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The ADC Pediatric is indicated for use to provide diagnostic quality images to aid in physician diagnosis. The ADC Pediatric is intended to be used in pediatric imaging applications.

The ADC Compact, the predicate device, is a computed radiography imaging system. Instead of screens and photographic film for producing the diagnostic image, the ADC Compact system utilizes an "imaging plate," a plate coated with photo-stimulatable storage phosphors that are sensitive to X-rays and capable of retaining a latent image. This imaging plats is inserted into a device that scans it with a laser and releases the latent image in the form of light which is converted into a digital bit stream. The bit stream of image data is stored locally and can also be stored in the PACS network in DICOM format.

The ADC Pediatric is identical in hardware and software to the ADC Compact. The only difference is in a data file that is provided that is accessed by the (unchanged) suftware, where preselected image processing parameters are paired with typical pediatric exposure parameters by age and exam type (assuming that the exposures follow the European Guidelings on Quality Criteria for Diagnostic Radiographic Images in Pacdiatrics). Rather than entering the exposure parameters manually. as would be the case for pediatric exams using the ADC Compact, the user would simply select the exam and patient age, and the proper image processing parameters would be selected automatically and applied to the image.

I am unable to provide a table of acceptance criteria and reported device performance from the provided text. The document does not specify any quantitative acceptance criteria or detailed device performance metrics. It primarily focuses on demonstrating substantial equivalence to a predicate device for the purpose of a 510(k) submission, rather than a detailed performance study with defined criteria.

However, I can extract other requested information:

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

• Acceptance Criteria: Not specified in the document. The document's goal is to demonstrate "substantial equivalence" to a predicate device, not meet specific performance criteria for a new clinical claim.
• Reported Device Performance: Not quantified. The device is stated to "provide diagnostic quality images to aid in physician diagnosis" and is "identical in hardware and software" to the predicate device, with the only difference being preselected image processing parameters for pediatric applications.

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

• Test Set Sample Size: Not explicitly stated. The document mentions "two hospitals" were used for testing to refine image processing parameters, but no specific number of images or patients is provided.
• Data Provenance: The images were exposed "according to the recommendations of the European Guidelines on Quality Criteria for Diagnostic Radiographic Images in Paediatrics." The country of origin of the data is not specified beyond this reference to European guidelines. The study appears to be retrospective in the sense that the parameter sets were optimized based on existing guidelines, but the exact nature of the image acquisition for this optimization is not detailed as a prospective clinical trial.

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. The text states that "parameter sets were optimized" based on the European Guidelines, but it does not describe a process involving experts establishing a ground truth for a test set.

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:

• No, an MRMC comparative effectiveness study was not done. The document describes a device that is substantially equivalent to a previous version, with the only change being optimized image processing parameters for pediatric use. There is no mention of AI or any study comparing human reader performance with or without the device.

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

• The device is a Computed Radiography imaging system, not an AI algorithm. Its performance is inherent to the imaging process and processing parameters. Therefore, the concept of a "standalone algorithm" performance as typically understood for AI devices does not apply to this submission. The device itself is "standalone" in generating the image, which is then interpreted by a human.

7. The type of ground truth used:

• The ground truth, if any, for the "optimization" of parameter sets was based on the "recommendations of the European Guidelines on Quality Criteria for Diagnostic Radiographic Images in Paediatrics." This points to a guideline-based/expert consensus approach to image quality, rather than pathology or outcomes data.

8. The sample size for the training set:

• The document does not explicitly mention a distinct "training set" with a specified sample size. The "optimization" of parameters occurred through testing in "two hospitals," which implicitly served as the data source for refining these parameters.

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

• The "ground truth" for the training (optimization) was established by aligning with the "recommendations of the European Guidelines on Quality Criteria for Diagnostic Radiographic Images in Paediatrics." This suggests that optimal image quality and diagnostic interpretability, as defined by these guidelines, served as the benchmark for parameter refinement. There is no mention of specific medical diagnoses or outcomes being used as ground truth for this optimization.

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