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The device is intended for use by a qualified physician for the evaluation of soft tissue and blood flow in the clinical applications; Fetal; Abdominal; Pediatric, Small Organ (breast, testes, thyroid); Musculo-skeletal (Conventional); Musculo-skeletal (Superficial); Peripheral Vascular (PV); and Urology (including prostate).

minisono product is an ultrasound imaging system for medical diagnosis. This device is available for portable and tablet PC (Microsoft corp., Surface pro). Also, this innovative system platform provides optimal patient diagnosis workflow with the tablet PC, optimal image quality.

1. Signal Mode: B(2D) mode, M mode, Pulsed Wave Doppler(PWD) mode, Color Doppler, Power Doppler, Tissue Harmonic Imaging
2. Combination Mode: B/Color Doppler, B/PWD, B/Color Doppler/PWD

The Alpinion Medical Systems Co., Ltd. minisono is an ultrasound imaging system for medical diagnosis. It is intended for use by a qualified physician for the evaluation of soft tissue and blood flow in various clinical applications including Fetal, Abdominal, Pediatric, Small Organ (breast, testes, thyroid), Musculo-skeletal (Conventional and Superficial), Peripheral Vascular (PV), and Urology (including prostate).

This document does not contain details of a study with acceptance criteria and reported device performance in the format of a table, efficacy data, or diagnostic performance metrics typically associated with AI/ML devices. Instead, it is a 510(k) premarket notification for a medical device that argues for substantial equivalence to a predicate device, the E-CUBE i7 Diagnostic Ultrasound System (K153444).

Therefore, specific information regarding the acceptance criteria and study proving the device meets them, as requested in the prompt, cannot be fully extracted in the requested format for a diagnostic AI/ML device. However, based on the provided text, here is a summary of the information related to the device's validation and comparison to a predicate:

1. Table of Acceptance Criteria and Reported Device Performance

This document does not provide a table of acceptance criteria and reported device performance in the context of diagnostic accuracy or clinical efficacy for an AI/ML component. The submission focuses on substantial equivalence to a predicate device based on similar indications for use, technological characteristics, and compliance with safety standards.

The acceptance criteria for this submission appear to be demonstrating that the minisono system possesses the same fundamental scientific technology, intended use, and similar technological characteristics as the predicate device (E-CUBE i7), and that any differences do not raise new questions of safety or effectiveness.

The document lists the following as "reported device performance" indirectly by comparing features to the predicate:

The "acceptance criteria" here is primarily the demonstration of substantial equivalence to the predicate device in terms of intended use, technology, and compliance with safety standards, rather than a specific performance metric like sensitivity or specificity for a diagnostic algorithm.

2. Sample Size for the Test Set and Data Provenance

The document explicitly states: "The subject of this premarket submission, minisono, did not require clinical studies to support substantial equivalence."

Therefore, there is no test set, sample size, or data provenance related to clinical performance or diagnostic accuracy provided. The evaluation was based on non-clinical tests demonstrating biocompatibility, acoustic output, and thermal, electrical, electromagnetic, and mechanical safety according to applicable medical device safety standards.

3. Number of Experts and Qualifications for Ground Truth

Since no clinical studies were conducted to support substantial equivalence, there were no experts used to establish ground truth for a test set in the context of diagnostic performance. The evaluation was based on engineering and safety standards.

4. Adjudication Method for the Test Set

Not applicable, as no clinical test set was used for diagnostic performance evaluation.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Not applicable. The document states that clinical studies were not required, and there is no mention of an MRMC study or evaluation of human reader improvement with AI assistance. The device is an ultrasound imaging system, not an AI-assisted diagnostic tool in the sense of image interpretation.

6. Standalone (Algorithm Only) Performance Study

Not applicable. This device is an ultrasound imaging system, not an algorithm being deployed in a standalone capacity for diagnostic interpretation. Its "software" features are related to image processing and user interface, not automated diagnostic algorithms.

7. Type of Ground Truth Used

Not applicable for diagnostic performance. For safety and functional aspects, the "ground truth" would be established by validated test methods and compliance with recognized standards (e.g., IEC, ISO, AIUM/NEMA).

8. Sample Size for the Training Set

Not applicable, as this is not an AI/ML-driven diagnostic device that would require a "training set" in the conventional sense. The device's "software" features are implemented based on established imaging principles and signal processing, not statistical learning from a dataset.

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

Not applicable, as there is no training set for an AI/ML algorithm.

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