Integrated Ultrasound Device for Precision Bladder Volume Monitoring via Acoustic Focusing and Machine Learning.

Abstract

Bladder volume monitoring is critical for managing lower urinary tract dysfunctions, yet existing methods remain invasive or operator-dependent and are unsuitable for continuous use. Here, we present a conformable wearable ultrasound system that combines lens-assisted acoustic focusing with machine-learning regression to enable non-invasive bladder volume estimation, while providing a clear path toward future real-time implementation. A flexible PZT array integrated with a concave acoustic lens enhances lateral energy concentration and depth selectivity, while a Random Forest model was used to map echo-derived features to bladder volume estimates. In a pilot study, bladder-volume estimates generated offline after data collection showed good agreement with a benchtop electrical impedance-based measurement system, supporting the feasibility of non-invasive bladder volume estimation. The device was operated using conservative low-voltage, low-duty-cycle excitation settings designed to minimize acoustic exposure and be consistent with diagnostic-ultrasound safety guidance, and biocompatible, flexible encapsulation is designed to support extended wear. Together with compact packaging and low-power wireless transmission, these attributes support ambulatory, longitudinal bladder monitoring and offer design insights for future wearable ultrasound systems targeting precise and ultimately continuous physiological monitoring.

AI evidence extraction

Main findings

A conformable wearable ultrasound system using lens-assisted acoustic focusing and a Random Forest regression model produced offline bladder-volume estimates that showed good agreement with a benchtop electrical impedance-based measurement system in a pilot study. The device was operated with conservative low-voltage, low-duty-cycle excitation settings intended to minimize acoustic exposure and align with diagnostic-ultrasound safety guidance.

Outcomes measured

• Bladder volume estimation accuracy/agreement
• Feasibility of non-invasive bladder volume monitoring
• Acoustic exposure minimization (low-voltage, low-duty-cycle settings)

Limitations

• Pilot study; sample size not reported in abstract
• Bladder-volume estimates were generated offline after data collection (not real-time)
• No ultrasound frequency or quantitative exposure metrics reported

{
    "study_type": "engineering",
    "exposure": {
        "band": null,
        "source": "wearable ultrasound device",
        "frequency_mhz": null,
        "sar_wkg": null,
        "duration": null
    },
    "population": null,
    "sample_size": null,
    "outcomes": [
        "Bladder volume estimation accuracy/agreement",
        "Feasibility of non-invasive bladder volume monitoring",
        "Acoustic exposure minimization (low-voltage, low-duty-cycle settings)"
    ],
    "main_findings": "A conformable wearable ultrasound system using lens-assisted acoustic focusing and a Random Forest regression model produced offline bladder-volume estimates that showed good agreement with a benchtop electrical impedance-based measurement system in a pilot study. The device was operated with conservative low-voltage, low-duty-cycle excitation settings intended to minimize acoustic exposure and align with diagnostic-ultrasound safety guidance.",
    "effect_direction": "unclear",
    "limitations": [
        "Pilot study; sample size not reported in abstract",
        "Bladder-volume estimates were generated offline after data collection (not real-time)",
        "No ultrasound frequency or quantitative exposure metrics reported"
    ],
    "evidence_strength": "insufficient",
    "confidence": 0.66000000000000003108624468950438313186168670654296875,
    "peer_reviewed_likely": "yes",
    "keywords": [
        "wearable ultrasound",
        "bladder volume monitoring",
        "acoustic focusing",
        "concave acoustic lens",
        "flexible PZT array",
        "machine learning",
        "Random Forest",
        "non-invasive monitoring",
        "wireless transmission"
    ],
    "suggested_hubs": []
}

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