Search Results

The Digital radiography X-ray system is used in hospitals, clinics, and medical practices.

The Digital radiography X-ray system enables radiographic exposures of the whole body including: skull, chest, abdomen, and extremities and may be used on adult and bariatric patients. Exposures may be taken with the patient sitting, standing, or in the prone position. It is not intended for mammographic applications.

The Digital radiography X-ray system uses digital detectors for generating diagnostic images by converting X-rays into image signals.

The Digital Radiography X-ray System is a radiography X-ray system. It is designed as a modular system with components such as a ceiling suspension with an X-ray tube, Bucky wall stand, Bucky table, X-ray generator, portable wireless, and fixed integrated detectors that may be combined into different configurations to meet specific customer needs. Software is of Basic level of concern, which is also based on a predicate device.

The provided documents describe the Digital Radiography X-ray System (Sontu100-Rad(E), Sontu300-Mars(E)) and its substantial equivalence to predicate devices (K220919 and K213700). However, the document does not contain specific acceptance criteria, reported device performance metrics in numerical form, details about a clinical study for comparative effectiveness, or information regarding ground truth establishment beyond comparison to predicate device characteristics and phantom/patient images.

Based on the information provided, here's what can be extracted:

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

The document mentions that "all the performance parameters of flat panel detector meet the requirements, and the performance parameters of proposed detector and predicate detector are the same." It also states that a "concurrence evaluation on a certain number of clinical images from the same phantom or patients" was performed, and the results "show the ability of the device to provide images with equivalent diagnostic capability to those of the cleared predicate devices."
However, specific quantitative acceptance criteria and their corresponding reported performance values are not provided in the document. The document primarily focuses on demonstrating substantial equivalence through component and technical characteristic comparisons with predicate devices and compliance with relevant IEC and ASTM standards.

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

The document mentions "a certain number of clinical images from the same phantom or patients" for the concurrence evaluation.

• Sample size: "a certain number" (specific number not provided).
• Data provenance: "clinical images from the same phantom or patients". The country of origin is not specified, nor is whether the data is retrospective or prospective.

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

This information is not provided in the document.

4. Adjudication method for the test set

This information is not provided in the document.

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

• MRMC study: The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study.
• AI assistance: The device described is a Digital Radiography X-ray System, which is hardware for generating images. There is no mention of AI assistance or an AI component in this device.

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

Since there is no mention of an AI algorithm or software component for image analysis (beyond the basic level of concern software managing the X-ray system), a standalone algorithm-only performance study like this is not applicable and not mentioned. The "performance data" refers to the overall X-ray system and its detector's image quality.

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

The document indicates that the performance of the flat panel detectors was evaluated based on standard image quality metrics (Effect Image Area, Linear Dose Range, Linear Dynamic Range, Spatial Resolution, Low Contrast Resolution, Flat Uniformity, Modulation Transfer Function, Detective Quantum Efficiency, Artifact, Erasure Thoroughness) and comparison to predicate devices. For clinical images, a "concurrence evaluation" was performed to show "equivalent diagnostic capability to those of the cleared predicate devices." This suggests the ground truth was implied to be equivalent to the diagnostic output of the predicate devices, likely through comparison of images by human readers, but the specifics of how this diagnostic capability was established as ground truth (e.g., expert consensus) are not detailed.

8. The sample size for the training set

The document describes an X-ray imaging system, not a device that uses a training set for machine learning. Therefore, a "training set" in this context is not applicable and not mentioned.

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

As there is no training set for this type of device, this information is not applicable.

Ask a specific question about this device