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UltraExtend NX CUW-U001S Ultrasound Image Analysis Program is designed to allow the user to observe images and perform analysis based on examination data acquired using the following diagnostic ultrasound systems; TUS-AI900, TUS-AI800, and TUS-AI700.

This system is suitable for use in hospital and clinical settings by physicians or legally qualified persons who have received the appropriate training.

The UltraExtend NX, V2.0 is designed to allow the user to observe images and perform analysis based on examination data acquired using the Aplio i900/i800/i700 diagnostic ultrasound systems. RAW only or data saved in Image + RAW should be used for UltraExtend NX.

The FDA 510(k) clearance letter for the UltraExtend NX CUW-U001S V2.0 Ultrasound Image Analysis Program indicates that the device has integrated AI/ML-based functionality (2D Wall Motion Tracking with Full-assist function for left ventricle (LV) and Auto EF with Full-assist function for LV) that was previously cleared with a reference device (K223017). The submission states that "these studies utilized a representative subset of the clinical data acquired for the original performance testing of these features; additionally these studies applied the same acceptance criteria to evaluate the performance of these features compared to the same ground truth as utilized in the original performance evaluation of these features with the reference device."

Unfortunately, the provided text does not contain the specific acceptance criteria or detailed results of the performance testing for these AI/ML features. It only states that the features "perform as intended when integrated into the subject device, and with substantial equivalence as with the reference device."

Therefore, I cannot provide a table of acceptance criteria and reported device performance or many of the specific details requested in your prompt based solely on the provided document. The document refers to the original performance testing of the reference device (K223017) for these details.

However, I can extract and infer information about the study design to the extent possible:

Here's what can be inferred from the provided text, and what cannot be determined:

Acceptance Criteria and Device Performance

• The document states that the same acceptance criteria as the original performance testing for the reference device (K223017) were applied.
• Cannot Determine: The specific numerical acceptance criteria (e.g., specific accuracy, sensitivity, specificity thresholds) or the reported device performance metrics (e.g., actual accuracy, sensitivity, specificity values) are not provided in this document.

Study Information

Summary of missing information:

To fully answer your prompt, you would need to consult the original 510(k) submission for the reference device (K223017), Aplio i900/i800/i700 Diagnostic Ultrasound System, Software Version 7.0, as that is where the detailed performance data, acceptance criteria, and ground truth establishment methodology for the AI/ML features would have been submitted and evaluated by the FDA. The current document (K250328) focuses on demonstrating that these already cleared AI/ML features maintain their performance when integrated into a new workstation.

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The Diagnostic Ultrasound System Aplio i900 Model TUS-A1900, Aplio i800 Model TUS-A1800 and Aplio i700 Model TUS-AI700 are indicated for the visualization of structures, and dynamic processes with the human body using ultrasound and to provide image information for diagnosis in the following clinical applications: fetal, abdominal, intra-operative (abdominal), pediatric, small organs (thyroid, breast and testicle), trans-rectal, neonatal cephalic, adult cephalic, cardiac (both adult and pediatic), peripheral vascular, transesophageal, musculo-skeletal (both conventional and superficial), laparoscopic and Thoracic/Pleural. This system provides high-quality ultrasound images in the following modes B mode, M mode, Continuous Wave, Color Doppler, Pulsed Wave Doppler and Combination Dopler, as well as Speckle-tracking, Tissue Harmonic Imaging, Combined Modes, Shear wave, Elastography, and Acoustic attenuation mapping. This system is suitable for use in hospital and clinical settings by physicians or legally qualified persons who have received the appropriate training.

The Aplio i900 Model TUS-AI900, Aplio i800 Model TUS-AI800 and Aplio i700 Model TUS-AI700, V7.0 are mobile diagnostic ultrasound systems. These systems are Track 3 devices that employ a wide array of probes including flat linear array, convex, and sector array with frequency ranges between approximately 2MHz to 33MHz.

The document describes the validation of several AI/ML-based features within the Aplio i900/i800/i700 Diagnostic Ultrasound System, Software V7.0. The study aims to demonstrate that these new features are substantially equivalent to existing functionalities and improve workflow.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The document describes several AI/ML-based features. While the format isn't a single table, I can synthesize the information for each feature:

Feature: Auto Plane Detection

Feature: Quick Strain

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The Diagnostic Ultrasound System Aplio flex Model CUS-AFL00 and Aplio go Model CUS-AGG00 are indicated for the visualization of structures, and dynamic processes with the human body using ultrasound and to provide image information for diagnosis in the following clinical applications: fetal, abdominal, pediatric, small organ (thyroid, breast and testicle), neonatal cephalic, trans-rectal, transvaginal, musculoskeletal (both conventional and superficial), cardiac, peripheral vascular, and thoracic/pleural.

This system provides high-quality ultrasound images in the following modes: B mode, M mode, Continuous Wave, Color Doppler, Pulsed Wave Doppler , Power Doppler as well as Tissue Harmonic Imaging, Combined Modes and Acoustic attenuation mapping.

This system is suitable for use in hospital and clinical settings by physicians or legally qualified persons who have received the appropriate training.

The Aplio flex, Model CUS-AFL00 and Aplio go, Model CUS-AGG00, V2.0 are mobile diagnostic ultrasound systems. These systems are Track 3 devices that employ an array of probes including flat linear array, convex, and sector array with frequency ranges between approximately 2.5MHz to 12MHz.

The provided text is a 510(k) summary for the Aplio flex and Aplio go Software V2.0 Diagnostic Ultrasound System. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed performance study with explicit acceptance criteria and corresponding results for specific device performance metrics.

Therefore, the document does not contain the detailed information required to fully answer your request, especially regarding:

• A table of acceptance criteria and reported device performance.
• Sample sizes, data provenance, number of experts, adjudication methods for a test set.
• MRMC comparative effectiveness study results (effect size).
• Details on standalone performance.
• Training set details and ground truth establishment for the training set.

The document states generally that "Risk Analysis and verification and validation activities demonstrate that the established specifications for these devices have been met" and "Additional performance testing included in the submission was conducted in order to demonstrate that the requirements for the new features were met." However, it does not elaborate on what those specific specifications or requirements were, nor does it quantify the performance against them.

The information that can be extracted or inferred is as follows:

1. A table of acceptance criteria and the reported device performance

The document does not provide a table with specific, quantifiable acceptance criteria and corresponding performance metrics for the Aplio flex and Aplio go Software V2.0. The overall "acceptance criterion" implied throughout the document is demonstrating substantial equivalence to the predicate device (Xario 200G/100G Diagnostic Ultrasound System, Software Version 1.1, K182596) in terms of safety and effectiveness, and meeting established specifications in verification and validation activities.

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

The document does not specify a "test set" in the context of clinical performance evaluation (e.g., patient cases, images). The testing mentioned is broadly "Risk Analysis and verification and validation activities" and "Additional performance testing for new features." This suggests engineering and system-level testing rather than a clinical performance study with a distinct test set. Therefore, sample size and data provenance are not reported.

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

This information is not provided, as the document does not describe a clinical study with a ground truth established by experts.

4. Adjudication method for the test set

This information is not provided.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

The document does not mention an MRMC comparative effectiveness study or any AI components that would assist human readers in a way that would require an "AI vs without AI assistance" comparison. The device is a diagnostic ultrasound system, not an AI-powered diagnostic aide in the sense of an algorithm interpreting images for a human.

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

This information is not provided, and based on the device description as a diagnostic ultrasound system, a standalone "algorithm only" performance would not be the primary focus for its type of application.

7. The type of ground truth used

This is not explicitly stated in relation to a clinical performance study. For the verification and validation, the "ground truth" would generally be established by engineering specifications, validated test methods, and potentially physical phantoms or established reference measurements for image quality, functional performance, and safety parameters.

8. The sample size for the training set

The document does not mention a "training set," which would typically be relevant for machine learning or AI-driven devices. This device is presented as an ultrasound system, not an AI model that requires a training set in the typical sense.

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

As no training set is mentioned, information on how its ground truth was established is not provided.

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