(118 days)
Yes
The device description explicitly states that "Bladder Al uses machine-learning techniques" and highlights "ML-based semi-automatic landmark placements" as a key feature.
No.
The device quantifies bladder volume from ultrasound images and helps generate reports, which are diagnostic and monitoring functions, not therapeutic.
Yes
The device "aids in the quantification of bladder volume from ultrasound images" and helps "evaluate, quantify, and generate reports for bladder ultrasound images," which supports assessing a patient's condition.
Yes
The device is explicitly described as "standalone software as a medical device (SaMD)" and its function is to process and analyze imported DICOM images from ultrasound scanners, without including or requiring any specific hardware component for its operation.
Based on the provided information, this device is NOT an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are medical devices used to perform tests on samples taken from the human body (like blood, urine, tissue) to provide information about a person's health. These tests are performed outside the body.
- Bladder AI's Function: Bladder AI analyzes images of the bladder obtained from an ultrasound scanner. It does not perform any tests on biological samples. It processes existing images to aid in the quantification of bladder volume.
- Input: The input is DICOM images from an ultrasound scanner, not biological samples.
- Output: The output is a measurement of bladder volume and a report, not a diagnostic result based on a biological test.
While Bladder AI is a medical device and uses advanced techniques like machine learning to aid in a clinical assessment, its function falls under the category of image analysis and quantification, not in vitro diagnostic testing.
No
The provided input text does not contain any explicit statement that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this device. The 'Control Plan Authorized (PCCP) and relevant text' section explicitly states 'Not Found'.
Intended Use / Indications for Use
Bladder Al uses machine-learning techniques to aid in the quantification of bladder volume from ultrasound images. The device is intended to be used on images of patients aged two years or older.
Product codes
QIH
Device Description
Bladder Al is a standalone software as a medical device (SaMD) that helps qualified users with image-based assessment of bladder ultrasound images in patients aged 2 or older. It is designed to simplify workflow by helping trained healthcare providers evaluate, quantify, and generate reports for bladder ultrasound images.
Bladder Al takes as an input imported Digital Imaging and Communications in Medicine (DICOM) images from ultrasound scanners and allows users to measure bladder volumes of a single frame and multi-frame ultrasound images, as well as create and finalize examination reports. It provides users with a specific toolset for viewing ultrasound images of the bladder, placing landmarks, and creating reports.
Key features of the software are
- ML-based semi-automatic landmark placements
- Bladder dimension and volume measurements
- . Report generation
Mentions image processing
Yes
Mentions AI, DNN, or ML
Bladder Al uses machine-learning techniques to aid in the quantification of bladder volume from ultrasound images.
ML-based semi-automatic landmark placements
Input Imaging Modality
ultrasound images
Anatomical Site
bladder
Indicated Patient Age Range
two years or older.
Intended User / Care Setting
qualified users / trained healthcare providers
Description of the training set, sample size, data source, and annotation protocol
Training and validation datasets have been selected and maintained to be appropriately independent of one another. All potential sources of dependence, including patient and site factors, have been considered and addressed to assure independence.
Description of the test set, sample size, data source, and annotation protocol
The clinical performance on Bladder Al was successfully evaluated on 122 subjects, on images acquired from cart-based and portable ultrasound devices (with frequency ranging from 1.3 to 9 MHz) and on bladder volumes ranging between 11 to 645 mL.
A diverse collection of clinical sites in metropolitan cities contributed to the test data, encompassing a broad range of demographic variables. These variables included ethnicity, gender, as well as age, spanning from 2 to 95 years old.
The test data was entirely separated from the training/validation datasets and was not used for any part of the training. To ensure data separation and generalizability, the data sources used in the test set are chosen to be different from the data sources used in the training set. We also established auditability measures, by assigning a unique identification number to each study and its corresponding images.
The ground truth for bladder volume (reference data) was obtained as the average bladder volume measurement among three expert clinicians.
Summary of Performance Studies
Validation Performance testing
The clinical performance on Bladder Al was successfully evaluated on 122 subjects, on images acquired from cart-based and portable ultrasound devices (with frequency ranging from 1.3 to 9 MHz) and on bladder volumes ranging between 11 to 645 mL. Performance was assessed by calculating the intraclass correlation coefficient (ICC) and 2-sided 95% Confidence Interval of the Bladder Volume error.
The results demonstrated that the algorithm performance is reliable and accurate compared to expert clinician. Additionally, the evaluation concluded that the algorithm's performance was consistent among clinically meaningful subgroups: age, gender, BMI and device manufacturers. Overall, the results support the generalizability of the Bladder Al across the intended patient population.
Key Metrics
Accuracy: Mean volume difference, Limits of Agreement.
Reliability: Intraclass correlation coefficient (ICC).
Bladder volume, Dual-View:
Accuracy: 2 mL (LoA: -42 to 46)
Reliability: 0.98
Bladder volume, Single-View:
Accuracy: 3 mL (LoA: -49 to 55)
Reliability: 0.97
Predicate Device(s)
Reference Device(s)
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
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Exo Inc. % Jacqueline Murray Senior Regulatory Affairs Specialist 4201 Burton Drive SANTA CLARA CA 95054
Re: K230497
June 22, 2023
Trade/Device Name: Bladder AI (AIBV01) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: April 18, 2023 Received: May 18, 2023
Dear Jacqueline Murray:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
1
devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
Jessica Lamb
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
Submission Number (if known)
Device Name
Bladder Al (AIBV01)
Indications for Use (Describe)
Bladder Al uses machine-learning techniques to aid in the quantification of bladder volume from ultrasound images. The device is intended to be used on images of patients aged two years or older.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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Image /page/3/Picture/1 description: The image shows the logo for EXO. The logo consists of a pattern of blue circles on the left and the word "EXO" in a dark gray sans-serif font on the right. The blue circles are arranged in a grid-like pattern, with the circles varying in shade from light blue to dark blue. The word "EXO" is written in all capital letters and is positioned to the right of the blue circles.
510(k) Summary
General Information
| 510(k) Sponsor | Exo Imaging |
|---|---|
| Address | 4201 Burton Drive |
| Santa Clara, CA 95054 | |
| Correspondence Person | Jacqueline Murray |
| Contact Information | jmurray@exo.inc |
| Cell: +236 838-5056 | |
| Date Prepared | February 23, 2023 |
Proposed Device
| Proprietary Name | Bladder AI(AIBV01) |
|---|---|
| Common Name | Exo Bladder AI |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
Predicate Device
| Proprietary Name | MEDO-Thyroid |
|---|---|
| Premarket Notification | K203502 |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
Reference Device
| Proprietary Name | LVivo Bladder |
|---|---|
| Premarket Notification | K200232 |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
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Image /page/4/Picture/1 description: The image contains a logo with two distinct parts. On the left, there is a cluster of blue and cyan gradient circles arranged in a pattern resembling a stylized letter or symbol. To the right of the circles, the letters "EXO" are displayed in a dark gray sans-serif font. The letters are bold and evenly spaced, creating a clean and modern look.
Device Description
Bladder Al is a standalone software as a medical device (SaMD) that helps qualified users with image-based assessment of bladder ultrasound images in patients aged 2 or older. It is designed to simplify workflow by helping trained healthcare providers evaluate, quantify, and generate reports for bladder ultrasound images.
Bladder Al takes as an input imported Digital Imaging and Communications in Medicine (DICOM) images from ultrasound scanners and allows users to measure bladder volumes of a single frame and multi-frame ultrasound images, as well as create and finalize examination reports. It provides users with a specific toolset for viewing ultrasound images of the bladder, placing landmarks, and creating reports.
Key features of the software are
- ML-based semi-automatic landmark placements
- Bladder dimension and volume measurements
- . Report generation
Training and validation datasets have been selected and maintained to be appropriately independent of one another. All potential sources of dependence, including patient and site factors, have been considered and addressed to assure independence.
Indications for Use
Bladder Al uses machine-learning techniques to aid in the quantification of bladder volume from ultrasound images. The device is intended to be used on images of patients aged two years or older.
Comparison of Technological Characteristics with the Predicate Device
| Feature/
Function | Subject Device
Bladder AI (K230497) | Predicate Device
MEDO-Thyroid (K203502) | Reference Device
LVivo Bladder (K200232) |
|----------------------|----------------------------------------|--------------------------------------------|---------------------------------------------|
| Image input | Complies with DICOM
Standard | Complies with DICOM
Standard | Complies with DICOM
Standard |
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Image /page/5/Picture/1 description: The image shows the logo for EXO. On the left side of the logo, there is a cluster of blue and light blue dots arranged in a pattern. To the right of the dots, the word "EXO" is written in a dark gray sans-serif font. The letters are stylized, with the "X" having a unique design.
| Feature/ | Subject Device | Predicate Device | Reference Device |
|---|---|---|---|
| Function | Bladder Al (K230497) | MEDO-Thyroid (K203502) | LVivo Bladder (K200232) |
| Scan type | Single and Multi-frame | ||
| images | Single and Multi-frame | ||
| images | Single-frame images | ||
| Image display | |||
| mode | Static | Static | Static |
| Image navigation | |||
| and manipulation | |||
| tools | Slice-scroll, pane layout, | ||
| reset | Slice-scroll, pane layout, | ||
| reset | Slice-scroll, pane layout, | ||
| reset | |||
| Image review | Yes, capable of reviewing all | ||
| frames of multi-frame | |||
| (multi-slice) images | Yes, capable of reviewing | ||
| all frames of multi-frame | |||
| (multi-slice) images | Yes, capable of reviewing | ||
| images | |||
| Manual landmark | |||
| placement | Yes | Yes | Yes |
| Semi-automatic | |||
| landmark | |||
| placement | Yes, user-modifiable | Yes, user-modifiable | Yes, user-modifiable |
| Quantitative | |||
| analysis | Distance, Volume | Distance, Volume | Distance, Volume |
| Report creation | Yes | Yes | Yes |
| Display Calipers | Yes | Yes | Yes |
| Frame | Transverse and Sagittal | ||
| Views | Transverse and Sagittal | ||
| Views | Transverse and Sagittal | ||
| Views | |||
| Operating System | Web browser (Google | ||
| Chrome) | Web browser (Google | ||
| Chrome) | Web browser (Google | ||
| Chrome) and Android | |||
| Algorithm | Image segmentation for | ||
| border detection | Image segmentation for | ||
| border detection | Image segmentation for | ||
| border detection |
Performance Data
Safety and performance of Bladder Al have been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/AC:2015 - Medical device software - Software life cycle processes, FDA Guidance (May 2005), "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and FDA Guidance (June 2022) "Technical performance assessment of quantitative imaging in radiological device premarket submissions".
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Image /page/6/Picture/0 description: The image shows the logo for EXO. The logo consists of two parts: a cluster of blue circles arranged in a triangular pattern on the left, and the word "EXO" in a dark gray sans-serif font on the right. The circles in the cluster vary in shade, with some being a lighter blue and others a darker blue.
Validation Performance testing
The clinical performance on Bladder Al was successfully evaluated on 122 subjects, on images acquired from cart-based and portable ultrasound devices (with frequency ranging from 1.3 to 9 MHz) and on bladder volumes ranging between 11 to 645 mL.
A diverse collection of clinical sites in metropolitan cities contributed to the test data, encompassing a broad range of demographic variables. These variables included ethnicity, gender, as well as age, spanning from 2 to 95 years old.
The test data was entirely separated from the training/validation datasets and was not used for any part of the training. To ensure data separation and generalizability, the data sources used in the test set are chosen to be different from the data sources used in the training set. We also established auditability measures, by assigning a unique identification number to each study and its corresponding images.
The ground truth for bladder volume (reference data) was obtained as the average bladder volume measurement among three expert clinicians. Performance was assessed by calculating the intraclass correlation coefficient (ICC) and 2-sided 95% Confidence Interval of the Bladder Volume error. The measurement accuracy and reliability of Bladder Al compared with this reference data is summarized in Table 1.
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Image /page/7/Picture/1 description: The image shows the logo for EXO. On the left side of the logo, there is a pattern of blue and light blue dots arranged in a triangular shape. To the right of the dots, the word "EXO" is written in a dark gray sans-serif font.
| Accuracy | Reliability | |
|---|---|---|
| Mean volume difference | ||
| (Limits of Agreement) | Intraclass correlation coefficient | |
| (ICC) | ||
| Bladder volume, | ||
| Dual-View1 | 2 mL (LoA: -42 to 46) | 0.98 |
| Bladder volume, | ||
| Single-View2 | 3 mL (LoA: -49 to 55) | 0.97 |
Table 1: Summary of Bladder Al measurement accuracy and reliability.
The results demonstrated that the algorithm performance is reliable and accurate compared to expert clinician. Additionally, the evaluation concluded that the algorithm's performance was consistent among clinically meaningful subgroups: age, gender, BMI and device manufacturers. Overall, the results support the generalizability of the Bladder Al across the intended patient population.
Conclusions
Exo's Bladder Al is substantially equivalent in intended use, design, principles of operation, technological characteristics, and safety features to the predicate device. There are no different questions of safety and/or effectiveness introduced by Bladder Al when used as intended.
1 Dual-View bladder volume is calculated from both transverse and sagittal views.
² Single-View bladder volume is calculated from only one view.