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The DELL U3014 with QUBYX PerfectLum is intended to be used for displaying and viewing of digital images, for review and analysis by trained medical practitioners. The DELL U3014 must only be used in conjunction with QUBYX PerfectLum. The device must not be used in primary image diagnosis in mammography. The device can not be used for a life-support system and does not contact with the patient.

The DELL U3014 with QUBYX PerfectLum is a 30" color display for medical viewing. It provides 2560x1600 resolution with an adjustable Look Up Table and a 10 bit panel. It is combined with QUBYX PerfectLum and PerfectLum remote management, a userfriendly DICOM calibration and AAPM TG18 verification software suite. The software allows setting the display function to DICOM, displaying test pattern and performing acceptance and constancy tests.

The acceptance criteria and study proving the device meets those criteria are detailed below. It's important to note that this document describes a medical display system, not an AI algorithm for diagnostic interpretation. Therefore, some standard questions related to AI studies (like MRMC, training set details, or deep learning specific ground truth) are not applicable in this context.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The "test set" in this context refers to the physical device undergoing conformance testing, not a dataset of medical images.

• Sample Size for Test Set: One unit of the DELL U3014 with QUBYX PerfectLum bundle.
• Data Provenance: The device performance data was generated through tests conducted by QUBYX and verified by the University of Arizona. This is a prospective evaluation of a specific hardware and software combination.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This question is not directly applicable. The "ground truth" for a medical display system's performance is objective compliance with technical standards (DICOM Part 14 GSDF and AAPM TG18). These standards define measurable characteristics and visual patterns for evaluation.

• The tests were performed using instrumentation (X-Rite i1 Display Pro measurement device) and software (QUBYX PerfectLum) according to the specified standards.
• One "user" was involved in the visual steps of the AAPM TG18 test. While not explicitly stated to be an "expert" in the same way a radiologist diagnoses an image, this user's role was to assess test patterns as per the AAPM standard, implying knowledge of what to look for based on documented criteria.
• The overall compliance was verified by the University of Arizona, implying expert oversight in the validation process, though specific qualifications of individuals involved are not provided in this summary.

4. Adjudication Method for the Test Set

Not directly applicable in the typical sense of agreement on diagnostic findings. The adjudication of the device's technical performance was based on:

• Objective Measurement: The X-Rite i1 Display Pro measurement device performed objective measurements of display characteristics.
• Software Analysis: The QUBYX PerfectLum software analyzed these measurements against target values defined by DICOM and AAPM standards.
• User Input (for visual tests): For visual steps of the AAPM TG18 test, a user's answers were recorded and analyzed by the software against the standard's criteria.
• Verification: The results of these tests were verified by the University of Arizona. There is no mention of a multi-reader adjudication method for discrepancies, as the tests themselves are designed to be objective or based on clearly defined visual criteria.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study was not performed. This type of study is relevant for evaluating the impact of an AI algorithm on human reader performance for diagnostic tasks using medical images. This submission is for a medical display system, where regulatory clearance is based on its technical compliance with established standards for image presentation, not its impact on diagnostic accuracy through reader studies.

6. If a Standalone (i.e. Algorithm Only Without Human-in-the-Loop Performance) Was Done

This question is not applicable. The device is a display system, not a diagnostic algorithm. Its "standalone performance" refers to its ability to meet display standards, which was evaluated through technical tests (DICOM and AAPM compliance) as described.

7. The Type of Ground Truth Used

The ground truth used for evaluating the display system's performance was:

• Established Industry Standards: DICOM Part 14 Grayscale Standard Display Function (GSDF) and AAPM TG18 display quality control guidelines.
• These standards define objective requirements for luminance response, uniformity, resolution, and other visual characteristics essential for medical image display.

8. The Sample Size for the Training Set

This question is not applicable. The device is a medical display system, not an AI algorithm that requires a training set of data.

9. How the Ground Truth for the Training Set Was Established

This question is not applicable for the reasons stated above.

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