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510(k) Data Aggregation
(244 days)
The Bladder Scanner (model: PadScan Z3, PadScan Z3, PadScan Z5) is B-mode pulsed-echo ultrasound device. It intended as a handheld battery-operated device. The Bladder Scanner projects ultrasound energy through the lower abdomen of the patient to obtain images of the bladder which is used to calculate bladder Volume noninvasively. The Bladder Scanner is intended to be used only by qualified medical professionals.
The PadScan Series manufactured by AvantSonic Technology Co., Ltd. provides non-invasive volume bladder measurement utilizing real-time ultrasound imaging and measurement. The equipment consists of the main unit, 3D probe, battery and adapter. It features: Two Operation Modes: Expert Mode and Easy Mode, Non-invasive, comfortable, correct, reliable, fast and simple operation, Printouts with ultrasound images and various parameters through PC software, Touch screen keyboard operation, Urine volume setting and alarm setting, Multi-language selection, Combined power supply with AC adapter and a built-in battery. The difference between these models is the size of the LCD screen and enclosure structure. PadScan DS3 is provided 7-inch LCD screen. PadScan Z3 is provided 7-inch LCD screen and LCD screen stand. PadScan Z5 is provided 8-inch LCD screen and LCD screen which has a handle.
Here's a breakdown of the acceptance criteria and study information for the AvantSonic Bladder Scanner, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
Parameter | Acceptance Criteria (Predicate Device K131227) | Reported Device Performance (PadScan DS3, Z3, Z5) |
---|---|---|
Volume Measurement Accuracy | ±15%, ±15ml | ±10%, ±10ml |
2. Sample Size Used for the Test Set and Data Provenance
The document explicitly states: "Clinical testing is not required." This indicates that no clinical test set was used for the substantial equivalence determination. The performance criteria were based on comparisons to the predicate device's specifications.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
Not applicable, as no clinical testing with a test set requiring expert ground truth was conducted. Performance was based on device specifications and non-clinical testing.
4. Adjudication Method for the Test Set
Not applicable, as no clinical testing with a test set requiring adjudication was conducted.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No MRMC comparative effectiveness study was mentioned or performed. The submission relies on demonstrating substantial equivalence to a predicate device through non-clinical testing and shared intended use/technological characteristics.
6. Standalone (Algorithm Only) Performance Study
While the device calculates bladder volume non-invasively, the provided information does not detail a standalone algorithm performance study. The performance is assessed based on the device's ability to measure bladder volume within specified accuracy against a reference.
7. Type of Ground Truth Used
For the reported device performance regarding volume measurement accuracy, the ground truth would have been established through a controlled measurement method (e.g., using known volumes of liquid in a phantom or other controlled environment) to calibrate and verify the device's calculations. This is implied by the accuracy specification but not explicitly described as "pathology" or "outcomes data."
8. Sample Size for the Training Set
Not applicable, as this is a medical device submission based on substantial equivalence, not a machine learning model requiring a training set in the typical sense. The device's "training" is inherent in its design and calibration processes to meet the specified accuracy.
9. How the Ground Truth for the Training Set Was Established
Not applicable. See point 8. The device's volume calculation mechanism is based on ultrasound physics and established algorithms for bladder volume estimation, not trained on a distinct dataset with "ground truth" labels in the context of machine learning. The accuracy is verified through metrological testing.
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