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.

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

AI/ML Overview

Here's a summary of the acceptance criteria and the study proving the device meets them, based on the provided text:

Bladder AI (AIBV01) Performance Study Summary

1. Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" with numerical thresholds directly. However, it demonstrates performance by reporting accuracy and reliability metrics that would implicitly serve as the criteria for clearance. The study's conclusion that "the algorithm performance is reliable and accurate compared to expert clinician" and that "the results support the generalizability of the Bladder Al across the intended patient population" suggests these metrics met the internal or regulatory expectations.

Metric Type	Acceptance Criteria (Implicit)	Reported Device Performance (Bladder AI)	Note
Accuracy	Acceptable Mean Volume Difference
	Dual-View Bladder Volume	2 mL (LoA: -42 to 46)	Mean volume difference compared to expert consensus. LoA: Limits of Agreement.
	Single-View Bladder Volume	3 mL (LoA: -49 to 55)	Mean volume difference compared to expert consensus. LoA: Limits of Agreement.
Reliability	Acceptable Intraclass Correlation Coefficient (ICC)
	Dual-View Bladder Volume	0.98	ICC measures consistency or agreement between measurements. A higher value (closer to 1) indicates better reliability.
	Single-View Bladder Volume	0.97	ICC measures consistency or agreement between measurements. A higher value (closer to 1) indicates better reliability.

2. Sample Size and Data Provenance for the Test Set:

Sample Size: 122 subjects.
Data Provenance: A diverse collection of clinical sites in metropolitan cities, chosen to provide a broad range of demographic variables (ethnicity, gender, age 2 to 95 years old). The document does not specify the country of origin, but "metropolitan cities" implies a broad geographic reach within a developed context. The test data was retrospective as it consists of "images acquired from cart-based and portable ultrasound devices."

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set):

Number of Experts: Three expert clinicians.
Qualifications: The document states they were "expert clinicians." Specific experience levels (e.g., "radiologist with 10 years of experience") are not provided.

4. Adjudication Method for the Test Set:

Adjudication Method: The ground truth for bladder volume was obtained as the average bladder volume measurement among three expert clinicians. This suggests a form of consensus or averaging method, rather than a 2+1 or 3+1 rule for disagreement.

5. MRMC Comparative Effectiveness Study:

Was an MRMC study done? No. The study assessed the standalone performance of the Bladder AI compared to expert consensus. There is no mention of human readers using the AI for assistance and comparing their performance with and without AI.
Effect Size of Human Readers Improvement with AI vs. without AI assistance: Not applicable, as no MRMC study was conducted.

6. Standalone Performance:

Was standalone (algorithm only without human-in-the-loop performance) done? Yes. The performance metrics (Mean volume difference, ICC) are for the Bladder AI's measurements compared directly to the expert-established ground truth value.

7. Type of Ground Truth Used:

Type of Ground Truth: Expert consensus. Specifically, the "average bladder volume measurement among three expert clinicians."

8. Sample Size for the Training Set:

The document implies a training set was used, stating "Training and validation datasets have been selected and maintained to be appropriately independent of one another." However, the specific sample size for the training set is not provided in the excerpt.

9. How the Ground Truth for the Training Set was Established:

The document states that the "Training and validation datasets have been selected and maintained to be appropriately independent of one another." Similar to the training set sample size, the method for establishing ground truth for the training set is not explicitly detailed in the provided text. It is reasonable to infer it would involve expert review, but the specifics are not elucidated.

Summary

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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

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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

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Indications for Use

Submission Number (if known)

K230497

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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K230497

510(k) Summary

General Information

510(k) Sponsor	Exo Imaging
Address	4201 Burton DriveSanta Clara, CA 95054
Correspondence Person	Jacqueline Murray
Contact Information	jmurray@exo.incCell: +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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Device Description

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 DeviceBladder AI (K230497)	Predicate DeviceMEDO-Thyroid (K203502)	Reference DeviceLVivo Bladder (K200232)
Image input	Complies with DICOMStandard	Complies with DICOMStandard	Complies with DICOMStandard

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Feature/	Subject Device	Predicate Device	Reference Device
Function	Bladder Al (K230497)	MEDO-Thyroid (K203502)	LVivo Bladder (K200232)
Scan type	Single and Multi-frameimages	Single and Multi-frameimages	Single-frame images
Image displaymode	Static	Static	Static
Image navigationand manipulationtools	Slice-scroll, pane layout,reset	Slice-scroll, pane layout,reset	Slice-scroll, pane layout,reset
Image review	Yes, capable of reviewing allframes of multi-frame(multi-slice) images	Yes, capable of reviewingall frames of multi-frame(multi-slice) images	Yes, capable of reviewingimages
Manual landmarkplacement	Yes	Yes	Yes
Semi-automaticlandmarkplacement	Yes, user-modifiable	Yes, user-modifiable	Yes, user-modifiable
Quantitativeanalysis	Distance, Volume	Distance, Volume	Distance, Volume
Report creation	Yes	Yes	Yes
Display Calipers	Yes	Yes	Yes
Frame	Transverse and SagittalViews	Transverse and SagittalViews	Transverse and SagittalViews
Operating System	Web browser (GoogleChrome)	Web browser (GoogleChrome)	Web browser (GoogleChrome) and Android
Algorithm	Image segmentation forborder detection	Image segmentation forborder detection	Image segmentation forborder 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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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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	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.

Regulation Number and Section

§ 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).