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hinscope is intended for spot-check vital signs measurement of adult patients in professional healthcare facilities, such as hospitals or skilled nursing facilities. It is intended to be used by trained healthcare professionals.

hinscope is intended for spot-check measurements, in adults, of:

• Non-Invasive blood pressure (NIBP) -
• -Pulse rate (PR)
• -Oxygen saturation (SpO2)

hinscope is not intended for use in high-acuity environments, such as intensive care units (ICU) or operating rooms. hinscope is not intended for use on acutely ill cardiac patients with the potential to develop life-threatening arrhythmias e.g. very fast atrial fibrillation. These patients should be monitored using a device with continuous electrocardiogram (ECG). hinscope is not a substitute for an ECG monitor.

hinscope is not intended for SpO2 and PR measurements in conditions of high motion or low perfusion.

hinscope consists of the:

• hinscope measurement unit
• hinscope mobile application

hinscope consists of a vital sign measurement unit, that is positioned on the patient's upper arm, and a mobile application with a user interface which allows display of the vital sign data. hinscope is a combination of optical sensors and a sphygmomanometer cuff which provide measurements of the patient's pulse rate (PR) oxygen saturation (SpO2), and non-invasive blood pressure (NIBP).

PR and SpO2 are measured using a photoplethysmography (PPG) optical measurement method. PPG is a noninvasive technology that uses light sources (green, red and infrared) and photodetectors at the surface of skin to measure the volumetric variations of blood circulation. Blood pressured with the sphygmomanometer using an algorithm in which the cuff is slowly inflated while simultaneously sensing oscillations are no longer detected, the cuff deflates rapidly, and the signals are processed to determine systolic and diastolic pressures.

The vital sign measurement device communicates with a mobile application via wireless Bluetooth Low Energy (BLE) communication. The hinlab mobile application displays the blood pressure, SpO2 and pulse rate measurements.

Here's a breakdown of the acceptance criteria and study details for the Hinscope device, based on the provided FDA 510(k) summary:

Acceptance Criteria and Reported Device Performance

Study Details

1. Sample size used for the test set and the data provenance:

   • SpO2 and Pulse Rate Study: 13 healthy adult subjects with varying BMIs and diverse skin tones. The provenance (e.g., country) of the data is not specified. The study appears to be prospective given it investigated accuracy in subjects.
   • NIBP Validation Study: 85 subjects (41 males, 44 females) with an arm circumference of 22-32 cm. The provenance of the data is not specified. The study appears to be prospective as it was a validation study with subjects.

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

   • The document does not mention the number of experts or their qualifications for establishing ground truth for either the SpO2/PR or NIBP studies. These studies rely on established reference methods (e.g., arterial blood gas measurements for SpO2, or a validated reference NIBP device) rather than expert consensus on images or interpretations.

3. Adjudication method for the test set:

   • Adjudication methods (like 2+1 or 3+1) are typically used for studies involving human interpretation of medical images or data where expert consensus is needed to establish ground truth. Since these are performance accuracy studies for vital sign measurements, such adjudication methods are not applicable and are not mentioned.

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:

   • No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This type of study is relevant for AI-assisted diagnostic devices where human readers interpret medical images. The Hinscope device measures vital signs directly and is not an AI-assisted diagnostic imaging device.

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

   • Yes, these were standalone performance studies of the Hinscope device (algorithm and hardware combined) to measure vital signs. The results reflect the device's inherent accuracy in measuring SpO2, Pulse Rate, and NIBP, independent of human interpretation or assistance for those specific measurements. The device is used by trained healthcare professionals, but the performance metrics described relate to the device's direct measurement capabilities.

6. The type of ground truth used:

   • SpO2 and Pulse Rate: The ground truth for SpO2 accuracy typically involves comparison with fractional oxyhemoglobin (SaO2) determined from co-oximetry of arterial blood samples. Given the reference to ISO 80601-2-61, this highly accurate method is implied. For pulse rate, ground truth is often established using a reference ECG or arterial line.
   • NIBP: The ground truth for NIBP accuracy typically involves comparison with invasive arterial blood pressure measurements or a highly accurate reference sphygmomanometer following a standardized protocol like ISO 81060-2.

7. The sample size for the training set:

   • The document does not specify a sample size for a training set. This is common for devices that use established physiological measurement principles (like PPG for SpO2/PR or oscillometry for NIBP) rather than complex machine learning models that require extensive training data. While there might be internal algorithms or parameters that were developed and "trained," this document focuses on the validation studies and does not disclose details of hypothetical training data for these core physiological measurement methods.

8. How the ground truth for the training set was established:

   • As no training set details are provided or implied for a machine learning model, information on how its ground truth was established is not available in these documents. The Hinscope's measurements rely on well-established physical principles rather than a learning-based model that would require a separate ground-truth-labeled training set as is typical for AI/ML-based diagnostic software.

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