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The M7/M7T/M7 Premium/M7 Expert/M7 Super Diagnostic Ultrasound System is applicable for adults, pregnant women, pediatric patients and neonates. It is intended for use in gynecology, obstetric, abdominal, pediatric, small parts (breast, testes, thyroid), neonatal cephalic, transcranial, cardiac, transvaginal, transrectal, peripheral vascular, urology, orthopedic, and musculoskeletal(conventional and superficial), intraoperative and transesophageal(cardiac) exams.

M7/M7T/M7 Premium/M7 Expert/M7 Super Diagnostic Ultrasound System is a general purpose, portable/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, Color M-Mode, Power/Dirpower Mode, TDI mode 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.

This is a 510(k) premarket notification for an ultrasound system, not an AI/ML device. Therefore, the typical acceptance criteria and study designs applicable to AI/ML devices, such as sample sizes for test and training sets, expert qualifications, and comparative effectiveness studies, are not discussed in this document.

The provided document describes the Shenzhen Mindray Bio-Medical Electronics Co., Ltd. M7/M7T/M7 Premium/M7 Expert/M7 Super Diagnostic Ultrasound System (K172970). The acceptance criteria and the study proving the device meets these criteria are related to the safety and performance of a conventional diagnostic ultrasound system, not an AI/ML-driven device.

Here's the information extracted and contextualized for a non-AI medical device:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this diagnostic ultrasound system are based on established medical safety standards and compliance with existing device functionalities. The device primarily aims for "substantial equivalence" to previously cleared predicate devices (Mindray M7, K131690, and Mindray M9, K171034).

Regarding the specific questions tailored for AI/ML devices, based on the provided general ultrasound device 510(k) summary, the following applies:

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

   • This document describes a traditional medical device (ultrasound system), not an AI/ML device. Therefore, there is no mention of a "test set" in the context of an algorithm's performance on a dataset of cases. The "testing" involves verification and validation against technical specifications and safety standards (e.g., electrical, acoustic output measurements) rather than evaluation on clinical image datasets.
   • No information on data provenance (country of origin, retrospective/prospective) is applicable or provided for this type of submission.

2. 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. As this is not an AI/ML device, there is no "ground truth" established by experts on a test set of clinical images for algorithm validation. The "ground truth" for compliance testing would be the physical properties measured against established engineering and safety standards.

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

   • Not applicable. There is no concept of adjudication by human readers for a test set in the context of this traditional ultrasound device submission.

4. 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 not an AI-assisted device, so no MRMC study or AI-related comparative effectiveness analysis was conducted or reported.

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

   • Not applicable. This device is a diagnostic ultrasound system operated by a human, not an algorithm that performs standalone diagnoses.

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

   • For a conventional ultrasound system, the "ground truth" for its performance evaluation would be defined by the accuracy and precision of its physical measurements (e.g., acoustic output, image quality metrics) against engineering specifications and regulatory limits, rather than clinical diagnoses or pathology reports.

7. The sample size for the training set:

   • Not applicable. This document does not describe an AI/ML device, so there is no concept of a "training set" for an algorithm.

8. How the ground truth for the training set was established:

   • Not applicable, for the same reason as above.

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