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The SONON Ultrasound Imaging System (Model: SONON 300C) is intended for diagnostic ultrasound echo imaging, measurement, and analysis of the human body for general clinical applications including obstetrics (OB), gynecology (GY) and general (abdominal) imaging.

The SONON Ultrasound Imaging System, Model: SONON 300C, is a wireless ultrasound system that uses pulsed-echo technology (frequency: 3.5 MHz; module: convex) to transmit ultrasound images via wireless communication to a mobile device that utilizes the iOS or Android operating system.

The SONON Ultrasound Imaging System is a portable, general-purpose, software-controlled, hand-held diagnostic ultrasound system that consists of (i) a commercial off-the-shelf iOS or Android mobile device. (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, and (iv) the instructions for use manual, battery, charger, and power cords.

The SONON software can be downloaded to an iOS or Android mobile device 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, brightness, contrast, linear/elliptical measurement, and image annotation, as well as storage and email 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. 2dimensional scan format. All images and data collected are stored in the mobile app. If the app is removed and reinstalled, all stored information is lost and cannot be recovered.

The SONON Ultrasound Imaging System utilizes pulsed-echo technology to determine the depth and location of tissue interfaces, and to measure the duration of an acoustic pulse from the transmitter to the tissue interface and back to the receiver. Ultrasound waves are emitted from the transducer, propagate through tissues, and return to the transducer as reflected echoes. The returned echoes are then converted into electrical impulses by transducer crystals and further processed in order to form the ultrasound image presented on the screen.

The provided text describes a 510(k) submission for the Healcerion Co., Ltd. SONON Ultrasound Imaging System (Model: SONON 300C). This document focuses on demonstrating substantial equivalence to predicate devices rather than proving a device meets specific acceptance criteria through a dedicated study with performance metrics like accuracy, sensitivity, or specificity.

Therefore, many of the requested details about acceptance criteria, specific performance metrics, sample sizes for test and training sets, expert qualifications, and ground truth establishment are not explicitly provided in this type of regulatory submission. This document highlights compliance with general safety and performance standards for ultrasound devices, but not the kind of detailed performance validation typically seen for AI/software-based diagnostics.

However, I can extract the information that is present:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not present acceptance criteria in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy) for a diagnostic task. Instead, it demonstrates compliance with recognized safety and performance standards relevant to ultrasound imaging systems.

• Results (Convex only transducer):
  • Ispta.3: 0.0627[W/cm2]
  • TIS: 0.2535
  • MI: 0.7861
  • Ipa.3@MImax: 11.6369[W/cm2]
• Conclusion: Below Track 3 FDA limits in accordance with Sept. 2008 ultrasound systems guidance document. |
  | Clinical Measurement Range and Accuracies | - Test: Used a phantom with vertical, dead zone, horizontal, and linear groups.
• Evaluation: Length and vertical/horizontal resolutions.
• Conclusion: Results met performance criteria (specific criteria not detailed). |
  | Display Performance | - Test: Assessed using various mobile devices.
• Conclusion: Test results demonstrate that the device meets performance specifications (specific specifications not detailed). Display ranges from 4 to 10 inches, similar to predicate devices. |
  | Electrical Safety, EMC, RF Wireless | - Standards: IEC 60601-1, IEC 60601-1-2, IEC 60601-2-37, IEC 62359.
• Conclusion: Device complies with these international standards. |
  | Usability | - Process: Usability engineering process conducted to assess and mitigate risks from usability problems and user errors.
• Conclusion: Test results demonstrate the product is reasonably safe and effective for intended users, uses, and environments. |
  | Failure Mode and Risk Analyses | - Standard: ISO 14971.
• Process: FMEA and risk analysis performed. Hazards identified, risks estimated, and procedures implemented for control.
• Conclusion: All identified hazards were reduced to acceptable levels. |
  | Biocompatibility | - Standard: ISO 10993-1.
• Test: Patient-contacting surfaces (probe nosepiece and lens) evaluated for cytotoxicity, skin irritation, and skin sensitization.
• Conclusion: Test results demonstrate biocompatibility. |
  | Software Evaluation & Cybersecurity | - Standard: IEC 62304.
• Process: Validation and verification activities, cybersecurity evaluation.
• Conclusion: Software passed performance requirements and met specifications. |

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

• Sample Size for Test Set: Not specified for any clinical performance evaluation, as clinical tests were not conducted. For non-clinical tests like acoustic output or phantom studies, specific sample sizes (e.g., number of measurements or phantoms) are not provided.
• Data Provenance: Not applicable as clinical data was not used for the primary demonstration of substantial equivalence. The non-clinical tests would have been performed by the manufacturer.

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

• This information is not provided. No clinical "ground truth" was established for a test set, as no clinical studies were performed (see "Clinical tests and animal studies - not conducted" under section 7 of the 510(k) Summary).

4. Adjudication method for the test set

• Not applicable, as no clinical test set requiring expert adjudication was used.

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 study was performed. The device is an ultrasound imaging system, and the submission focuses on its equivalence to existing ultrasound systems, not on its role as an AI-powered diagnostic aid for human readers.

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

• No standalone algorithm performance study was done for diagnostic accuracy. The "algorithm" here refers to the software controlling the ultrasound device and image display, not a diagnostic AI algorithm.

7. The type of ground truth used

• For the non-clinical performance evaluations (acoustic output, measurement accuracy, display performance), the "ground truth" would be established by the physical properties of the testing equipment, phantoms, and calibrated measurement devices, according to the specified standards (e.g., NEMA UD2-2004 for acoustic output). There is no "ground truth" in terms of expert consensus, pathology, or outcomes data mentioned, as no clinical studies were conducted.

8. The sample size for the training set

• Not applicable. This device is an ultrasound imaging system, not a machine learning model that would require a "training set" for diagnostic algorithm development. The software development follows a software life-cycle process (IEC 62304) and includes verification and validation activities, but this is distinct from training a machine learning model.

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

• Not applicable, as no training set for a machine learning model was used.

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