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The Diaqnostic Ultrasound System Aplio 500 Model TUS-A500, Aplio 400 Model TUS-A400 And Aplio 300 Model TUS-A300 is indicated for the visualization of structures, and dynamic processes with the human body using ultrasound and to provide image information for diaqnosis in the following clinical applications: fetal, abdominal, intra-operative (abdominal), pediatric, small orqans, trans-vaqinal, trans-rectal, neonatal cephalic, adult cephalic, cardiac (both adult and pediatric), peripheral vascular, transesophaqeal, musculo-skeletal (both conventional and superficial) and laparoscopic.

The Aplio 500 Model TUS-A500, Aplio 400 Model TUS-A400 and Aplio 300 Model TUS-A300 are mobile diagnostic ultrasound systems. These systems are Track 3 devices that employ a wide array of probes including flat linear array, convex linear array, and sector array with frequency ranges between approximately 2 MHz to 12 MHz.

Here's an analysis of the acceptance criteria and study information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical "acceptance criteria" for the performance of the device's specific features. Instead, it describes performance testing conducted to demonstrate that the new and improved features performed as intended or equivalently to manual methods. The acceptance is based on demonstrating the functionality and clinical validity of the features.

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

The document provides very limited detail on sample sizes and data provenance:

• Shear Wave Measurement (Elasticity Phantom): Test set size is not specified, only "known targets within an elasticity phantom." Data provenance is not specified beyond "bench testing."
• Smart 3D Feature: Test set size is not specified, performed using "existing 2D transducers." Data provenance is not specified.
• Clinical Evaluation (Shear Wave): "Representative clinical images of volunteers" were used. The number of volunteers is not specified. Data provenance (e.g., country of origin, retrospective or prospective) is not specified.
• Auto-NT Feature: The study "validated the performance of the Auto NT feature by demonstrating that the tool is able to equivalently measure Nuchal Translucency thickness when compared to manual measurement." The sample size for this study (number of cases or measurements) is not specified. Data provenance (e.g., retrospective or prospective) is not specified.

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

The document does not provide information on the number of experts, their qualifications, or how ground truth was established for the clinical evaluations (e.g., for the Auto-NT feature or clinical Shear Wave results).

• For the Shear Wave Measurement (Elasticity Phantom), the ground truth was "known targets within an elasticity phantom," implying a controlled, manufactured standard rather than expert interpretation.

4. Adjudication Method for the Test Set

The document does not specify any adjudication methods (e.g., 2+1, 3+1, none) for any of the performance tests.

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

The document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was performed to assess how much human readers improve with AI vs. without AI assistance. The clinical evaluation for Auto-NT compares the tool's measurement to manual measurement, but it's not structured as an MRMC study to show reader improvement.

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

The document implies standalone performance testing for some features:

• Shear Wave Measurement (Elasticity Phantom): This appears to be a standalone test of the algorithm's measurement accuracy against a known physical standard.
• Smart 3D Feature: The testing confirmed the feature "successfully displayed 3D images using existing 2D transducers," suggesting standalone functional validation.
• Auto-NT Feature: The validation compares the tool's measurement to manual measurement, which hints at standalone algorithm performance assessment.

The document does not explicitly use the term "standalone performance" but the descriptions of these tests align with the concept.

7. The Type of Ground Truth Used

• Shear Wave Measurement (Elasticity Phantom): "known targets within an elasticity phantom" (physical phantoms with measurable properties).
• Smart 3D Feature: Implicitly, the ground truth is the expected correct 3D image reconstruction from 2D data, likely evaluated visually for correct render and correspondence.
• Clinical Evaluation (Shear Wave): For "numerical Shear wave results in vivo," the ground truth is clinical acceptability, presumably against a clinician's expectation or established medical practice, though not explicitly defined (e.g., pathology, outcomes, expert consensus).
• Auto-NT Feature: "manual measurement" as the comparator for equivalence.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size for a training set. Given that this is a 510(k) submission for an ultrasound system with updated features (rather than a de novo AI/ML medical device submission), details about training data for specific "tools" or "features" are not typically as extensively detailed unless they represent a novel AI component requiring such validation. The features mentioned, while advanced (e.g., Auto-NT, Smart 3D), might rely on established signal processing or image reconstruction algorithms rather than deep learning models requiring extensive training data.

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

Since no information about a training set is provided, there is also no information on how ground truth for any potential training set was established.

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The Diaqnostic Ultrasound System Aplio 500 Model TUS-A500, Aplio 400 Model TUS-A400 And Aplio 300 Model TUS-A300 is indicated for the visualization of structures, and dynamic processes with the human body using ultrasound and to provide image information for diaqnosis in the following clinical applications: fetal, abdominal, intra-operative (abdominal), pediatric, small orqans, trans-vaqinal, trans-rectal, neonatal cephalic, adult cephalic, cardiac (both adult and pediatric), peripheral vascular, transesophaqeal, musculo-skeletal (both conventional and superficial) and laparoscopic.

The Aplio 500 Model TUS-A500, Aplio 400 Model TUS-A400 and Aplio 300 Model TUS-A300 are mobile diagnostic ultrasound systems. These systems are Track 3 devices that employ a wide array of probes including flat linear array, convex linear array, and sector array with frequency ranges between approximately 2 MHz to 12 MHz.

This document describes the 510(k) premarket notification for the Toshiba Medical Systems Corporation's Aplio 500/400/300 V5.0 Diagnostic Ultrasound System. The submission is a modification of a previously cleared device (K133761) and includes improvements to image quality, existing features (Needle Enhancement), and new features (Shear wave and Smart Navigation) along with four new transducers.

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of quantitative acceptance criteria with reported device performance values. Instead, it describes "three studies" conducted to demonstrate that the new features perform as intended. The "reported device performance" is qualitative, indicating successful demonstration.

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

The document states that "three studies were conducted," and the testing involved "bench testing" and "an elasticity phantom." It also mentions "targets at various depths." However, it does not specify numerical sample sizes for the test sets used in any of these studies.

The data provenance is from bench testing, implying controlled laboratory conditions rather than clinical human data. The country of origin of the data is not explicitly stated, but the submission is from "Toshiba Medical Systems Corporation, Shimoishigami Otawara-shi, Tochigi-ken, Japan 324-8550". It's likely the testing occurred in Japan or the US (as part of Toshiba America Medical Systems, Inc.). The studies are prospective in nature, as they were conducted to validate the new features of the device before market clearance.

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

The document does not specify the number of experts used or their qualifications for establishing ground truth for the bench testing.

For the shear wave study, "known targets within an elasticity phantom" were used. This indicates a physical phantom with pre-defined or known elastic properties, which serves as the ground truth. Therefore, expert input for ground truth establishment might have been less relevant or was built into the design of the phantom itself.

4. Adjudication Method for the Test Set

The document does not mention any adjudication method (e.g., 2+1, 3+1, none) for the test set results. The testing described appears to be objective performance evaluation based on physical measurements (e.g., accurately measuring shear wave speed) or functional confirmation (improving needle visualization, successful navigation).

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not mentioned in this document. The studies described are focused on the technical performance of new features rather than comparing human reader performance with and without AI assistance.

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

The performance of the "Needle Enhancement" and "Shear wave" features likely involves standalone measurements by the algorithm/system, as they are inherent imaging modes or measurement capabilities. For example, "accurately measured the shear wave speed and elasticity of known targets within an elasticity phantom" implies an algorithm-only measurement. The "Smart Navigation" feature "successfully navigates needles to targets" where the system displays a "projected path," which could be interpreted as the algorithm providing guidance that a human then follows. While not explicitly stated as "standalone evaluation," the nature of these tests suggests measuring the direct output of the device's enhanced functionalities.

7. The Type of Ground Truth Used

The types of ground truth used for the specific performance tests are:

• Needle Enhancement: Implicitly, the visibility of a physical needle in an ultrasound image. The ground truth for "improved visualization" would be a qualitative assessment, possibly against a baseline or a known ideal.
• Shear Wave: "Known targets within an elasticity phantom." This indicates a physical phantom with established mechanical properties (e.g., known elasticity values).
• Smart Navigation: "Targets at various depths" with a "projected path displayed on the system." The ground truth here would be the physical location of the targets and the actual path the needle takes compared to the projected path.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size for a training set. This is not uncommon for 510(k) submissions, especially for ultrasound systems where "AI" might refer to advanced image processing algorithms rather than deep learning models requiring large training datasets. The focus here is on the functional performance validation of hardware and software features.

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

Since no training set is mentioned (see point 8), there is no information on how ground truth for a training set was established.

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The Diagnostic Ultrasound System Aplio 500 Model TUS-A500, Aplio 400 Model TUS-A400 and Aplio 300 Model TUS-A300 is 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, trans-vaginal, trans-rectal, neonatal cephalic, adult cephalic, cardiac (both adult and pediatric), peripheral vascular, transesophageal, and musculo-skeletal (both conventional and superficial).

The Aplio 500 Model TUS-A500, Aplio 400 Model TUS-A400 and Aplio 300 Model TUS-A300 are mobile diagnostic ultrasound systems. These systems are Track 3 devices that employ a wide array of probes including flat linear array, convex linear array, and sector array with frequency ranges between approximately 2 MHz to 12 MHz.

This is a 510(k) premarket notification for modifications to an ultrasound system, not for an AI device. The document describes the device as the "Aplio 500 Model TUS-A500, Aplio 400 Model TUS-A400 and Aplio 300 Model TUS-A300" diagnostic ultrasound systems. The submission is for "Modification of a cleared device" that "improves upon existing features including the image visualization of blood flow."

Therefore, the prompt's request for "acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of an AI device, along with details like "sample size used for the test set," "number of experts used to establish the ground truth," "adjudication method," "MRMC comparative effectiveness study," "standalone performance," and "training set," is not applicable to this document.

The document does not describe an Artificial Intelligence (AI) / Machine Learning (ML) enabled device. It is a traditional medical device modification.

Here's what can be extracted regarding performance testing, although it's not in the context of AI acceptance criteria:

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

This document does not provide specific quantitative acceptance criteria or detailed performance metrics in the format typically seen for AI device evaluations. The submission states:

• Acceptance Criteria (Implicit): The device modifications meet the requirements for improved/added features. The device is safe and effective for its intended use.
• Reported Device Performance: The modifications improve existing features, specifically "the image visualization of blood flow." The document also lists the various clinical applications and modes of operation for which the system and its transducers are indicated (e.g., Fetal, Abdominal, Cardiac, Peripheral Vascular, etc., and B-mode, M-mode, PWD, CWD, Color Doppler, etc.). However, it does not provide quantitative results like sensitivity, specificity, or image quality scores for these improvements or listed functionalities, as would be expected for an AI device.

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

• Sample Size: Not specified for any test set.
• Data Provenance: "acquisition of representative clinical images" was conducted as part of the testing. No country of origin is mentioned, and it is implied to be retrospective, as the images are "acquired."

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

• Not applicable as this is not an AI device submission requiring expert human ground truth for algorithm performance evaluation. Testing involved "bench testing and the acquisition of representative clinical images."

4. Adjudication method for the test set:

• Not applicable as this is not an AI device submission requiring adjudication of human expert annotations or 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:

• No MRMC comparative effectiveness study was done, as this is not an AI device.

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

• Not applicable, as this is not an AI device.

7. The type of ground truth used:

• For the "acquisition of representative clinical images", the ground truth is implicitly the clinical reality captured by the ultrasound imaging, verified by standard clinical interpretation and potentially other diagnostic methods. However, the document does not elaborate on how this "ground truth" was formally established or used to evaluate the new features of the device (like improved blood flow visualization) beyond stating that the features met requirements.

8. The sample size for the training set:

• Not applicable, as this is not an AI device and thus has no training set in the AI/ML sense.

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

• Not applicable, as this is not an AI device and thus has no training set.

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The DIAGNOSTIC ULTRASOUND SYSTEM APLIO 500 MODEL TUS-A500, APLIO 400 MODEL TUS-A400 and APLIO 300 MODEL TUS-A300 is 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, trans-vaginal, trans-rectal, neonatal cephalic, adult cephalic, cardiac (both adult and pediatric), peripheral vascular, transesophageal, and musculoskeletal (both conventional and superficial).

The DIAGNOSTIC ULTRASOUND SYSTEM APLIO 500 MODEL TUS-A500. APLIO 400 MODEL TUS-A400 and APLIO 300 MODEL TUS-A300 are mobile system. These systems are Track 3 devices that employ a wide array of probes that include flat linear array, convex linear array, and sector array with a frequency range of approximately 2 MHz to 12 MHz.

The provided text is a 510(k) summary for the Toshiba DIAGNOSTIC ULTRASOUND SYSTEM APLIO 500 MODEL TUS-A500, APLIO 400 MODEL TUS-A400, and APLIO 300 MODEL TUS-A300 Version 2.1. It primarily focuses on demonstrating substantial equivalence to a predicate device (Toshiba DIAGNOSTIC ULTRASOUND SYSTEM APLIO 500 MODEL TUS-A500 / APLIO 400 MODEL TUS-A400 / APLIO 300 MODEL TUS-A300 V2.0; 510(k) control number K110870) and outlining the intended uses for various transducers.

Based on the provided document, the information requested in your prompt about acceptance criteria and a study proving performance (specifically in the context of AI/ML or new clinical parameters) is not present. This document predates the widespread regulatory submissions for AI/ML devices in medical imaging, and its focus is on general ultrasound system functionality and traditional clinical applications.

Therefore, I cannot provide a table of acceptance criteria and reported device performance for AI features, nor can I provide details about sample sizes for test/training sets, data provenance, number/qualifications of experts, adjudication methods, MRMC comparative effectiveness studies, or standalone algorithm performance studies related to AI.

The document does include detailed tables for each transducer showing the "Intended Use: Diagnostic ultrasound imaging or fluid flow analysis of the human body as follows:", listing various clinical applications and modes of operation. For each application, it indicates whether it's a "P" (previously cleared by FDA) or "E" (added under this appendix) indication, or "N" (new indication) for a very few. This is essentially a declaration of the intended uses, but it does not specify performance acceptance criteria or provide study results to demonstrate performance for these applications.

The document mentions compliance with several standards, such as IEC 60601-1 and its parts, IEC 62304, and AIUM-NEMA UD2/UD3 standards. These are general safety and performance standards for medical electrical equipment and ultrasound output measurement/display. They are compliance standards, not specific acceptance criteria for AI or diagnostic performance in patient studies.

In summary, the provided content is a regulatory submission for device clearance based on substantial equivalence to a predicate device, focusing on intended uses and compliance with general safety and performance standards. It does not contain the kind of detailed information about acceptance criteria and performance studies you're asking for, particularly concerning AI or specific diagnostic efficacy metrics.

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