Search Results

Measure blood pressure (systolic and diastolic) and pulse rate.

UB-1100BLE UltraConnect digital blood pressure monitor has the same design as the predicate devices with an inflatable cuff which is wrapped around the patient's wrist. After the user starts the blood pressure measurement, the cuff is inflated automatically by an internal pump. The systolic and diastolic blood pressures are also determined by oscillometric method. The deflation rate is controlled by an internal exhaust valve. There is a quick exhaust mechanism so that the cuff pressure can be completely released immediately. There is a maximum pressure safety setting at 299 mmHg. UB-1100BLE UltraConnect digital blood pressure monitor will not inflate the cuff higher than 299 mmHg. UB-1100BLE will turn on an irregular heartbeat indicator if an irregular heartbeat was detected during the measurement process. At the end of the measurement, the systolic and diastolic pressures with pulse rate are shown on the OLED and transmitted via Bluetooth wireless module to the connected app. The cuff is also deflated automatically to 0 mmHg at the same time. If the monitor receives no further action from the user for 1 minute, it will automatically turn off by itself.

This document is a 510(k) premarket notification for a blood pressure monitor, not an AI/ML medical device. Therefore, much of the requested information regarding AI acceptance criteria, ground truth, expert adjudication, and MRMC studies is not applicable.

However, I can extract information related to the device's performance and the general testing methodology for this type of medical device as presented in the document.

Acceptance Criteria and Device Performance (based on the provided specifications for a blood pressure monitor):

The acceptance criteria for a non-invasive blood pressure measurement system like the UB-1100BLE are typically defined by recognized standards such as AAMI/ANSI/IEC 80601-2-30.

Study Proving Device Meets Acceptance Criteria:

The document states: "There is no modification requiring conducting clinical tests." This indicates that the device's performance was not proven through a new clinical study. Instead, substantial equivalence was demonstrated based on its similarities to previously cleared predicate devices and adherence to relevant standards.

Detailed Breakdown of Information as applicable to this medical device submission:

1. A table of acceptance criteria and the reported device performance:
   (See table above)

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

   • Sample Size: Not applicable as no new clinical test set was used to prove the device's performance. The submission relies on the established performance of its predicate devices and non-clinical testing for equivalence.
   • Data Provenance: Not applicable for a new clinical test set. The predicate devices' performance data would have originated from their respective clinical trials and validation studies, the details of which are not in this document. The current submission focuses on non-clinical tests (e.g., electrical safety, EMC, reliability) to demonstrate that the modifications did not alter the fundamental performance.

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):
   Not applicable, as this is a blood pressure monitor and not an AI/ML diagnostic imaging device that requires expert human interpretation for ground truth.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
   Not applicable, as there was no test set requiring adjudication in the context of a new clinical study.

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:
   Not applicable. This device is a standalone blood pressure monitor and does not involve AI assistance for human readers/clinicians, nor does it conduct diagnostic tasks in the way an imaging AI would.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   The device itself operates as a standalone blood pressure measurement system (algorithm only for generating BP readings) in the sense that it provides measurements directly. However, the context of this question usually refers to AI algorithms performing a diagnostic task. In that sense, it's not directly comparable. The performance (accuracy of BP and pulse readings) is what defines its standalone performance, and that's assessed against physical standards and other measurement devices rather than human interpretation.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
   For blood pressure monitors, the "ground truth" is typically established by comparative measurements against a reference standard or invasively measured blood pressure, or through clinical validation according to protocols like those outlined in the AAMI/ANSI/IEC 80601-2-30 standard. It's not based on expert consensus or pathology in the typical sense of AI/ML diagnostic tools.

8. The sample size for the training set:
   Not applicable. This device does not use machine learning in a way that requires a "training set" in the context of AI/ML model development. It uses an oscillometric method, which is a classical signal processing technique.

9. How the ground truth for the training set was established:
   Not applicable, as there is no training set for an AI/ML model.

Ask a specific question about this device