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The Resona 7/Resona 7CV/Resona 7EXP/Resona 7S/Resona 70B diagnostic ultrasound system is applicable for adults, pregnant women, pediatric patients and neonates. It is intended for use in fetal, abdominal, intra-operative (abdominal, thoracic, and vascular), pediatric, small organ (breast, thyroid, testes), neonatal cephalic, trans-rectal, trans-vaginal, musculo-skeletal (conventional), cardiac adult, cardiac pediatric, trans-esoph. (cardiac), peripheral vessel and urology exams.

Resona 7/Resona 7CV/Resona 7EXP/Resona 7S/Resona 70B Diagnostic Ultrasound System is a general purpose, mobile, software controlled, ultrasound diagnostic system. Its function is to acquire and display ultrasound images in B mode, M mode, PW mode, CW mode, Color mode, Power/Dirpower mode, THI, TDI mode, 3D/4D mode, Color M mode, iScape mode, Strain Elastography, Contrast imaging(LVO and Liver), Ultrasound Fusion Imaging, V Flow, STE, STQ or the combined mode (i.e. B/M-Mode).This system is a Track 3 device that employs an array of probes that include linear array, convex array and phased array.

The provided document is a 510(k) Premarket Notification from Shenzhen Mindray Bio-Medical Electronics Co., Ltd. to the FDA regarding their Resona 7 Diagnostic Ultrasound System. This type of submission is for demonstrating "substantial equivalence" to a legally marketed predicate device, not necessarily for proving novel clinical claims or improved clinical effectiveness through extensive clinical trials.

Therefore, the document does not contain information on acceptance criteria for an AI/CADe device's performance, nor does it detail a study proving such performance in the way a clinical study for a novel AI algorithm would. Instead, it focuses on demonstrating that a modified ultrasound system remains substantially equivalent to its predicate.

However, based on the structure of your request and assuming this was a document from which one hoped to extract such information about a theoretical AI/CADe device, I will explain why most of your requested points cannot be answered from this document and what information is relevant to the device's acceptance.

This document is for an ultrasound imaging system, not an AI/CADe device. As such, the "acceptance criteria" and "study that proves the device meets the acceptance criteria" are related to traditional ultrasound device performance and safety, not AI algorithm performance.

Here's an analysis based on the provided document:

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

The document does not provide a table of acceptance criteria for AI performance. The acceptance criteria for this ultrasound system are related to its safety, electrical performance, acoustic output, and imaging capabilities being equivalent to the predicate device. The performance is "proven" by compliance with recognized standards and comparison to the predicate.

• Acceptance Criteria (Implicit for a traditional ultrasound device K-submission):

  • Substantial equivalence to predicate device in terms of intended use, technology, safety, and effectiveness.
  • Compliance with recognized safety standards (e.g., AAMI/ANSI ES60601-1, IEC 60601-1-2, IEC 60601-2-37).
  • Acoustic output levels below FDA limits.
  • Biocompatibility of patient-contacting materials (for new transducers/brackets).
  • Effective cleaning and disinfection.
  • Software lifecycle processes compliance (IEC 62304).
  • Risk management (ISO 14971).
  • Imaging modes and features perform similarly to predicate.

• Reported Device Performance: The document states that the device "has been found to conform with applicable medical safety standards" and "is substantially equivalent with respect to safety and effectiveness to devices currently cleared for market." It doesn't present specific quantitative performance metrics like sensitivity, specificity, or AUC as one would expect for an AI/CADe device, but rather confirms compliance with established engineering and safety benchmarks relevant to ultrasound systems.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

This document is for a traditional ultrasound imaging system submission (510(k)), not an AI/CADe device. Therefore, it does not describe a "test set" or "data provenance" in the context of an AI algorithm being evaluated on a dataset of patient images.

The "testing" mentioned refers to engineering and safety tests on the physical device and its software, not performance on a clinical image dataset for diagnostic accuracy.

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

Not applicable. This is not an AI/CADe device that requires expert-established ground truth for a test set. The "ground truth" for the device's function is its ability to produce images and measurements according to its specifications and to operate safely, which is verified through engineering tests and comparison to the predicate.

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

Not applicable, for the same reasons as point 3.

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. This is a 510(k) for an ultrasound system, not an AI-assisted device. The "multi-reader multi-case" study design is typical for evaluating the impact of AI on human reader performance, which is not relevant to this submission.

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

Not applicable. This is not an AI algorithm. Its performance is inherent in its operation as an imaging system.

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

Not applicable for an AI/CADe device. The "ground truth" for this ultrasound system relates to its technical performance and safety, which are evaluated against engineering standards and comparison to a predicate device.

8. The sample size for the training set

Not applicable. This is not an AI/CADe device that has a "training set."

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

Not applicable. This is not an AI/CADe device that has a "training set."

Summary from the document's perspective:

The document describes a 510(k) submission for a Diagnostic Ultrasound System (Resona 7/Resona 7CV/Resona 7EXP/Resona 7S/Resona 7OB). The core of this submission is to demonstrate substantial equivalence to existing legally marketed predicate devices (primarily K162267, Resona 7, and others like DC-8, ZS3).

The "acceptance criteria" and "study" are therefore focused on:

• Safety and Effectiveness: Ensuring the new/modified device is as safe and effective as the predicate.
• Compliance with Standards: A list of recognized consensus standards is provided (e.g., AAMI/ANSI ES60601-1 for electrical safety, IEC 60601-1-2 for EMC, IEC 60601-2-37 for ultrasonic equipment, ISO 14971 for risk management, ISO 10993 for biocompatibility, NEMA UD 2 for acoustic output). The "study" is the non-clinical testing performed to show compliance with these standards.
• Functional Equivalence: The device employs the same underlying technology (ultrasonic energy transmission and processing), has the same intended uses (e.g., fetal, abdominal, cardiac exams), and generally the same basic operating modes as its predicate. New transducers and features are added, but they are evaluated in the context of the device remaining substantially equivalent.

The document explicitly states under "8. Clinical Tests: Not Applicable," which further confirms that no clinical studies (like those evaluating AI diagnostic performance or human reader improvement with AI) were conducted or required for this particular 510(k) submission.

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