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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

Feature: Auto LVOT

Feature: Auto AoV

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

• Data Provenance: All data used for performance testing was "entirely independent and sequestered from the data used for training and was acquired from U.S. clinical patients with the predicate device, identical to the subject device in terms of data acquisition functionality." The data was collected prospectively ("acquired over a two-month period at a U.S. clinical site") for these validation studies, or selected from previously acquired data, which implies it was retrospective in nature for the selection process. The data was from the USA.
• Sample Sizes for Test Sets:
  • Auto Plane Detection: 50 patients (images from 239 demographically diverse patients were acquired over a two-month period, but 50 were selected for this specific study.)
  • Quick Strain: 50 patients (same data acquisition pool of 239 patients, 50 selected for this study).
  • Auto LVOT: 45 patients (same data acquisition pool of 239 patients, 45 selected for this study).
  • Auto AoV: 45 patients (same data acquisition pool of 239 patients, 45 selected for this study).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

• For Auto Plane Detection: Three clinical sonographers. Their qualifications are described as "qualifications and expertise representative of U.S. intended users."
• For Quick Strain: Three licensed sonographers.
• For Auto LVOT: Three licensed sonographers.
• For Auto AoV: Three licensed sonographers.

The document states generally for all features that "Ground Truth was established by three clinical sonographers with qualifications and clinical experience representative of intended users of these features in the U.S." This implies they are experienced and licensed professionals in sonography.

4. Adjudication Method for the Test Set

• Auto Plane Detection: "A licensed sonographer selected representative images for each of the four evaluated chamber views (A4C/A3C/A2C/SAX) and two different licensed sonographers independently identified the cardiac view for all selected images, with any discrepancies resolved by consensus among the three." This is a 2+1 consensus method.
• Quick Strain, Auto LVOT, Auto AoV: For these features, ground truth was established by the "median of manual measurement results taken by three licensed sonographers." This is a 3-expert median method. No explicit mention of an adjudication process if there were significant outliers, but the use of the median inherently provides a robust central tendency.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study was not explicitly stated in the traditional sense of comparing human readers with vs. without AI assistance. The studies focused on comparing the AI/ML-based features' performance (e.g., accuracy, time savings, inter-operator variability) against the existing predicate functionality (manual methods).

The benefit derived is a workflow improvement (time savings) while maintaining equivalent performance, rather than an explicit improvement in diagnostic accuracy of the human reader with AI assistance. The AI features are described as automating or assisting parts of the process that were previously manual measurements or selections.

6. If a Standalone (i.e. algorithm only without human-in-the loop performance) Was Done

Yes, the studies clearly evaluate the standalone performance of the AI algorithms for Auto Plane Detection, Quick Strain, Auto LVOT, and Auto AoV. While the ground truth is established by human experts, the algorithms themselves are performing the tasks (e.g., selecting views, tracing waveforms, calculating metrics) and their output is compared against human expert derived ground truth or existing manual workflows.

7. The Type of Ground Truth Used

• Expert Consensus:
  • Auto Plane Detection: Expert consensus (3 sonographers) on the correct cardiac views.
  • Quick Strain, Auto LVOT, Auto AoV: Expert consensus measurements (median of 3 sonographers' manual measurements using the predicate method). This acts as the "gold standard" for quantitative comparison.

No pathology or outcomes data was used for ground truth.

8. The Sample Size for the Training Set

The document explicitly states: "The data used for the performance testing of these improved features was entirely independent and sequestered from the data used for training..." However, the sample size for the training set is not provided in the given text.

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

The document states that the testing data was sequestered from the training data, but it does not provide information on how the ground truth for the training set was established.

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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 pediatric), 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 Doppler, 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 33 MHz.

This FDA 510(k) summary describes a modification to an existing diagnostic ultrasound system, the Aplio i900/i800/i700 Diagnostic Ultrasound System, moving from Software V6.5 (predicate device K212333) to Software V7.0 (subject device K223017).

The documentation does not provide detailed acceptance criteria or a specific study proving the device meets those criteria in the way one might expect for a new AI/CADx device that targets a specific clinical outcome with clear metrics like sensitivity, specificity, or AUC. Instead, it focuses on demonstrating substantial equivalence to the predicate device by showing that the V7.0 software has the same intended use, fundamental scientific technology, and similar function, with improvements to existing features and functionality.

Therefore, the "acceptance criteria" here are primarily about meeting specified performance targets for the improved features and demonstrating that the overall system remains safe and effective for its indicated uses, similar to the predicate device. The "study" mentioned refers to the verification and validation activities and performance testing of these improved features.

Here's the breakdown of the information requested, based on the provided text:

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

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics for new clinical outcomes. The "acceptance criteria" are implied by the comprehensive testing and verification that the improved features function as intended and meet established specifications.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not specify a distinct "test set" in terms of patient data or image count. The testing conducted primarily involves verification and validation activities and performance testing of the improved features. This typically involves internal R&D testing environments, phantom studies, and potentially a limited number of clinical cases (likely internal and for feature validation rather than a large clinical study for a specific diagnostic accuracy claim).

• Sample Size: Not specified in terms of patient numbers or image volume.
• Data Provenance: Not specified. It's common for such V&V to occur internally, potentially using a mix of synthetic data, phantom data, and potentially a small set of retrospective clinical data for feature-specific validation.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. This submission focuses on software changes and feature improvements for an existing diagnostic ultrasound system. There is no mention of a clinical study that required expert-established ground truth for a test set to determine diagnostic accuracy. The "ground truth" for these types of V&V activities generally comes from engineering specifications, established physical principles, and clinical user feedback on the functionality of the improvements.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable. No mention of a clinical test set requiring expert adjudication for ground truth.

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

Not applicable. This submission is for a general diagnostic ultrasound system with feature improvements, not a specific AI/CADx device that provides diagnostic assistance to human readers in a comparative effectiveness study setting. The "AI" mentioned (e.g., Smart3D, SmartFetalHeart) refers to advanced processing and workflow-enhancing features of the ultrasound system, not typically external AI tools requiring MRMC studies for regulatory clearance in this context.

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

Not applicable. The device is a diagnostic ultrasound system intended for use by trained physicians, with human-in-the-loop performance inherent to its design and indications for use. No standalone algorithm performance is described.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not applicable for a typical clinical ground truth definition in this submission. The ground truth for the verification and validation of the improved features would be based on:

• Engineering specifications and design requirements.
• Physical measurements and phantom studies (e.g., for image quality, frequency range).
• Clinical utility and user feedback for workflow improvements.
• Referencing the performance of the predicate device for features that are simply being improved rather than fundamentally changed.

8. The sample size for the training set

Not applicable. This document describes improvements to existing software functions of a diagnostic ultrasound system. There is no indication of a new machine learning model being trained that would require a "training set" in the context of deep learning or similar AI/ML development. The "learning" for any adaptive features would be built into the algorithms through design and established parameters, not via large-scale training data in the AI sense.

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

Not applicable, as no dedicated training set is described.

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