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The Smart-C is a mini C-arm X-ray system designed to provide physicians with real time general fluoroscopic visualization of adult and pediatric patients. It is intended to aid physicians and surgeons during diagnostic procedures, therapeutic treatment, or surgical procedures of the limbs, extremities, or shoulders including but not limited to, orthopedics and emergency medicine. The Smart-C is intended to be used on a table or other hard flat surface. It may also be used with the optional support stand.

The Smart-C is an ultra-portable, battery-powered, mobile fluoroscopic mini C-arm system. The main component is a mini C-arm that consists of a CMOS flat panel detector aligned with an X-ray source monoblock to be used for image acquisition. The system can be hand-transported for imaging at the point of care. The primary operator workstation is a tablet computer that receives the images from the C-arm via wireless transfer protocol. The system includes a wireless footswitch to initiate image acquisition, making the entire system cord-free during operation. It comes with 2 battery packs, a table-top battery charger, and a tablet docking station. An optional Monitor Cart is provided as an accessory. The Smart-C monitor cart includes a 27" full-color touchscreen monitor, a keyboard for data entry, a printer for hard-copy of the x-ray images, and a battery charger for the Smart-C battery packs. The whole cart is battery-powered, to provide a completely cord-free user experience.

The provided text describes the 510(k) premarket notification for the Smart-C™ X-ray Imaging System and its comparison to a predicate device, the Orthoscan Mobile DI Mini C-arm. The document focuses on demonstrating substantial equivalence, rather than a traditional AI/ML performance study with specific acceptance criteria metrics like sensitivity, specificity, or AUC.

Therefore, the requested information regarding "acceptance criteria and the study that proves the device meets the acceptance criteria" in terms of explicit performance metrics, sample sizes for training/test sets, expert adjudication methods, MRMC studies, standalone algorithm performance, and ground truth establishment for a medical AI device cannot be fully extracted from this document. This is because the Smart-C is an imaging device, not an AI/ML algorithm that interprets images. The "performance" discussed relates to image quality and usability, compared to a predicate device, rather than diagnostic accuracy of an algorithm.

However, I can extract information related to the device's evaluation methods and the qualitative assessment of its performance against the predicate, which serves as its "acceptance criteria" for substantial equivalence.

Here's an attempt to answer the questions based on the available text, with caveats where the specific details are not provided:

Device: Smart-C™ X-ray Imaging System

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

The acceptance criteria are not explicitly stated as quantitative performance metrics (e.g., specific image resolution values to be met). Instead, the performance is evaluated against the predicate device and relevant standards to demonstrate substantial equivalence. The "acceptance" is qualitative: that the device's image quality and usability are "at least as good as" the predicate device and meet applicable safety and performance standards.

"Based on physician feedback, the clinical images obtained with the Smart-C were at least as good as the predicate device." |
| Safety and Efficacy: Compliance with relevant standards and guidance documents. | The device "meets the same recognized performance and safety standards, and to conform to FDA guidance regarding solid-state x-ray imaging systems." It has been tested for compliance with "applicable IEC series of x-ray performance standards, including IEC60601-2-54" and "all applicable 21CFR Subchapter J performance standards." Risk analysis and design mitigations were successfully tested. |
| Usability: Equivalent or improved workflow/ease of imaging, given differences like wireless technology and battery power. | The wireless image transfer "improves the workflow and ease of imaging." "The clinical utility of the Smart-C was demonstrated by performing a Clinical Imaging Evaluation. Cadaver subjects were chosen to represent the range of extremity imaging, including shoulders." Pediatric Imaging Usability Evaluation was performed for neonatal and infant patients. "There were no new concerns regarding patient positioning, including for neonatal and infant patients." "The Smart-C has been evaluated by numerous physicians and surgeons for image quality and usability on anthropomorphic phantoms, image quality phantoms, and cadaver subjects in clinical settings. They determined that it performs at least as well as the predicate device, and that it is efficacious for the intended uses." |
| Technical Equivalence: Fundamental scientific technology and core components similar to predicate. | Both devices "use the same fundamental scientific technology of generating fluoroscopic x-ray images using an x-ray source monoblock and flat-panel x-ray imaging detector in a fixed C-arm configuration." Designs are "based on the same modern technologies using a compact monoblock x-ray generator and flat-panel x-ray detector, operating at similar power levels." |

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

• Test Set (Clinical Imaging Evaluation):
  • Sample Size:
    • "Cadaver subjects were chosen to represent the range of extremity imaging, including shoulders." (Specific number not provided).
    • A "Pediatric Imaging Usability Evaluation was performed" for "neonatal and infant patients." (Specific number of subjects not provided).
    • For the tablet display evaluation, "all presented cases" were evaluated (number of cases not specified).
    • "Anthropomorphic phantoms" and "image quality phantoms" were also used.
  • Data Provenance: Not explicitly stated (e.g., country of origin). The evaluation involved "clinical" settings using cadavers. The pediatric study was also an "Evaluation," implying a simulated or controlled setting, not necessarily retrospective or prospective patient data from a real clinic.

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

• Number of Experts:
  • Tablet Display Evaluation: "2 independent board-certified physicians."
  • Image Quality Evaluation (Smart-C): "independent physicians" (number not specified).
  • General Evaluation: "numerous physicians and surgeons" (number not specified).
• Qualifications of Experts: "board-certified physicians" for the display evaluation. For other evaluations, they are referred to simply as "physicians" or "physicians and surgeons."

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

• For the tablet display evaluation, it states, "The conclusion of the expert evaluators is that the image quality of the tablet is diagnostic in all presented cases..." This suggests that both experts agreed, or their consensus was sufficient. No formal adjudication method like "2+1" or "3+1" is described.
• For other aspects, "physician feedback" was used, implying a qualitative assessment rather than a structured adjudication process for ground truth.

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 comparative effectiveness study was done with AI assistance. This device is an X-ray imaging system, not an AI-powered diagnostic algorithm. The evaluations were performed to establish image quality and usability of the device itself compared to a predicate device, not to evaluate human reader performance with or without AI assistance.

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

• Not applicable. The Smart-C is a piece of hardware (fluoroscopic X-ray system) that produces images for human interpretation, not a standalone diagnostic algorithm. Its "performance" refers to the quality of the images it generates.

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

• The "ground truth" for the evaluations was primarily expert judgment/consensus regarding image diagnostic quality and usability. This was based on:
  • "Anthropomorphic phantoms" and "image quality phantoms" (for objective image quality measures like contrast and spatial resolution).
  • "Cadaver subjects" (for clinical imaging evaluation and simulating patient positioning).
  • "Neonatal and infant patients" (for pediatric usability evaluation, likely simulated or using models).
  • Physicians' informal "feedback" and "conclusion" on images and usability.

8. The sample size for the training set

• Not applicable. This document describes the evaluation of a medical imaging device, not the development or training of an AI/ML algorithm. Therefore, there is no "training set" in the context of machine learning.

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

• Not applicable. As a hardware medical imaging device, there is no AI/ML training set or associated ground truth establishment process in the context of this FDA submission.

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