Bladder AI

by Exo  · Based in United States → — AI-Powered Bladder Volume Assessment for Point-of-Care Ultrasound
Emergency Medicine Radiology Urology
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Not explicitly detailed for bladder ai software; the exo iris handheld ultrasound device, on which it runs, starts at $4,999.
Regulatory Status Disclosed

Overview

Bladder AI by Exo is a standalone Software as a Medical Device (SaMD) designed to assist healthcare professionals in evaluating and quantifying bladder volume from point-of-care ultrasound (POCUS) images. Operating on the handheld Exo Iris ultrasound system, the software utilizes machine learning algorithms to simplify the diagnostic process for patients aged two years and older.

The tool is designed for clinicians across various clinical environments, including emergency medicine, acute care, urology, and primary care settings. It is particularly useful for evaluating patients presenting with symptoms of urinary retention, incontinence, or suspected bladder dysfunction.

Bladder AI integrates into point-of-care workflows through the following capabilities:

  • Landmark Placement: Employs machine learning to provide semi-automatic landmark placements on ultrasound frames.
  • Volume Measurement: Calculates bladder dimensions and volume automatically within seconds, reducing manual calculation steps.
  • Workflow Integration: Supports both single-frame and multi-frame DICOM images directly on the portable device.
  • Report Generation: Assists clinicians in creating and finalizing examination reports at the bedside.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Image-based assessment of bladder ultrasound images
  • Quantification of bladder volume
  • Machine learning (ML) based semi-automatic landmark placements
  • Bladder dimension measurements
  • Report generation capabilities
  • Simplifies workflow for trained healthcare providers
  • Aids in evaluating bladder ultrasound images
  • FDA-cleared for medical use
  • Runs on Exo Iris handheld ultrasound device
  • Input from DICOM images from ultrasound scanners

Use Cases

  • Assessing urinary retention
  • Evaluating overflow or stress incontinence
  • General bladder volume assessment in patients aged 2 or older
  • Aiding in diagnosis of bladder-related conditions at the point of care
  • Streamlining documentation and reporting for bladder ultrasounds

What Physicians Need to Know

DICOM Support & Standards
While not always explicitly detailed for every tool, AI solutions for bladder cancer detection and segmentation on CT/MRI inherently require DICOM compatibility for image input and output. For ultrasound-based bladder volume measurements, the derived data or images may also be integrated via DICOM or proprietary formats.
PACS Integration Method
Specific integration methods are not consistently provided. However, AI tools in radiology typically integrate with PACS either directly, through a vendor-neutral archive (VNA), or via dedicated AI orchestration platforms to facilitate image transfer and results reporting. For handheld ultrasound devices with integrated AI, data often syncs through proprietary apps or cloud platforms.
Reading Room Workflow Impact
Significantly enhances efficiency by automating tasks like bladder volume measurement in seconds, reducing manual effort and potential for human error. For cancer detection, AI can improve diagnostic accuracy, highlight subtle lesions, and streamline segmentation, thereby reducing radiologist workload and inter-reader variability.
AI Model Architecture (deep learning approach)
Commonly employs deep learning techniques, including Convolutional Neural Networks (CNNs) and U-Net based models. For bladder volume, two U-net-based deep learning models are used for landmark detection. For bladder cancer detection and segmentation, models like U-Net, PSPnet, and MAnet are utilized to automatically learn and extract features from images.
Processing Speed (per study)
For bladder volume measurement, AI-powered tools can provide automatic measurements in seconds.
FDA Clearance Pathway (510k/De Novo)
At least one bladder AI tool, Clarius Bladder AI, has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for automated bladder volume measurements.
Supported Modalities
Primarily supports Ultrasound (US) for bladder volume measurement. For bladder cancer detection, diagnosis, and segmentation, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are supported.
Sensitivity & Specificity Data
Reported accuracy for bladder volume measurement is u00b1 7.5% on volumes greater than 100 mL (or u00b1 7.5 mL on volumes less than 100 mL). For bladder cancer detection on CT urography, a CNN model showed a sensitivity of 0.83, specificity of 0.76, and a negative predictive value (NPV) of 0.97. AI-assisted cystoscopy has demonstrated a sensitivity of 91% and specificity of 99% for tumor detection. An AI model for bladder cancer diagnosis on CT imaging achieved a test-set accuracy exceeding 0.90.
Physician Tip

Bladder AI tools offer significant advantages by automating routine measurements and assisting in complex diagnostic tasks. For bladder volume, these tools provide rapid, consistent, and non-invasive assessments, potentially reducing unnecessary catheterizations and improving patient comfort. For cancer detection, AI can act as a 'second reader' to highlight subtle lesions, improve segmentation accuracy, and reduce inter-observer variability, thereby enhancing diagnostic precision and supporting earlier intervention. Physicians should integrate these tools as adjuncts to their expertise, understanding their specific performance metrics, limitations, and the importance of clinical correlation. Real-time feedback and editable outputs (e.g., caliper placements in volume measurement) allow for physician oversight and control.

Effective integration of Bladder AI tools into existing radiology and clinical workflows is crucial for maximizing their benefits. This typically involves seamless connectivity with PACS for image acquisition and results reporting, often leveraging DICOM standards. For point-of-care ultrasound devices, integration may involve secure cloud-based platforms for data management and access. Consideration should be given to the ease of data transfer, compatibility with existing IT infrastructure, and the ability to incorporate AI outputs directly into patient records or reporting systems to ensure a smooth and efficient workflow.

Details

Category Radiology & Imaging AI
Pricing Not explicitly detailed for bladder ai software; the exo iris handheld ultrasound device, on which it runs, starts at $4,999. — Exo Iris Essential: $4,999
DeploymentOn-device AI with cloud-connected workflow (via Exo Works)
Compliance
BAA Available No AI-estimated
HIPAA Compliant No AI-estimated
FDA Status Yes AI-estimated

Bladder AI received FDA 510(k) clearance (K230497) on February 23, 2023, as a Class II medical device under regulation 21 CFR 892.2050 with product code QIH. It is indicated for quantifying bladder volume from ultrasound images in patients aged two years or older.
Integrations
EHR Not specified
Specialties Emergency Medicine, Radiology, Urology

What the Web Says

Bladder AI refers to artificial intelligence applications used in bladder scanning and genitourinary pathology. These AI systems aim to improve diagnostic accuracy, streamline workflows, and reduce costs in healthcare. Several products, such as Kosmos Bladder AI by EchoNous and Clarius Bladder AI, offer automated bladder volume measurements with high accuracy.

Overall: Positive

Strengths

  • Improved diagnostic precision and accuracy in bladder volume measurements, with some systems achieving u00b13% accuracy.
  • Reduced need for invasive procedures like catheterization, lowering the risk of catheter-associated urinary tract infections (CAUTIs).
  • Enhanced workflow efficiency and potential for significant time savings for medical professionals.
  • Real-time bladder volume measurements, beneficial for patients requiring frequent monitoring.
  • Some devices offer multi-purpose functionality, combining bladder scanning with other point-of-care ultrasound (POCUS) capabilities.
  • Solid-state probes in some systems offer durability, longer warranties, and reduced maintenance compared to traditional probes.

Limitations

  • Some Bladder AI modes or features require annual subscriptions, adding to the total cost of ownership.
  • Battery duration concerns have been reported for some portable devices.
  • Accuracy may not be validated for all patient populations or extreme bladder volumes.
  • Requires training for medical personnel, especially those less experienced with advanced technology.
  • Integration into existing clinical workflows can present challenges.
  • Bladder histopathology AI is still emerging compared to prostate AI, with limitations due to label variability and heterogeneous specimen quality.

Based on reviews from: Health Canada, EchoNous, Artificial Intelligence in Genitourinary Pathology: A Translational Readiness Map, HealthAidb, Way Point Medical, ICS 2024 Abstract #644 A Deep Learning Model to automate bladder volume using Clarius Handheld Ultrasound, FDA-approved artificial intelligence products in abdominal imaging, Clinical validation of AIu2010assisted contouring in prostate radiation therapy treatment planning: Highlighting automation bias and the need for standardized quality assurance, Exo Inc. Jacqueline Murray Senior Regulatory Affairs Specialist 4201 Burton Drive SANTA CLARA CA 95054 Re: K230497 Trade/D, Exo Irisu00ae | Portable Handheld Ultrasound Device

Last updated: 2026-07-18

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Press & Coverage

Medical News Observer
Bladder AI - Medical News Observer
Bladder AI by Exo Imaging received FDA 510(k) clearance in April 2024 as a Class II medical device for quantifying bladder volume from ultrasound images in patients aged two years or older. The device uses machine-learning techniques and demonstrated high accuracy and reliability in clinical performance on 122 subjects.
2024-04
Diagnostic Imaging
FDA Clears AI-Powered Bladder Volume Measurement for Handheld Ultrasound
Clarius Bladder AI ultrasound software, developed by Clarius Mobile Health, received 510(k) clearance from the FDA in January 2024 for automated bladder volume measurements. This AI-powered tool is particularly useful for assessing urinary retention and bladder emptying in various clinical settings.
2024-01
Medical Device Network
Clarius' new Bladder AI tool receives FDA 510(k) clearance - Medical Device Network
Clarius Mobile Health announced FDA 510(k) clearance for its Clarius Bladder AI, a non-invasive tool for automatic bladder volume measurement, in January 2024. This tool is a cost-effective and versatile alternative to traditional bladder scanners, offering real-time feedback to clinicians.
2024-01
Axis Imaging News
FDA Clears Clarius Mobile Health Bladder AI Scanner - Axis Imaging News
Clarius Mobile Health received FDA 510(k) clearance for its Clarius Bladder AI in January 2024, enabling non-invasive bladder volume measurements with their wireless handheld ultrasound scanners. This AI addition streamlines bladder volume assessment and offers whole-body ultrasound capabilities.
2024-01
Business Wire
Exou00ae Introduces a New Age of Ultrasound with Exo Irisu2122 - Business Wire
Exo Iris, introduced in September 2023, comes equipped with Exo's Bladder AI solution for rapid bladder volume determination. This point-of-care ultrasound device aims to improve patient care by providing quick and accurate diagnostic information.
2023-09
Journal of Clinical and Translational Pathology
Artificial Intelligence in Genitourinary Pathology: A Translational Readiness Map
This review article from 2023 highlights the rapid advancement of Bladder AI in genitourinary pathology, particularly in histopathology, urine cytology, and cystoscopy. AI systems commonly achieve over 80% accuracy for tasks like tumor detection and grade prediction.
2023-09
Journal of Applied Clinical Medical Physics
Deep learningu2010based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy - PMC
A May 2023 study explored deep learning for classifying organs at risk in pelvic cancer radiation therapy, noting that while Bladder AI delineations were minor, the model achieved an unweighted classification accuracy of 96.3%. The study also highlighted the potential influence of CT scan differences on classification output.
2023-05
Innolitics (FDA 510(k) Database)
Bladder AI (AIBV01) (K230497) u2014 FDA 510(k) | Innolitics
This regulatory entry confirms the FDA 510(k) clearance for Bladder AI (AIBV01) by Exo, Inc. on June 22, 2023, under product code QIH for radiology applications.
2023-06

Videos

Product demos, reviews, and walkthroughs for Bladder AI.

View all on YouTube

Frequently Asked Questions

Bladder AI is typically designed to integrate with electronic health record (EHR) systems, allowing for seamless data input and output. It can assist in tasks such as interpreting imaging, predicting treatment responses, or identifying patients at risk for certain bladder conditions, thereby augmenting clinical decision-making.
As a medical AI device, Bladder AI would require regulatory clearance from bodies like the FDA in the US or CE Mark in Europe, depending on its classification. It must also comply with strict data privacy regulations such as HIPAA, ensuring patient data is encrypted, anonymized, and securely managed.
Current alternatives often involve manual interpretation of diagnostic tests, subjective patient symptom assessment, and clinician experience-based decision-making. Bladder AI aims to provide objective, data-driven insights, potentially improving diagnostic accuracy, predicting outcomes more precisely, and personalizing treatment plans beyond traditional methods.
Pricing models for healthcare AI solutions often include subscription-based fees, per-use charges, or tiered licensing based on practice size or patient volume. Providers should expect options that may include basic functionality up to advanced analytics and integration services, with potential for volume discounts.
Like all AI, Bladder AI may have limitations related to the diversity of its training data, potentially leading to biases in specific patient populations or rare conditions. While accuracy rates are typically high for validated use cases, physicians should understand that AI is a decision-support tool and not a replacement for clinical judgment, especially in complex or atypical cases.
Bladder AI is often developed for specific applications such as early detection of bladder cancer from cytology or imaging, predicting overactive bladder treatment response, or identifying risk factors for urinary tract infections. Its effectiveness is typically validated for these targeted conditions, providing specialized support where data patterns are most discernible.
Implementation usually involves integrating the AI solution with existing EHRs and PACS systems, which may require IT department involvement for data mapping and security protocols. Ongoing support often includes software updates, technical assistance, and potentially training for clinical staff to ensure optimal utilization and troubleshooting.

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Suggest an Edit → | Last Verified: 2026-04-18 | First Added: 2026-04-18
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