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The system is intended to be used by a trained/qualified physician in a hospital or clinical setting for ultrasound evaluation of fetal, abdominal, pediatric, small organ (thyroid, testes, breast), neonatal cephalic, musculoskeletal (conventional and superficial), cardiac (adult and pediatric), peripheral vascular, trans-rectal, obstetrics/ gynecology and urology applications in B-Mode (B, 2B, 4B), M-Mode, CFM, CPA, DPA, PWD, CWD, Combined (B, M, CFM, CPA, DPA, PWD, XBeam, Panoscope), and others (3D, Trapezoidal /Extended Sector Imaging, Elastography, Anatomical M-mode, TDI, THI, MFI, ECG, VS Flow, Color M, DICOM).

The SIUI Apogee 1000 series (Apogee 1000/ Apogee 1000Neo/ Apogee 1000Lite/ Apogee 1000Exp/ Apogee 1000B/W/ Apogee 1 series (Apogee 1T/ Apogee 1T/ Apogee 1G) Digital Color Ultrasound Imaging System is capable of the following operating modes: B-Mode (B, 2B, 4B), M-Mode, CFM, CPA, DPA, PWD, CWD, Combined(B, M, CFM, CPA, DPA, PWD, CWD, XBeam, Panoscope), and others (3D, Trapezoidal /Extended Sector Imaging, Elastography, Anatomical M-mode, THI, TDI, DPA, MFI, ECG, VS Flow, Color M, DICOM). The system is designed for use in linear, convex, phased array scanning modes and supports linear, convex, phased array and endocavity (trans-vaginal and trans-rectal) transducers. The system has cine review, image zoom, measurements and calculations, image storage and review, printing and recording capabilities. This system is a Track 3 device and the software used in Apogee 1000 series / Apogee 1 series Digital Color Ultrasound Imaging System is Ultrasound Software by SIUI and is based on the predicate device. The Level of Concern for the Ultrasound Software is Moderate.

The provided document is a 510(k) summary for the SIUI Apogee 1000 series/Apogee 1 series Digital Color Doppler Ultrasound Imaging System. It describes the device, its intended use, and its comparison to a predicate device (SIUI Apogee 2300, K173000) to demonstrate substantial equivalence.

Based on the content, here's an analysis of the acceptance criteria and study information:

Description of Acceptance Criteria and Device Performance

The document does not explicitly state specific, quantifiable acceptance criteria (e.g., minimum sensitivity, specificity, or image quality metrics) for the device's performance. Instead, it relies on demonstrating substantial equivalence to a legally marketed predicate device (Apogee 2300, K173000) through compliance with recognized standards and a comparison of technological characteristics.

The "device performance" reported is the demonstration that the subject device (Apogee 1000 series/Apogee 1 series) is comparable to the predicate device in terms of:

• Intended Use: Both devices have the same intended use.
• Technological Characteristics: They share the same PC module, digital signal processing module, and ultrasound front-end module. The overall user interface is substantially unchanged.
• Functions: While some models of the subject device may lack the 4D imaging mode present in the predicate, they are similar in "other ultrasonic diagnostic functions, imaging modes, file storage management, compatible peripherals, measurement and calculation functions."
• Safety and Effectiveness: Non-clinical tests demonstrate that the subject device performs as well as, and is substantially equivalent with respect to safety and effectiveness of the predicate device.

Table of Acceptance Criteria and Reported Device Performance

Given the nature of the 510(k) submission for a diagnostic ultrasound system based on substantial equivalence, specific numerical acceptance criteria (like those for AI/CADe devices) are not typically presented in this format. The "acceptance criteria" here are implicitly meeting the regulatory requirements for substantial equivalence by demonstrating comparable technical characteristics and complying with relevant standards.

Study Details

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

   • The document describes non-clinical testing to demonstrate compliance with various international standards (e.g., electrical safety, EMC, software lifecycle, biological evaluation).
   • It does not mention a "test set" in the context of clinical image data or patient data. The evaluation is primarily based on engineering and performance standards applied to the device itself, rather than testing its diagnostic output against a specific dataset.
   • Data provenance is not applicable as this is not a study involving clinical data.

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

   • Not applicable. There is no mention of an external "test set" with ground truth established by experts. The "ground truth" implicitly comes from adherence to established engineering and medical device standards.

3. Adjudication method for the test set:

   • Not applicable. No "test set" or adjudication process described.

4. 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 comparative effectiveness study was not done.
   • This submission is for a general-purpose ultrasound imaging system, not an AI/CADe (Computer-Aided Detection/Diagnosis) device designed to assist human readers or perform automated analysis. Therefore, the concept of "human readers improve with AI vs without AI assistance" is not relevant to this submission.

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

   • No, a standalone algorithm performance study was not done.
   • This device is an imaging system, not an algorithm, and is intended for use by a "trained/qualified physician" (human-in-the-loop).

6. The type of ground truth used:

   • For the non-clinical testing, the "ground truth" is implied by the technical specifications outlined in the international standards (e.g., electrical parameters, mechanical safety, software functionality, biocompatibility). It's not a clinical ground truth like pathology or patient outcomes.

7. The sample size for the training set:

   • Not applicable. This is not an AI/ML device that requires a "training set."

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

   • Not applicable. No training set is mentioned.

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