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Clarius Bladder AI is intended for semi-automatic non-invasive measurements of bladder volume on ultrasound data acquired by the Clarius Ultrasound Scanner (i.e., curvilinear and phased array scanners). The user shall be a healthcare professional trained and qualified in ultrasound. The ultimate responsibility of ascertaining the measurements based on standard practices and clinical judgment. Clarius Bladder AI is indicated for use in adult patients only.

Clarius Bladder AI is a radiological (ultrasound) image processing software application which implements artificial intelligence (Al), utilizing non-adaptive machine learning algorithms, and is incorporated into the Clarius App software for use as part of the complete Clarius Ultrasound Scanner system product offering in bladder ultrasound imaging applications. Clarius Bladder Al is intended for use by trained healthcare practitioners for non-invasive measurements of bladder volume on ultrasound data acquired by the Clarius Ultrasound Scanner system (i.e., curvilinear and phased array scanners) using an artificial intelligence (AI) image segmentation algorithm.

During the ultrasound imaging procedure, the anatomical site (bladder) is selected through a preset software selection (i.e., bladder) within the Clarius App in which Clarius Bladder Al will engage to segment the bladder and place calipers for calculation of bladder volume.

Clarius Bladder Al operates by performing the following automations:

• . Automatic detection and measurement of bladder depth
• . Automatic detection and measurement of bladder width
• . Automatic detection and measurement of bladder height
• . Automatic detection of the corresponding image view (sagittal vs. transverse)

Clarius Bladder Al operates by performing automatic measurements of bladder height, width, and length, and calculates bladder volume. The user has the option to manually adjust the measurements made by Clarius Bladder Al by moving the caliper crosshairs. Clarius Bladder Al does not perform any functions that could not be accomplished manually by a trained and qualified user. Clarius Bladder Al is intended for use in B-Mode only.

Clarius Bladder AI is an assistive tool intended to inform clinical management and is not intended to replace clinical decision-making. The clinician retains the ultimate responsibility of ascertaining the measurements based on standard practices and clinical judgment. Clarius Bladder Al is indicated for use in adult patients only.

Clarius Bladder AI is incorporated into the Clarius App software, which is compatible with iOS and Android operating systems two versions prior to the latest iOS or Android stable release build and is intended for use with the following Clarius Ultrasound Scanner system transducers (previously 510(k)-cleared in K213436). Clarius Bladder Al is not a stand-alone software device.

Here's a breakdown of the acceptance criteria and the study details for the Clarius Bladder AI device, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The core acceptance criterion for Clarius Bladder AI's automated measurements was non-inferiority to manual measurements performed by qualified experts, with an equivalence margin of 25% for the mean difference between percentage differences of bladder volume measurements.

Prospective Study: p-value of 1.36e-14 (confirming non-inferiority). Mean difference between percent differences of clinical expert mean and Bladder AI mean was -0.0228 (95% CI -0.074, 0.028). |
| Agreement with Experts: Strong agreement between Clarius Bladder AI measurements and the mean of expert clinicians' measurements, and with individual expert measurements. | Both retrospective and prospective studies reported strong agreement between Clarius Bladder AI and expert measurements, as well as high inter-rater reliability (Intraclass Correlation Coefficients for inter-rater reliability were calculated and found to be strong). Average Dice scores and Jaccard index were also calculated, indicating good segmentation agreement. |
| Clinical Usability: Performs as intended in a representative user environment, meets product requirements, is clinically usable, and meets user needs for semi-automated bladder volume measurements. | Clinical validation study results showed consistent results among all users, meeting pre-defined acceptance criteria, demonstrating that Clarius Bladder AI performs as intended and meets user needs. Users were able to activate, image, perform live segmentation, automatic measurements, manual adjustments, and save measurements. |

2. Sample Size and Data Provenance for Test Set

Retrospective Study:

• Sample Size: 66 subjects (10 female, 38 male, gender of remaining unknown)
• Data Provenance: Anonymized multi-center database of images from predominantly the United States. Institutions included in the model training and tuning datasets were excluded from this study. Retrospective.

Prospective Study:

• Sample Size: 58 subjects (40 female, 18 male)
• Data Provenance: Conducted at a healthcare institution in the United States. Images were obtained prospectively.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: 3 reviewers (referred to as "clinical truthers" or "clinical experts") for both the retrospective and prospective studies.
• Qualifications of Experts: Described as "qualified experts with relevant (i.e., bladder) ultrasound experience."
  • For the retrospective study: "qualified experts with relevant (i.e., bladder) ultrasound experience."
  • For the prospective study: "qualified experts with clinical experience in bladder ultrasound."

4. Adjudication Method for the Test Set

The ground truth for bladder volume in both retrospective and prospective studies was established as the mean bladder volume measurement among the three clinical experts. Each reviewer was blinded to the Clarius Bladder AI output and the other reviewers' annotations.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The provided information does not explicitly describe a traditional MRMC comparative effectiveness study designed to measure the effect size of human readers improving with AI vs. without AI assistance.

Instead, the studies focused on demonstrating the non-inferiority of the AI device's standalone measurements compared to the mean of multiple human expert measurements. While comparisons were made between reviewer pairs (inter-rater reliability), and between the AI output and individual/mean expert measurements, the studies did not seem to directly evaluate human performance with the AI assistance versus human performance without it in a controlled MRMC setting to quantify a "human improvement" effect size.

6. Standalone Performance Study (Algorithm Only)

Yes, a standalone (algorithm only) performance study was done. The core of both the retrospective and prospective verification studies was to evaluate the Clarius Bladder AI's automated measurements directly against expert manual measurements, demonstrating its performance without human intervention (other than initial image acquisition and potential later manual adjustment by the user, which was a separate feature). The non-inferiority claims are based on this standalone performance.

7. Type of Ground Truth Used

The ground truth used was expert consensus, specifically defined as the mean bladder volume measurement among three clinical experts.

8. Sample Size for the Training Set

• Training Dataset: 1352 subjects (353 female, 999 male).
  • Note: This also includes a validation (tuning) dataset which was 10% of the training data.

9. How the Ground Truth for the Training Set Was Established

The deep neural network (DNN) model was trained using the raw training dataset. The summary states that the test data (which was independent) was "labelled by experts." While it doesn't explicitly detail the ground truth establishment for the training set itself, it can be inferred that similar expert labeling or a robust annotation process would have been used to generate the ground truth for the images used in training. The summary highlights that the validation (tuning) data was independent, and the test data was "labelled by experts," suggesting expert annotation for ground truth across relevant datasets. The overall context points to expert-derived ground truth for model development.

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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.

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

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.

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.

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The V8 diagnostic ultrasound system and probes are designed to obtain ultrasound images and analyze body fluids. The clinical applications include: Fetal/Obsterics, Abdominal, Gynecology, Intra-operative, Small Organ, Neonatal Cephalic, Adult Cephalic, Trans-raginal, Muscular-Skeletal (Conventional, Superficial), Urology, Cardiac Adult, Cardiac Pediatric, Thoracic, Trans-esophageal (Cardiac) and Peripheral vessel.

It is intended for use by, or by the order of, and under the supervision of, an appropriately trained healthcare professional who is qualified for direct use of medical devices. It can be used in hospitals, clinics and similar care environment for clinical diagnosis of patients.

Modes of Operation: 2D mode, Color Doppler mode, Power Doppler (PD) mode, M mode, Pulsed Wave (PW) Doppler mode, Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, ElastoScan Mode, Combined modes, Multi-Image mode(Dual, Quad), 3D/4D mode

The V8 is a general purpose, mobile, software controlled, diagnostic ultrasound system. Its function is to acquire ultrasound data and to display the data as 2D mode, Color Doppler mode, Power Doppler (PD) mode, M mode, Pulsed Wave (PW) Doppler mode, Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, ElastoScan Mode, Combined modes, Multi-Image mode(Dual, Quad), 3D/4D mode. The V8 also gives the operator the ability to measure anatomical structures and offers analysis packages that provide information that is used to make a diagnosis by competent health care professionals. The V8 has real time acoustic output display with two basic indices, a mechanical index and a thermal index, which are both automatically displayed.

The provided document is a 510(k) Summary for the Samsung Medison V8 Diagnostic Ultrasound System. It details the device's intended use, technological comparison to predicate devices, and non-clinical testing. However, it explicitly states that no clinical studies were required to demonstrate substantial equivalence.

Therefore, I cannot provide information on acceptance criteria and a study proving the device meets those criteria from this document. The document primarily focuses on demonstrating substantial equivalence through non-clinical testing and comparison to existing predicate devices, rather than a clinical study with defined performance metrics and acceptance criteria.

The relevant section from the document is:

12. Summary of Clinical Tests

The proposed device V8 Ultrasound System did not require clinical studies to demonstrate substantial equivalence.

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The HS40 diagnostic ultrasound system and probes are designed to obtain ultrasound images and analyze body fluids.

The clinical applications include: Fetal/Obstetrics, Abdominal, Gynecology, Pediatric, Small Organ, Neonatal Cephalic, Adult Cephalic, Trans-rectal, Trans-vaginal, Muscular-Skeletal (Conventional, Superficial), Urology, Cardiac Adult, Cardiac Pediatric and Peripheral vessel.

It is intended for use by, or by the order of, and under the supervision of, an appropriately trained healthcare professional who is qualified for direct use of medical devices. It can be used in hospitals, private practices, clinics and similar care environment for clinical diagnosis of patients.

Modes of Operation: 2D mode, Color Doppler mode, Power Doppler (PD) mode, M mode, Pulsed Wave (PW) Doppler mode, Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, ElastoScan Mode, Combined modes, Multi-Image mode(Dual, Quad), 3D/4D mode.

The HS40 is a general purpose, mobile, software controlled, diagnostic ultrasound system. Its function is to acquire ultrasound data and to display the data as 2D mode, M mode, Color Doppler imaging, Power Doppler imaging (including Directional Power Doppler mode; S-Flow), PW Spectral Doppler mode, CW Spectral Doppler mode, Harmonic imaging(S-Harmonic), Tissue Doppler imaging, Tissue Doppler Wave, Panoramic Imaging, Freehand 3D, 3D imaging mode (real-time 4D imaging mode), Elastoscan Mode or as a combination of these modes. The HS40 also gives the operator the ability to measure anatomical structures and offers analysis packages that provide information that is used to make a diagnosis by competent health care professionals. The HS40 has real time acoustic output display with two basic indices, a mechanical index and a thermal index, which are both automatically displayed.

The provided text is a 510(k) Premarket Notification for the Samsung Medison HS40 Diagnostic Ultrasound System. It describes the device, its intended use, and its substantial equivalence to a predicate device. However, it explicitly states:

"The subject of this premarket submission HS40. is not required clinical studies to support substantial equivalence." and "Summary of Clinical Tests: The subject of this premarket submission HS40. is not required clinical studies to support substantial equivalence."

Therefore, based on the provided document, there is no information available about acceptance criteria, the study that proves the device meets specific acceptance criteria, or any details related to human reader performance, expert involvement, or ground truth establishment relevant to the questions asked.

The document focuses on non-clinical tests (acoustic output, biocompatibility, software function, electrical/thermal/electromagnetic/mechanical safety, cleaning/disinfection) and demonstrating substantial equivalence to a predicate device based on similar intended use and technological features.

Because the document states that clinical studies were not required, it cannot provide the information requested in the prompt.

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The Acclarix AX3 series/Acclarix LX3 series Diagnostic Ultrasound System is intended for use by a qualified physician or allied health professional for ultrasound evaluations in hospitals and clinical applications include:Abdominal. Gvnecology, Obstetric. Cardiac, Small parts. Urology, Musculosketal . Peripheral vascular. Intraoperative, Pediatric, Neonatal , Adult Cephalic.

Acclarix AX3 Series/ Acclarix LX3 Series is a software controlled Diagnostic Ultrasound System, which consists of a main unit along with associated transducers. It is intended for use by a qualified physician or allied health professional for ultrasound evaluations. This system is a Track 3 device to acquire and display ultrasound data in various imaging modes.

The provided text describes a 510(k) premarket notification for a Diagnostic Ultrasound System. This document focuses on demonstrating substantial equivalence to previously cleared predicate devices, rather than establishing acceptance criteria and conducting a new study to prove a device meets those criteria for novel technology.

Therefore, many of the requested elements for a study proving device performance against acceptance criteria are not available in this document. The document primarily focuses on explaining how the new device is similar to already approved ultrasound systems and transducers.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

This information is not explicitly stated in terms of specific performance metrics with acceptance criteria and their corresponding reported values. Instead, the document claims substantial equivalence to predicate devices based on:

• Intended Use/Indications for Use
• Mode of Operations
• Design principles
• Hardware and software platforms
• Transducer types
• Acoustic Output (stated to be below FDA limits)
• Materials of transducers and needle guide brackets

2. Sample size used for the test set and data provenance:

• Test Set Sample Size: Not provided. The document states "Clinical testing is not required" and primarily relies on non-clinical tests and comparison to predicate devices.
• Data Provenance: The document implies that the "clinical applications" and "imaging modes" added to the new device are based on what was already cleared for the Acclarix LX9 series (K192879) and other predicate devices (K200232 and K201693). This suggests a reliance on retrospective data and established performance of predicate devices, rather than new prospective clinical trials for this specific submission. The origin of the data for the predicate devices is not specified in this document.

3. Number of experts used to establish the ground truth for the test set and qualifications of those experts:

• Not applicable. Since clinical testing was not required for this submission, there was no separate test set requiring new ground truth establishment by experts for specific AI algorithm performance.

4. Adjudication method for the test set:

• Not applicable. No specific adjudication method is mentioned as there was no dedicated clinical test set described for new performance evaluation.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size:

• No, an MRMC study was NOT done. The document explicitly states "Clinical testing is not required" and focuses on substantial equivalence based on technical specifications and previously cleared indications. There is no mention of human readers or AI assistance.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

• Not applicable. This document is for a diagnostic ultrasound system, not an AI algorithm. While the system may employ internal processing (which could include algorithmic components), the submission does not detail testing of standalone AI performance against a ground truth.

7. The type of ground truth used:

• Not applicable for novel performance claims. The "ground truth" implicitly relies on the established and cleared performance of the predicate diagnostic ultrasound systems and their associated transducers. The "Acoustic output testing" was done "as per the guideline 'Marketing Clearance of Diagnostic Ultrasound Systems and Transducers' dated June 27, 2019," which suggests adherence to industry standards and regulatory guidance for ultrasound device performance, rather than clinical ground truth for a specific medical condition.

8. The sample size for the training set:

• Not applicable. This document does not describe the development or training of a new AI algorithm. It refers to a diagnostic ultrasound system and its transducers.

9. How the ground truth for the training set was established:

• Not applicable. As no new AI algorithm development is described, there is no training set or ground truth specific to an algorithm's training.

Summary of what the document does provide:

The document serves as a 510(k) submission arguing for the substantial equivalence of the new Edan Instruments Diagnostic Ultrasound Systems (Acclarix AX3 series and Acclarix LX3 series) to existing cleared predicate devices. The arguments are based on:

• Non-clinical performance tests demonstrating compliance with relevant electrical safety, EMC, and acoustic output standards (ANSVAAMI ES 60601-1, IEC 60601-1-2, IEC 60601-2-37, ISO 10993-1, ISO 10993-5, ISO 10993-10).
• Comparison of intended use, indications for use, mode of operations, design principles, hardware/software platforms, and transducer types to previously cleared Edan Instruments ultrasound systems (K192791, K192879) and other predicate devices (K200232, K201693).
• The argument is that the differences are minor and do not raise new questions of safety or effectiveness. For example, new clinical applications, transducers, and imaging/measurement modes have already been cleared on other predicate devices.

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