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Digital Ultrasonic Diagnostic Imaging System (Model: F6) is a general use ultrasound device intended for use by appropriately qualified and trained healthcare professionals for ultrasound imaging, measurement, display and fluid flow analysis of the human body. Specific clinical applications and exam types include: Abdominal, Obstetrics, Gynaecology, Small Parts (breast, thyroid, etc.), Peripheral Vascular, Urology. Modes of operation include B mode, M mode, Color Doppler, Power Doppler, PWD and Combined (B+M; B+CD; B+PWD). This system is suitable for use in hospital and clinical settings.

The Digital Ultrasonic Diagnostic Imaging System is designed to be portable notebook ultrasound systems and mainly include the ultrasound equipment, transducer and adapter. The equipment to complete the ultrasonic imaging function, host completes the image display, measurement, patient information, image storage, and other applications Standard keyboards, touch panels, and buttons, as well as multifunctional editors, mainly control device operations. The display screen is used to display work interfaces, images, and parameters. Different interfaces are used to connect transducers, other devices, or power supplies. There are four different types of transducers, namely large convex, linear array, Micro convex, Intracavity, the frequency scope of different transducers is from 2.5MHz to 11MHz. The system provides a variety of modes for flexibly selecting, which include B-mode, M-mode, Color-mode, Power-mode, PW-mode, and combined modes (i.e., B/M- mode). Real time image and dynamic changes of tissues, organs or vessels will be showed in the image under B-mode, while coloring blood flow can be seen under Color-mode, PW-mode or Power-mode. M-mode is defined as time motion display of the ultrasound wave along a chosen ultrasound line. The system not only provides real-time tissues, organs or blood flow images, but also contains measurement of anatomical structures and analysis packages, which can offer diagnosticians detailed and useful information.

The provided document is a 510(k) Premarket Notification from the FDA for a Digital Ultrasonic Diagnostic Imaging System (Model: F6). This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving the device meets specific acceptance criteria through a dedicated study with performance metrics.

Therefore, the document does not contain the detailed information requested regarding acceptance criteria and a study proving the device meets them in the way a clinical trial or performance study report would.

However, based on the information provided, here's what can be extracted and inferred:

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

The document doesn't present a formal table of acceptance criteria and reported device performance. Instead, it relies on demonstrating substantial equivalence to predicate devices, implying that the acceptance criteria are met if the device's technical characteristics and performance are comparable or don't raise new safety/effectiveness concerns.

The "Test Summary" section (6.1 Summary of Non-Clinical Tests) lists various international and FDA guidance standards that the device has been evaluated against. Meeting these standards is an implicit "acceptance criterion" for safety, electromagnetic compatibility, and basic performance.

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 explicitly states: "No clinical study is included in this submission." Therefore, there is no information about a test set sample size or its provenance in terms of patient data. The "tests" mentioned are non-clinical (bench testing) against standards.

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)

Since there was no clinical study, there is no mention of experts establishing ground truth for a test set.

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

Not applicable as there was no clinical test set.

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. The device is a "Digital Ultrasonic Diagnostic Imaging System," which is a standalone imaging device, not an AI-assisted diagnostic tool that would involve human readers and comparative effectiveness studies.

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

This question typically applies to AI/software as a medical device. The device in question is a diagnostic ultrasound system. Its performance is assessed through its ability to generate images, measure, display, and analyze fluid flow, which is its inherent standalone function. The non-clinical tests listed serve to prove its performance in this standalone capacity by meeting established safety and performance standards relevant to ultrasound technology.

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

For the non-clinical tests, the "ground truth" would be the specifications and requirements outlined in the referenced standards (e.g., specific acoustic output limits, cytotoxicity thresholds, electromagnetic compatibility levels).

8. The sample size for the training set

Not applicable. This device is a diagnostic ultrasound system, not an AI/machine learning model that typically requires a training set of data.

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

Not applicable, as it's not an AI/ML device.

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The SONON Ultrasound Imaging System (Model: SONON 500L) is intended for diagnostic ultrasound echo imaging, measurement, and analysis of the human body for general clinical applications including musculoskeletal (MSK), vascular, small parts (breast, thyroid), and thorax (thoracic/pleural motion and fluid detection imaging).

SONON 500L provides 4 modes: B (2D) mode, CF (Color Flow) mode, PW (Pulsed Wave) mode, M (Motion) mode.

SONON 500L is suitable for use in professional healthcare environment (hospital, clinic and medical office settings) by appropriately trained healthcare professional.

The SONON Ultrasound Imaging System, Model: SONON 500L, is a wireless ultrasound system that uses pulsed-echo / Doppler technology (Color Flow Doppler (CF Mode) / Brightness mode(2D B Mode) / Pulsed wave mode(PW Mode) / Motion mode(M mode); frequency: 4 MHz – 13MHz; module: linear; depth max: 6 cm) to transmit ultrasound images via wireless communication to a mobile device that utilizes the iOS or Android, or PC that uses Windows operating system.

The minimum requirements for the mobile devices that utilize the iOS, Android or Windows operating system for use with the SONON Ultrasound Imaging System, Model: SONON 500L are as follows:

Operating system versions

• iOS: iOS 11.0 or later
• Android: Android 5.0 (Lollipop) or later
• Windows (UWP): Windows 10 (64-bit) or later

Minimum specifications for mobile devices

• CPU: 1.7GHz
• Core: 8 cores
• RAM: 4GB
• Resolution: 2560 X 1600 (287ppi)

The SONON Ultrasound Imaging System is a TRACK 3, portable, general-purpose, software-controlled, hand-held diagnostic ultrasound system that consists of
(i) a commercial off-the-shelf iOS or Android mobile device or Windows PC
(ii) the SONON Ultrasound Imaging System software that runs as an app on the mobile device,
(iii) the battery-operated, hand-held SONON Ultrasound Imaging System transducer that communicates wirelessly with iOS or Android mobile devices or Windows PC and
(iv) the instructions for use manual, battery, charger, and power cords.

The mobile application of SONON 500L, which controls the probe, imaging capabilities and functionalities, can be downloaded to an iOS, Android mobile device or Windows PC and utilizes an icon touch-based user interface. The software enables ultrasound image capture and review, controls for time gain, dynamic range, display of mirror image, focal length, depth, brightness, contrast, linear/elliptical measurement, and image annotation, as well as storage and PACS transmission of images and videos. The SONON Ultrasound Imaging System allows the user to image in real time and review cine or freeze-frame images on the screen in a B-Mode with 2-dimensional scan format. The software also allows the user to save / edit patient and diagnosis information. An identification and password are required for accessing the SONON 500L app via the mobile device or PC. All information captured is saved in the app. If the app is removed and reinstalled, all stored information is lost and cannot be recovered.

The SONON ultrasound device utilizes pulsed-echo technology to determine the depth and location of tissue interfaces. Ultrasound imaging requires mechanical oscillation of crystals excited by electrical pulses, generating a piezoelectric effect. A number of these crystals make up a transducer, which converts one type of energy into another. Using pulse-echo transformation by the piezoelectric crystals, an ultrasound transducer converts electricity into sound.

The SONON ultrasound device measures the duration of an acoustic pulse travelling from the transmitter to the tissue interface and back to the receiver. Ultrasound waves emitted from the transducer propagate through various tissues and return to the transducer as reflected echoes. These echoes are then converted into high-frequency electrical signals by the crystals in the transducer. Next, the signals are amplified and further processed by several analog and digital circuits and software filters to adjust the frequency and time response, in order to finally generate a series of digital images.

The device components are not supplied sterile and do not require sterilization prior to use.

Here's a breakdown of the acceptance criteria and the study information for the SONON Ultrasound Imaging System (Model: SONON 500L), based on the provided FDA 510(k) Clearance Letter.

Disclaimer: This document is a 510(k) summary, which generally provides limited detail on specific performance acceptance criteria and study methodologies compared to a full submission. Therefore, some information might be missing or generalized. The document primarily focuses on demonstrating substantial equivalence to predicate devices rather than proving a novel device's performance against clinical endpoints.

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly list specific quantitative acceptance criteria (e.g., minimum accuracy, sensitivity, or specificity values) or detailed reported device performance against such metrics. Instead, it relies on demonstrating compliance with recognized standards and established engineering practices, as well as showing substantial equivalence to predicate devices.

The acceptance criteria are generally framed around compliance with safety and performance standards and equivalency to predicate devices.

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

The document does not describe a "test set" in the context of clinical image data for evaluating diagnostic performance (e.g., sensitivity, specificity). The studies mentioned are primarily engineering verification and validation tests and compliance with standards.

• Sample Size for performance evaluation: Not applicable in the context of a diagnostic performance test set composed of patient data. Performance tests were bench tests using unspecified samples/units of the device.
• Data Provenance: Not applicable as no clinical image data set for diagnostic performance evaluation is described. The document indicates that no clinical studies or animal studies were conducted to support substantial equivalence.

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

Not applicable. As noted above, no clinical studies with a diagnostic "test set" requiring expert-established ground truth were conducted.

4. Adjudication Method for the Test Set

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

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 done. The document explicitly states: "The subject of this premarket submission, SONON Ultrasound Imaging System (Model: SONON 500L), requires no clinical studies to support substantial equivalence." This implies there was no study comparing human readers with and without AI assistance for improved effectiveness.

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

Not applicable. The SONON Ultrasound Imaging System is a hardware/software medical device that produces images for human interpretation. It is not an AI algorithm that provides a standalone diagnostic output. Therefore, an "algorithm only" performance would not be relevant in this context.

7. The Type of Ground Truth Used

For the engineering and safety tests, the "ground truth" was based on:

• Established standard requirements (e.g., ISO 10993 for biocompatibility, IEC 60601 series for electrical and ultrasound safety, FDA acoustic output limits).
• Design specifications and functional requirements of the device itself (for "basic performance and the output of the ultrasound equipment").

No pathology, expert consensus on clinical images, or outcomes data were used as ground truth, as no clinical studies were performed.

8. The Sample Size for the Training Set

Not applicable. This device is an ultrasound imaging system, not an AI model that requires a "training set" of data for learning a diagnostic task. The software validation mentioned refers to the development and testing of the device's operating software, not an AI inference model.

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

Not applicable, as there is no "training set" for an AI model described. The software validation ensures that the device's software functions as intended and meets its specifications, rather than learning from data.

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