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The XBit Series Digital Color Doppler Ultrasound System is intended for diagnostic ultrasound imaging in B (2D/3D/4D),B/M,M,B+CFM,B+CPA (PD),B+DPD,B+PW,B+CW,B+CFM +D (PW)/CW, B+ CPA(PD) + D (PW)/CW, TDI and Fusion Harmonic Imaging modes. The device is a general-purpose ultrasonic imaging instrument intended for use by a qualified clinician for evaluation of Fetal ,Abdominal,Pediatric,Small Organ (breast, thyroid,testes ), Neonatal Cephalic , Adult Cephalic, Cardiac (adult , pediatric), Musculo-skeletal (Conventional , Superficial) , Peripheral Vascular, Transesophageal, Trans-rectal, Trans-vaginal, OB/GYN and Urology.

The XBit Series Digital Color Doppler Ultrasound System is an integrated preprogrammed color doppler ultrasound imaging system, capable of producing high detail resolution intended for clinical diagnostic imaging applications. This system is a Track 3 device that employs a wide array of probes that include linear array, convex array and phased array. This system consists of a mobile console with keyboard control panel, power supply module, color LED monitor and optional probes. This system is a mobile, general purpose, software controlled, color diagnostic ultrasound system. Its basic function is to acquire ultrasound echo data and to display the image B-Mode (including Fusion Harmonic Imaging), M-Mode, Pulsed (PW) Doppler Mode, Continuous (CW) Doppler Mode, Color Doppler Mode, Power Doppler Mode, Directional Power Doppler Mode, TDI Mode or a combination of these modes, Elastography, contrast imaging, 3D/4D. Auto Follicle, SonoBeam, SonoColor, and SonoFusion, these four features are all semi-automated functions, they need to be modified during processing.

The provided text is a 510(k) Summary for the CHISON XBit Series Digital Color Doppler Ultrasound System. This type of submission focuses on demonstrating substantial equivalence to a predicate device, rather than providing detailed clinical study results designed to prove device performance against specific acceptance criteria.

Therefore, the document does not contain the acceptance criteria or a detailed study proving the device meets those criteria in the way typically expected for a novel AI/ML device. The focus here is on the similarity of the new device to existing, already cleared ultrasound systems.

Specifically, the document states:

• "7. Clinical Test: No clinical testing was required." This explicitly indicates that no clinical study was conducted for this submission.

Given this, I cannot extract the information required for accepting a device, particularly regarding a study that proves the device meets specific performance acceptance criteria related to AI/ML features. The "Acceptance Criteria" in this context would likely refer to the FDA's requirements for demonstrating substantial equivalence (e.g., similar indications for use, technological characteristics, and safety/performance as a legally marketed predicate device).

However, I can extract information related to the device's assessment of its substantially equivalent nature, which includes functional comparisons. The "SE Analysis" sections do describe what the device's "semi-automated functions" do and compare them to predicate features. These comparisons serve as the basis for the declaration of substantial equivalence, which is the "acceptance" in a 510(k) context.

Let's break down what can and cannot be answered based on the provided text, assuming the "AI" features refer to the "semi-automated functions" mentioned in section 4 and detailed in the SE Analyses (e.g., Auto Follicle, SonoBeam, SonoColor, SonoFusion).

Summary of what can be extracted from the document:

The document describes the XBit Series Digital Color Doppler Ultrasound System, which includes "semi-automated functions" like Auto Follicle, SonoBeam, SonoColor, and SonoFusion. For a 510(k) submission, the "acceptance criteria" revolve around demonstrating substantial equivalence to legally marketed predicate devices rather than proving performance against novel, quantitatively defined acceptance metrics. Indeed, the document explicitly states: "No clinical testing was required."

However, the "SE Analysis" sections serve as the basis for demonstrating that these "semi-automated functions" are substantially equivalent to features found in predicate devices. This is the closest the document comes to describing how the device "meets acceptance criteria" for these features.

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

Since "No clinical testing was required," there are no explicit quantitative acceptance criteria or reported device performance metrics in the traditional sense (e.g., sensitivity, specificity, accuracy). Instead, substantial equivalence is claimed based on functional similarity.

Here's how the document frames the comparison for the "semi-automated functions":

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

• Sample Size: Not applicable. The document states "No clinical testing was required." The "test set" for demonstrating substantial equivalence is the comparison of functional specifications and safety data to predicate devices.
• Data Provenance: Not applicable for a clinical test set. The regulatory submission data provenance is CHISON Medical Technologies Co., Ltd. from China. The comparison is based on the declared specifications and previously cleared status of predicate devices.

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

• Not applicable. No clinical testing with ground truth establishment was conducted.

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

• Not applicable. No clinical testing was performed for which adjudication would be relevant.

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. The document explicitly states "No clinical testing was required." Therefore, no MRMC study was performed.

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

• Not applicable. No standalone performance evaluation for these "semi-automated functions" is mentioned or appears to have been required. The functions are described as "semi-automated," implying a human-in-the-loop is always present.

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

• Not applicable. As no clinical testing was performed, no ground truth needed to be established for performance evaluation. Ground truth for the predicate devices' original clearances would have been established, but this document does not detail that.

8. The sample size for the training set:

• Not applicable. The document does not provide information on the training set for the "semi-automated functions" (AI/ML components). This level of detail is typically not required for a 510(k) submission unless the AI/ML algorithm represents a significant change in technology or indications from the predicate.

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

• Not applicable. As no information on the training set is provided, how its ground truth was established is also not detailed.

In conclusion, this 510(k) summary for an ultrasound system relies on demonstrating substantial equivalence to predicate devices, and explicitly states that no clinical testing was required. Therefore, the detailed and quantitative information typically sought for AI/ML device acceptance criteria and performance studies (sample sizes, expert ground truth, MRMC studies, etc.) is not present in this document. The "acceptance" of this device is based on its similarity to already cleared devices in terms of indications, technology, and safety/performance characteristics.

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