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feel-DRCS software, used together with digital X-ray detector is the digital X-ray image processing system designed for acquiring images and processing acquired images efficiently. The main features of this software are controlling and interfacing the detector, acquiring images after X-ray, storing acquired images, managing data, optimizing window level and width of acquired images, rotating images, zooming images, measuring images and other features used for imaging processing.

feel-DRCS is compatible with DICOM 3.0 standard. It can transfer images processed in PACS and print images with a film printer compatible with DICOM 3.0 by using DICOM and network systems.

feel-DRCS is not approved for the acquisition of mammographic image data and is meant to be used by qualified medical personnel only. All users must be qualified to create and diagnose radiological image data.

This medical imaging device software is composed of a combination of many functions. The functions include interfacing X-ray detector, integration X-ray generator, networking PACS, image enhancement processing and editing image/information. The name of this software is called DROC(Digital Radiography Operation Console)

The operational process of feel-DRCS is described below:

• 1 Input or select study information patient ID/Name, body part and projection, if you can use DICOM Modality Worklist Management SCP, you select in worklist.
• Setup exposure parameter of X-ray generator (2)
• After exposure, this software acquires raw images from X-ray Detector. (3)
• A After acquisition of raw image, this software corrects raw image.
• (5) After correction of raw image, this software processes with noise reduction, enhancement, auto windowing etc.
• (6) After processing of corrected image, this software displays an image on the monitor.
• (7) If this software is connected with PACS network, it transfers images to PACS Server or DICOM film printer.

The provided text is a 510(k) summary for the feel-DRCS Radiological Image Processing System. This document focuses on establishing substantial equivalence to a predicate device based on intended use and technological characteristics rather than providing detailed acceptance criteria and a study demonstrating the device meets those criteria for specific performance metrics (e.g., diagnostic accuracy, sensitivity, specificity).

Therefore, based solely on the provided text, the following information is largely not available or not applicable in the format requested.

The document states: "This section [Summary of non-clinical testing:] is not applicable." This indicates that the submission did not include a specific study to prove the device meets acceptance criteria related to its image processing performance in a quantitative manner. Instead, the equivalence is argued based on functional similarity.

Here's a breakdown of what can be gleaned and what is missing:

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

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

• Not Available. The submission explicitly states, "Summary of non-clinical testing: This section is not applicable." This indicates that no specific test set data was provided to evaluate the performance against specific acceptance criteria for image analysis or diagnostic accuracy. The assessment relies on a comparison of device functionalities and characteristics.

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. Since no specific test set or performance study was conducted to establish diagnostic accuracy or image processing quality against a "ground truth," experts were not used in this capacity for the purpose of the 510(k) submission as described. The intended users of the device are "qualified medical personnel" and "Radiologists."

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

• Not Applicable. No test set or performance study was described, so no adjudication method would have been necessary.

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. This device is a "Radiological Image Processing System" intended for basic image processing, viewing, and archival. It is not described as an AI-powered diagnostic aid, and no MRMC study was mentioned or implied.

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

• No. The device is a "Picture archiving and communications system" and "Radiological Image Processing System" which inherently involves human interaction for input, processing, and interpretation. No standalone algorithm performance study, in the sense of a diagnostic algorithm, was conducted or reported.

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

• Not Applicable. No performance study requiring a form of ground truth was conducted or reported in this 510(k) summary.

8. The sample size for the training set

• Not Applicable. This device is an image processing and archiving system, not a machine learning or AI-driven diagnostic algorithm that typically relies on a distinct training set. The functionality is based on established image processing techniques and DICOM standards.

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

• Not Applicable. As there is no described training set for an AI/ML algorithm, the concept of establishing ground truth for it does not apply.

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