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The Sedecal Mobile Radiographic Systems with Digital Detector are intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position.

Sedecal Mobile X-ray Units with Digital Detector arc mobile units which operate from 120 or 220 V 50-60~ AC or batteries. The units utilize high frequency inverters. The usual safety precautions regarding the use of xrays must be observed by the operator. This modified device combines two FDA cleared devices by way of a common user interface, a color LCD touch panel. The X-Ray generator is controlled by a serial port on the CANON CXDI-50G.

The provided text describes a 510(k) summary for the Sedecal Mobile X-Ray Units with Digital Detector. This regulatory submission primarily focuses on demonstrating substantial equivalence to existing predicate devices, rather than an independent study to prove the device meets specific performance acceptance criteria in the same way a de novo or PMA submission might.

Therefore, the information typically requested in your prompt regarding acceptance criteria, study design, expert involvement, and ground truth for a performance claims study is largely not applicable in this context.

Here's an breakdown based on the provided text:

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

The submission does not present explicit acceptance criteria for diagnostic performance (e.g., sensitivity, specificity, accuracy for detecting specific pathologies) because it's a substantial equivalence submission. Instead, the "acceptance criteria" are implied by demonstrating equivalence in technical specifications and intended use to existing, cleared devices.

The table provided is a comparison to predicate devices for various technical characteristics, not performance metrics against acceptance criteria.

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

The text states: "The results of bench, user, and standards testing indicates that the new device is as safe and effective as the predicate devices." and "After analyzing bench, user, and standards testing data...".

• Sample Size for Test Set: Not specified. This typically refers to a clinical dataset for performance evaluation, which wasn't the primary focus of this 510(k). The "user testing" likely refers to functional testing, not a clinical study with a defined patient sample.
• Data Provenance: Not specified.
• Retrospective or Prospective: Not specified. Given the nature of a 510(k) for device combination, clinical studies are often not required if substantial equivalence can be demonstrated through technical comparison and bench testing.

3. 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. The submission does not detail any clinical study where ground truth for diagnostic features would need to be established by experts.

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

Not Applicable. No clinical study with adjudicated ground truth is described.

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 an X-ray imaging system, not an AI-powered diagnostic tool. No MRMC study was conducted or is relevant for this type of 510(k) submission.

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

Not Applicable. This is an X-ray imaging system, not an algorithm, so standalone performance is not relevant in this context.

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

Not Applicable. As no clinical diagnostic performance study is described, the concept of "ground truth" for diagnostic findings is not addressed in this submission. The "ground truth" for the technical specifications (e.g., power output, exposure times) would be established by direct measurement during bench testing.

8. The sample size for the training set

Not Applicable. This is an X-ray imaging system, not a machine learning algorithm that requires a training set.

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

Not Applicable. As there is no training set mentioned.

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