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This flat panel digital imaging system is intended for use in generating radiographic images of human anatomy. This device is intended to replace film/screen systems in all general purpose diagnostic procedures. This device is intended for use by qualified medical personnel. (Not for mammography)

This is a digital x-ray panel coupled with image acquisition software. The acquisition software is installed on a Windows compatible workstation. No routine user calibration or generator connection is required. AED and ACC are standard features. AED is automatic exposure detection. No synchronization connection to the generator is required. ACC is Automatic Calibration Control. Calibration values are automatically loaded at power-up. The TFT is inherently linear, and all that is necessary is to set a Start (Dark) and Stop (Bright) value to define the dynamic range you want to operate in. It is completely independent of the x-ray source. That is why this can be done at the factory. MECHANICAL: FITS IN THE BUCKY TRAY WITHOUT MODIFICATION... Ultra-Light Cassette Sized Detector easily moves from table top to wall to stretcher with ease. The system is compatible with CPI CMP 200 and Sedecal SHF series generators.

The provided document is a 510(k) summary for the Fusion Digital Diagnostic X-Ray Upgrade Kit (K141743). It explicitly states that "No new testing was done because no modifications have been made other than deleting the generator and tube stand" for both non-clinical and clinical tests (Sections 7 and 8 on page 4).

Therefore, this document does not contain the information requested regarding acceptance criteria and a study proving the device meets those criteria for the K141743 submission.

The document refers to a previous clearance (K132294) for the "IDENTICAL digital x-ray panel/software/workstation." To obtain the requested information, one would need to review the 510(k) submission for K132294.

Based on the provided document K141743, the following questions cannot be answered:

1. A table of acceptance criteria and the reported device performance
2. Sample sized used for the test set and the data provenance
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
4. Adjudication method for the test set
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
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
7. The type of ground truth used
8. The sample size for the training set
9. How the ground truth for the training set was established

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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. (Not for mammography)

This is a complete stationary diagnostic x-ray system employing a digital x-ray panel coupled with image acquisition software. The acquisition software is installed on a Windows compatible workstation. The high frequency generator is available in four different power ratings. All components are either 510(k) exempt or previously cleared. The only difference between the two models is the configuration of the tube stand. Galaxy has a straight arm and the Galaxy+ Plus is in a U-Arm configuration. Generators are available in 40-50-65-or 80 kW (High Frequency) models.

The provided text describes a 510(k) summary for the Galaxy and Galaxy+ Plus Digital Diagnostic X-Ray System. It does not contain information about specific acceptance criteria or a detailed study proving the device meets them in the way typically found for AI/algorithm-driven devices.

The document focuses on demonstrating substantial equivalence to a predicate device (Sedecal X-Plus LP Plus Digital Diagnostic X-Ray Systems, K090238) rather than meeting pre-defined performance acceptance criteria for an AI algorithm. The core of the submission is to show that the new device has "essentially the same technological characteristics" and similar clinical indications for use as the predicate.

Here's an analysis based on the information provided, highlighting what is present and what is not:

Acceptance Criteria and Device Performance:

The document doesn't explicitly state "acceptance criteria" in terms of specific performance metrics (e.g., sensitivity, specificity, AUC) for an AI component, nor does it report such metrics for the device. Instead, the "acceptance criteria" for this 510(k) submission appear to be implicitly about demonstrating that the new device is as safe and effective as the predicate device.

Table of Acceptance Criteria and Reported Device Performance:

• Digital Receptor Panel: Atlaim ATAL 8 and ATAL 8C (Cleared in K113812), compared to Canon panels in predicate.
• Panel Communication: Tethered Ethernet, one panel (Same as predicate).
• Panel Resolution: Pixel size 139 x 139 μm, Image matrix size 3072 x 3072 pixels, Approx. 9.4 million pixels (Predicate: 160 x 160 μm, 2208 x 2688 pixels, Approx. 5.9 million pixels). The new device has higher resolution.
• DICOM: Yes (Same software as cleared in K112527) (Same as predicate).
• Tube Stand: One model is a straight arm, the other is similar to U-Arm (Predicate: U-Arm).
• Generator: CPI, 40-50-65-or 80 kW (High Frequency) (Predicate: Sedecal 65 kW, optional 80 kW). |
  | Electrical Safety & EMC | "The unit has undergone electrical safety and electromagnetic compatibility testing... The unit meets IEC safety and EMC standards." (Implicitly, this is an acceptance criterion). |
  | Software Validation & Risk Analysis | "software validation and risk analysis" were performed. (Implicitly, this is an acceptance criterion). |
  | Clinical Image Quality | "Clinical images were obtained in accordance with the FDA Guidance Document on Solid State Imaging Devices. They were evaluated by professional radiologist and found to be of good diagnostic quality." (This is the primary "performance" metric related to clinical utility). |

Detailed Study Information:

The document describes non-clinical and clinical tests, but these are general descriptions rather than a detailed study protocol for an AI/algorithm.

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

   • Test Set Sample Size: Not specified. The clinical tests mention "Clinical images were obtained," but no quantity is given.
   • Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

   • Number of Experts: Not specified, but referred to as "professional radiologist" (plural).
   • Qualifications of Experts: "professional radiologist." No years of experience or specific subspecialty mentioned.

3. Adjudication method for the test set:

   • Not specified. The text simply states images "were evaluated by professional radiologists and found to be of good diagnostic quality." No mention of consensus, voting, or specific adjudication rules.

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:

   • No MRMC study was performed or described. This submission is for a conventional digital X-ray system, not an AI-driven diagnostic aid. The "clinical tests" assessed the diagnostic quality of the images produced by the system itself, not the performance of human readers with or without AI assistance.

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

   • No standalone algorithm performance study was done. This device is a diagnostic imaging system, not an algorithm. The "performance" being evaluated is the image quality produced by the system for human interpretation.

6. The type of ground truth used:

   • The "ground truth" for the clinical images appears to be the expert opinion/consensus of professional radiologists regarding the "good diagnostic quality" of the images generated by the device. It's not pathology, outcomes data, or a pre-defined objective reference standard for specific病灶 detection, but rather an assessment of general image utility.

7. The sample size for the training set:

   • Not applicable. This device is a digital X-ray system, and the submission does not mention any machine learning or AI components that would require a "training set."

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

   • Not applicable, as no training set for an AI algorithm is mentioned or implied.

Summary of the Device and Submission Context:

This 510(k) pertains to a new digital X-ray system. The "study" described is a testing and evaluation process for a medical device's physical and functional characteristics, not a clinical trial or performance study for an AI algorithm. The device produces images, and the "clinical tests" verified that these images are of "good diagnostic quality" as assessed by radiologists, aligning with the performance expected from similar legally marketed predicate devices. The core argument for clearance is substantial equivalence based on comparable indications for use, technological characteristics (with the new device having higher resolution panels), and meeting relevant safety and EMC standards.

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