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Fully Automatic Electronic Blood Pressure Monitor is for use by medical professionals or at home and is a non-invasive blood pressure measurement system intended to measure the diastolic and systolic blood pressures and pulse rate of an adult individual by using a non-invasive technique in which an inflatable cuff is wrapped around the upper arm. The cuff circumference is limited to 15cm-48cm (approx. 5.9"-18.9").

Fully Automatic Electronic Blood Pressure Monitor (BP-300C, BP-300CV, BP-300V, BPM1, BPX1, KD-338N, KD-553, KD-557BR, KD-558, KD-558BR, KD-595, KD-5031N, KD-5810, KD-5810B, KD-5811, KD-5811A, KD-5811V, KD-5815, KD-5920, KD-5920L, KD-5920TL, KD-5923, KN-550LT) is designed and manufactured according to IEC 80601-2-30.

The operational principle is based on Oscillo-metric and silicon integrates pressure sensor technology. It can calculate the systolic and diastolic blood pressure and display the result. The measurements results can also be classified by the function of blood pressure classification indicator. If any irregular heartbeat is detected, it can be shown to the user.

The provided document is a 510(k) clearance letter for various blood pressure monitors. It outlines the regulatory approval process and compares the new devices to a predicate device. However, it does not contain the detailed acceptance criteria and study results in the format typically used for AI/software devices.

Specifically, this document describes validation against standards for medical electrical equipment (IEC 60601-1, IEC 60601-1-2, IEC 60601-1-11) and automated non-invasive sphygmomanometers (IEC 80601-2-30, ISO 81060-2). It focuses on the substantial equivalence of the physical blood pressure monitors and their underlying oscillometric and pressure sensor technology, rather than the performance of an AI algorithm based on a test set, ground truth, and expert interpretations.

Therefore, many of the specific questions about AI/software device validation (e.g., sample size for the test set, data provenance, number of experts for ground truth, MRMC studies, standalone performance, training set details) cannot be answered from this document.

However, I can extract information related to the performance of the blood pressure monitors themselves, based on the included standards.

Acceptance Criteria and Device Performance (for Blood Pressure Monitor functionality, not AI):

Since this is a blood pressure monitor, the primary performance criteria relate to its accuracy in measuring blood pressure and pulse rate, and compliance with relevant safety and performance standards for automated non-invasive sphygmomanometers.

• IEC 60601-1:2005+AMD1: 2012+AMD2: 2020
• IEC 60601-1-2:2014+AMD1: 2020
• IEC 60601-1-11: 2015+AMD1: 2020
• IEC 80601-2-30: 2018 | All listed standards were met, demonstrating basic safety, essential performance, EMC, and home healthcare environment compliance. |

Unable to Answer from Document (Common for AI/Software Device Submissions, but not for this type of device):

The following questions are not applicable or cannot be answered from this 510(k) summary because the device described is a physical blood pressure monitor, not an AI/software device that interprets medical images or other complex data requiring expert adjudication, training sets, or MRMC studies.

• Sample size used for the test set and the data provenance:
  • Test Set Size: "A total of 231 patients (107 males and 124 females) were enrolled in the study." This is the clinical study population for blood pressure measurement accuracy.
  • Data Provenance: Not explicitly stated (e.g., country of origin). The study is described as a "clinical study," which implies prospective data collection for the purpose of the study.
• 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):
  • Not applicable. Ground truth for blood pressure measurement is established through a standard auscultation method (manual measurement by medical professionals using a stethoscope and sphygmomanometer), not by interpretation of images by experts.
• Adjudication method (e.g. 2+1, 3+1, none) for the test set:
  • Not applicable. Ground truth is direct measurement by a reference method.
• 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 not an AI-assisted diagnostic device.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
  • The device is a standalone blood pressure monitor. No human-in-the-loop interaction for interpretation (as in AI devices) is relevant. Its performance is its direct measurement accuracy.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
  • Ground Truth: "Standard auscultation method was used as the reference blood pressure monitor measuring." This is the established clinical standard for direct comparison.
• The sample size for the training set:
  • Not applicable. This is not an AI/machine learning device requiring a training set.
• How the ground truth for the training set was established:
  • Not applicable.

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The BPM1 (Electronic Sphygmomanometer) is intended for use in a professional setting or at home and is a non-invasive blood pressure measurement system. It is designed to measure the systolic blood pressures and pulse rate of an adult individual by using a technique in which an inflatable cuff is wrapped around the upper arm. The measurement range of the cuff circumference is 8.6" to 18.9" (22cm-48cm)

iHealth BPM1 Wireless Blood Pressure Monitor is designed and manufactured according to IEC 80601-2-30. The operational principle is based on oscillometric and silicon integrates pressure sensor technology. It can calculate the systolic and diastolic blood pressure, the measurements results can also be classified by the function of blood pressure classification indicator. If any irreqular heartbeat is detected, it can be shown to the user. The new devices can connect to iOS or Andriod devices to show the results.

The provided text describes the iHealth Wireless Blood Pressure Monitor (BPM1) and its submission for 510(k) clearance. The document focuses on demonstrating substantial equivalence to predicate devices rather than providing a detailed study of its performance against specific acceptance criteria.

However, based on the information provided, we can infer some aspects relevant to acceptance criteria and the "study" that proves the device meets them, largely through adherence to international standards and comparison to predicate devices.

Acceptance Criteria and Reported Device Performance

The document asserts that performance is "Similar" to predicate devices, which would have met these accuracy standards. |
| Electrical Safety | Adherence to IEC 60601-1 standards. | "Electrical safety according test to IEC 60601-1" was done. And "Electrical safety" is "Identical" to predicate devices. |
| Electromagnetic Compatibility (EMC) | Adherence to IEC 60601-1-2 standards. | "Electromagnetic compatibility test according to IEC 60601-1-2" was done. And "EMC" is "Identical" to predicate devices. |
| Mechanical Safety | Not explicitly detailed, but inferred from general safety and performance standards. | "Mechanical safety" is "Identical" to predicate devices. |
| Biocompatibility | Device materials that contact patients are biocompatible. | "Patients contact Materials" are "Similar" to predicate devices, and "Biocompatibility" is "Identical" to predicate devices. |
| Functionality | Ability to measure systolic and diastolic blood pressure, pulse rate, display results, and classify blood pressure. | "The operational principle is based on oscillometric and silicon integrates pressure sensor technology. It can calculate the systolic and diastolic blood pressure, the measurements results can also be classified by the function of blood pressure classification indicator. If any irregular heartbeat is detected, it can be shown to the user." "Function" is "Similar" to predicate devices. |
| Connectivity | Ability to connect to iOS or Android devices for data transmission. | "The new devices can connect to iOS or Andriod devices to show the results." and "More over, the new device BPM1 can transmitting data to a mobile device via WiFi." |

Study Details:

The document describes pre-market notification (510(k)) and focuses on demonstrating substantial equivalence rather than a new standalone clinical study for accuracy.

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

   • The document states: "Clinical data have been transferred from the predicates."
   • This implies that no new clinical test set was used for the BPM1 specifically for primary accuracy evaluation. Instead, the clinical data and performance of the predicate devices (Andon Health Co., Ltd. KD-927, K141984, KD-936, K120672, and KD-931, K102939) are relied upon.
   • The provenance of the original clinical data for the predicate devices is not specified (e.g., country of origin, retrospective or prospective).

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

   • Since clinical data were "transferred from the predicates," this information would pertain to the original studies for the predicate devices. This detail is not provided in the current document. Typically, accuracy studies for blood pressure monitors involve simultaneous measurements by trained observers (experts) using a reference method (e.g., mercury sphygmomanometer) and the device under test.

3. Adjudication method for the test set:

   • As clinical data were transferred, the adjudication method (if any) used for the predicate device studies is not described in this document. Standard blood pressure monitor accuracy studies often involve multiple observers performing auscultation and comparing their readings to ensure consensus or inter-observer reliability in establishing ground truth.

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, an MRMC comparative effectiveness study was not done. This device is a blood pressure monitor, which does not involve "human readers" interpreting images or data with or without "AI assistance" in the typical sense of MRMC studies. Its function is to provide direct measurements.

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

   • Yes, in essence, standalone performance was evaluated by adherence to IEC 80601-2-30. This standard specifically defines requirements for automated noninvasive sphygmomanometers, meaning the device's accuracy in measuring blood pressure is assessed independently of human interpretation of the measurement process itself. The device operates automatically to produce a reading.
   • The document mentions "Non-clinical Tests have been done as follows: ... c. Safety and performance characteristics of the test according to IEC 80601-2-30". This demonstrates standalone performance against established international standards.

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

   • For blood pressure monitors, the ground truth is typically established by simultaneous measurements by trained human observers using a validated reference method, such as a mercury sphygmomanometer, often following protocols like those outlined by the Association for the Advancement of Medical Instrumentation (AAMI) or the British Hypertension Society (BHS). This would be a form of expert reference measurement. As clinical data were transferred, this ground truth method would have been used for the predicate devices.

7. The sample size for the training set:

   • The document implies that the device's development and validation primarily relied on its similarity to predicate devices and adherence to standards. It does not explicitly mention a "training set" in the context of machine learning (AI) for this specific device's accuracy. The device uses an oscillometric principle, which is a well-established algorithm. Therefore, there isn't a "training set" in the typical AI sense to teach the device to measure blood pressure. The underlying algorithms are developed and refined based on extensive physiological and engineering data, but not a "training set" as one might see for image classification AI.

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

   • As there isn't a "training set" described for this device in the AI sense, this question is not directly applicable. The "ground truth" for the oscillometric method's development (which is the core of the device's function) would have been established through extensive research comparing oscillometric waveforms to directly measured intra-arterial pressures or auscultation by experts.

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