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The Diagnostic Ultrasound Systems Aplio a550 Model CUS-AA550. Aplio a450 Model CUS-AA450, and Aplio a Model CUS-AA000 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, trans-vaginal, trans-rectal, neonatal cephalic, adult cephalic, cardiac (both adult and pediatric), peripheral vascular, transesophageal, musculo-skeletal (both conventional and superficial) and laparoscopic.

The Aplio a550 Model CUS-AA550, Aplio a450 Model CUS-AA450 and Aplio a Model CUS-AA000 are mobile diagnostic ultrasound 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 20 MHz.

This document describes the premarket notification (510(k)) for the Canon Medical Systems Corporation's Aplio a550, Aplio a450, and Aplio a Diagnostic Ultrasound Systems, V4.0. The submission aims to demonstrate substantial equivalence to a previously cleared predicate device (K182427).

Summary of Acceptance Criteria and Device Performance (Based on "Improvements to previously cleared functionality" table):

Detailed Information about the Study:

1. A table of acceptance criteria and the reported device performance:
   (See table above)
   The acceptance criteria are implied by the "Comments" column in the "Improvements to previously cleared functionality" table. The reported device performance is stated as "Improvement" for these features, along with specific benefits.

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

   • Sample Size: The document does not specify exact sample sizes (e.g., number of patients or images) for the "Bench Assessment" performance testing. It generally states that "testing conducted, which are included, demonstrates that the requirements for the features have been met" (Section 16).
   • Data Provenance: The document does not explicitly state the country of origin of the data or whether it was retrospective or prospective. Given that no clinical studies were required and the testing involved "Bench Assessment," it implies that the performance evaluation was likely conducted in a controlled, non-clinical environment, possibly using phantom or simulated data, or internally curated datasets.

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

   The document does not mention the use of experts to establish ground truth for the "Bench Assessment" performance testing. This type of testing typically relies on predefined technical specifications, simulation models, or controlled experimental setups rather than human expert consensus for ground truth.

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

   No adjudication method is described, as the testing appears to be "Bench Assessment" focusing on technical performance endpoints rather than human interpretation accuracy.

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. The document explicitly states: "No clinical studies were required to demonstrate safety and efficacy of the Aplio a550, Aplio a systems" (Section 16). The improvements listed are related to visualization, automation, and expanded applicability of existing modes, not diagnostic accuracy requiring human reader studies.

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

   The "Bench Assessment" performance testing described for features like "Doppler Luminance," "Auto E/A," "Measurement in volume rendering view," "Auto EF," "Stress Echo Color/Doppler," and "Vascularity Index" would fall under a form of standalone performance evaluation. This indicates that the functionalities were assessed directly against their technical specifications or expected outcomes.

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

   For the "Bench Assessment" performance testing, the ground truth would likely be based on:

   • Technical Specifications: Conformance to predefined performance parameters for image quality, measurement accuracy, or automation efficiency.
   • Expected System Behavior: Verification that the new or improved features function as designed, e.g., the 3D effect for Doppler Luminance, or calculation accuracy for Auto E/A.
     These are not clinical ground truths like pathology or expert consensus on a diagnosis.

8. The sample size for the training set:

   This document is for a 510(k) premarket notification for diagnostic ultrasound systems and does not contain information about "training sets" in the context of deep learning or AI. The listed improvements are functionality enhancements rather than new AI algorithms requiring extensive training data.

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

   As no training set (in the AI/deep learning context) is mentioned or relevant to the described updates in this submission, the method for establishing ground truth for a training set is not applicable and not provided. The changes are described as "modifications to a cleared device" and "improvements upon existing features" (Section 14).

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