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The ASU-1000 and Volume mode software, like other marketed diagnostic ultrasound systems and 3-D rendering packages, is indicated for imaging body structures in two and three dimensions to aid in the diagnosis of disease or abnormality.

Diagnostic Ultrasound imaging and Doppler analysis of the human body as follows: Fetal, Abdominal. Combined Modes: B/M, B/PWD. Other Indications or Modes: 3-D rendering of fetal and maternal structures in Obstetrics.

Diagnostic Ultrasound imaging and Doppler analysis of the human body as follows: Fetal, Abdominal. Combined Modes: B/M, B/PWD, M/CD, B/PWD/CD. Other Indications or Modes: 3-D rendering of fetal and maternal structures in Obstetrics.

This premarket notification has been submitted by Aloka Co., Ltd. and covers the Aloka ASU-1000 transducer and associated Volume Mode 3-D rendering software. The proprietary name for the transducer is the Aloka ASU-1000 diagnostic ultrasound transducer. The 3-D rendering software is "Volume Mode". The common name for this type of device is a diagnostic ultrasound transducer and associated accessories. The Aloka ASU-1000 and Volume mode software can be used on the Aloka SSD-1700 and SSD-1400 diagnostic ultrasound systems. These systems function in the same manner as other diagnostic ultrasound devices. High frequency sound waves are transmitted into the body by a piezo-electric transducer. In the body, differences in the acoustic impedance of different tissues reflect a certain amount of the ultrasound energy back to the transducer, where it is processed into slice images. The slice images are processed by the Volume Mode software into a 3-D surface rendering of the target tissues.

The provided text is a 510(k) summary for the Aloka ASU-1000 transducer and associated Volume Mode 3-D rendering software. It focuses on demonstrating substantial equivalence to previously marketed devices rather than presenting a performance study with specific acceptance criteria and detailed quantitative results.

Therefore, much of the requested information regarding an acceptance criteria table, detailed study design, sample sizes, expert involvement, and ground truth establishment cannot be extracted from this document as it does not contain such a study report.

Here's what can be inferred or stated based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

• No specific quantitative acceptance criteria or reported device performance metrics are explicitly stated in the document. The basis for clearance is "substantial equivalence" to existing devices, implying that its performance is considered comparable, not necessarily meeting a predefined quantitative threshold.
• The document lists several qualitative statements about how the device performs similarly to or is acceptable in comparison to other marketed devices. These can be considered the "performance" aspects being highlighted for substantial equivalence, though they are not numerical acceptance criteria.

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

• Not explicitly stated. The document does not describe a clinical study with a "test set" in the context of AI/algorithm performance. The clearance is based on substantial equivalence to predicate devices, implying that the established safety and effectiveness of the predicate devices are extended to this new device based on its similar design and performance characteristics.

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

• Not applicable. No such test set or ground truth establishment process is described in this document.

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

• Not applicable. No test set requiring adjudication is described.

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, an MRMC study was not done or reported. This filing precedes the widespread use of sophisticated AI in medical devices and focuses on hardware (transducer) and basic 3D rendering software, not AI-assisted interpretation.

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

• No, a standalone algorithm performance study was not done or reported. The "Volume Mode 3-D rendering software" is a post-processing tool that works with the transducer to create 3D images for human interpretation, not an autonomous diagnostic algorithm.

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

• Not applicable. No specific ground truth for performance evaluation of an algorithm is mentioned. The device's indications for use are "to aid in the diagnosis of disease or abnormality," implying that the final diagnosis (ground truth) would still be established by a clinician.

8. The sample size for the training set:

• Not applicable. This document describes a medical device seeking 510(k) clearance based on substantial equivalence, not a machine learning model developed with a training set.

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

• Not applicable. As per point 8, no training set for a machine learning model is mentioned.

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