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The Pocket Ultrasound System is intended for diagnostic ultrasound imaging in B (2D), Color Doppler, Combined (B+M), and Pulsed Wave modes. It is indicated for diagnostic ultrasound imaging and fluid flow analysis in the following applications depending on different configuration of array probes:

Convex array probe - Fetal/Obstetrics, Gynecological, Abdominal, Urology
Linear array probe - Small Parts (breast, thyroid), Carotid, Peripheral Vessel, Muscular-Skeletal, Pediatric
Phase array probe - Cardiology, Cardiac Fetal Echo.

The Pocket Ultrasound System is a transportable ultrasound system intended for use in environments where healthcare is provided by healthcare professionals.

The Pocket Ultrasound System (Model: C10, C10TX, C10RLpro) is designed to be a portable, general-purpose, software-controlled, diagnostic ultrasound system used to acquire and display highresolution, real-time ultrasound data through an off-the-shelf (OTS) Android device. the system mainly includes the ultrasound equipment and software application components. The ultrasound equipment is used to complete the ultrasonic imaging function and the software application components are used to complete the image display, measurement, patient information management as well as image storage etc. The Pocket Ultrasound System comprises a series of wireless transducers employing Wi-Fi-based technology to communicate with smartphone devices via direct Wi-Fi. One of the software application components is a software APP (MY USG), which is deployed on Android devices which supported Wi-Fi wireless communication. The software contained in the ultrasound equipment to control operation of the equipment and transfer images via Wi-Fi to the smartphone devices. This allows the user to export ultrasound images and display them across a range of portable personal devices. The communication protocol between the software APP and the other software contained in the equipment is specially defined for unique connection.

The system is only intended for use by qualified healthcare practitioners (e.g., doctors, nurses, sonographers) who are trained in the use of ultrasound imaging technology. The Pocket Ultrasound System comprises the following:

• . A commercial off-the-shelf (COTS) Android smartphone/tablet device.
• . the software application (MY USG) running as an App on the COTS device with Android system.
• . The ultrasound equipment/Transducers: C10 / C10TX / C10RL / C10RLpro
  • The 4 models have no difference in composition and performance parameters, only the appearance and battery capacity are different.
• Accessory: Wireless Battery Charger & Power Adapter

Different type transducers are available for the Pocket Ultrasound System (Model: C10, C10TX, C10RL, C10RLpro), the frequencies are 2.5 MHz, 3.2 MHz and 7.5MHz respectively depending on different transducer. The Pocket Ultrasound System is designed with four ultrasound modes of operation, including B-mode, Combination Mode (B+M mode), Color Doppler mode and Pulsed Wave Doppler mode.

Real time image and dynamic changes of tissues, organs or vessels will be showed in the image under Bmode, while coloring blood flow can be seen under Color Doppler mode and Pulsed Wave Doppler mode. Combination mode B+M-mode is the combination of the B-mode and M-mode of operation, superimposed on the display. M-mode is defined as time motion display of the ultrasound wave along a chosen ultrasound line, so it's usually applied in diagnosis of cardiovascular diseases.

The system provides real-time images for tissues, organs or blood flow via touch screen of the COTS Android smartphone/tablet device, which, in combination with the embedded software system contained in the ultrasound device, provides users a friendly and convenient graphical interface and makes users be able to easily control the whole system.

This document is a 510(k) summary for the Pocket Ultrasound System, focusing on demonstrating substantial equivalence to a predicate device. It does not contain the detailed information necessary to answer all the questions regarding acceptance criteria and a study proving the device meets those criteria, particularly in the context of an AI-powered medical device where performance characteristics beyond basic technical specifications (like diagnostic accuracy metrics) would be rigorously evaluated.

The document primarily addresses the substantial equivalence of the new models (C10TX, C10RL, C10RLpro) to the existing predicate device (C10). The testing mentioned (battery capacity, pigment safety) relates to minor differences in appearance and battery capacity, not to the diagnostic performance or AI-related capabilities.

Therefore, many of your questions cannot be answered from the provided text. However, I can extract the information that is present:

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

The document does not explicitly present a table of acceptance criteria and reported diagnostic device performance (e.g., accuracy, sensitivity, specificity for specific pathologies). Instead, it focuses on demonstrating equivalence in technical parameters and safety standards to the predicate device. The "acceptance criteria" presented are implicitly met by showing substantial equivalence in the following areas:

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 states, "Three new models are added in this submission (C10TX / C10RLpro). The new models have no difference in composition and performance parameters from the original model, only the appearance and battery capacity are different. We have tested two types of batteries with different capacities to ensure that they are both qualified. Different models of devices have different colors in appearance, and safety tests have been conducted on the pigments that form different colors to ensure that they can come into contact with human skin."

This implies the "testing" was primarily concerned with the new hardware variations (battery, appearance) and their safety, not a clinical performance study using patient data. Therefore, details about a "test set" in the context of diagnostic performance (sample size, data provenance) are not provided. This is common for a 510(k) if the changes are truly minor and do not impact core performance.

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/Not provided. The testing described is on hardware components (battery, pigments), not on diagnostic performance data requiring expert ground truth.

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

Not applicable/Not provided. As above, no clinical test set for diagnostic performance 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

Not applicable/Not provided. This device is a "Pocket Ultrasound System" and the documentation does not mention any AI capabilities or human-in-the-loop studies. It describes a diagnostic ultrasound imaging system, not an AI-assisted diagnostic tool.

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

Not applicable/Not provided. No indication of an AI algorithm being present or evaluated in standalone mode.

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

Not applicable/Not provided. For a simple hardware change 510(k), clinical ground truth for diagnostic imaging is typically not required if substantial equivalence in performance is being claimed based on technical specifications and predicate performance.

8. The sample size for the training set

Not applicable/Not provided. The device does not appear to be an AI/ML device that requires a training set for model development.

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

Not applicable/Not provided. As above, no training set for AI/ML is mentioned.

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