e.g., radiologists, physicians with X years of experience), or if they were involved in establishing the "ground truth" beyond performing the reference measurements with the mercury sphygmomanometer. The ISO 81060-2 standard usually details requirements for reference measurements and observers.

4. Adjudication method for the test set

The document describes a "Same arm simultaneous method" used during the clinical validation, with a manual Mercury Sphygmomanometer as a reference. This usually implies a direct comparison, rather than an adjudication process between multiple readers. No mention of an adjudication method (e.g., 2+1, 3+1) is made for determining the ground truth for the 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

There is no mention of a multi-reader, multi-case (MRMC) comparative effectiveness study. The device is not an AI-assisted diagnostic tool in the typical sense where AI provides an interpretation and human readers improve with its assistance. Instead, it digitizes and visualizes blood pressure and Korotkoff sounds for a human user to interpret. The study focuses on the accuracy of the device itself compared to a reference standard (manual mercury sphygmomanometer).

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

No, the device is explicitly designed for human-in-the-loop performance. The core method for blood pressure determination is "By listening to the Korotkoff sounds with human ear". The device visualizes the sounds and cuff pressure, which "provides an extra feature to help capture the first and last Korotkoff sounds for blood pressure determination." Therefore, no standalone algorithm-only performance was conducted or is applicable given the device's function. The pulse rate is "automatically calculates based on cuff pressure oscillometry," which is a standalone algorithm component for that specific measurement.

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

The ground truth for blood pressure measurement was established using a "manual Mercury Sphygmomanometer" as a reference device in the clinical testing, following the protocol of ISO 81060-2:2013. This implies a direct, real-time comparison to a well-accepted clinical standard performed by human observers.

8. The sample size for the training set

The document does not mention a "training set" in the context of machine learning or AI models. Given that the device's primary method for blood pressure determination relies on human auscultation supplemented by visualization, and pulse rate is calculated via oscillometry (a known physiological method), there isn't an explicit "training set" for an AI algorithm for these core functions. The term "training set" is usually relevant for AI/ML devices that learn from data, which doesn't appear to be the case for the primary blood pressure determination function here.

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

As there is no explicit mention of a "training set" for a machine learning model for blood pressure determination, this question is not applicable to the provided information.

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