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The Viewing Client Mobile is a software application that is intended to be used by qualified medical professionals for the review of medical images derived from multiple modalities. Clinical reports can also be viewed using this device.

The Viewing Client Mobile application can be used to perform image manipulation (for example, window width and level, zoom, pan, rotation) and measurement. It can display both lossless and lossy compressed images. For lossy images, the user must determine if the level of loss is acceptable for their purposes.

The Viewing Client Mobile application provides wireless and portable access to medical images from only the following modalities: MRI, CT, X-ray and Ultrasound. It is not intended to replace a full workstation and should be used only when there is no access to one.

The Viewing Client Mobile application must not be used for the primary interpretation of mammographic images.

AXON Medical Technology Corp.'s Viewing Client Mobile is a viewer that facilitates the secure viewing of 2D, DICOM 3.0-compliant soft-copy imaging studies both within and without the Enterprise context. The product operates through an API.

Viewing Client Mobile is used under mobile viewing conditions within the device's intended use. Viewing Client Mobile app runs on iOS platforms.

Core measurement and image manipulation tools provided by the Viewing Client Mobile include zoom, pan, invert, W/L, Pixel Value/ Line/ Angle, ROI and CINE. The Viewing Client Mobile also supports multiseries and study display.

The Viewing Client Mobile app operates on the iPad, a portable, "off-the-sheh" hardware device, used to wirelessly access medical images under mobile conditions, and is therefore more sensitive to factors not typical for reading room workstations (e.g. display condition, variable lighting, viewing angle, etc.). The user is therefore instructed to properly follow the operating instructions provided with the hardware device, utilize the Viewing Client Mobile's risk mitigation features and heed precautions related to safe device use.

The provided text describes the AXON Medical Technologies Corp.'s "Viewing Client Mobile" device and its performance testing.

Here's an analysis of the acceptance criteria and study details based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't provide a specific table with quantitative acceptance criteria for diagnostice performance and measured values against those criteria. Instead, it states that "in each case acceptance criteria was met" for bench testing and that clinical testing resulted in positive qualitative feedback.

Bench Testing:

Clinical Testing:

2. Sample Size and Data Provenance

• Bench Testing: The sample size for bench testing is not explicitly stated as a number of devices or platforms, but it was performed "on all supported off-the-shelf mobile platforms." The data provenance is internal to AXON Medical Technologies Corp.
• Clinical Testing: The document does not specify the exact sample size (number of patient cases or images) used for the clinical image quality demonstration study. The data provenance (country of origin, retrospective/prospective) is not mentioned.

3. Number of Experts and Qualifications for Ground Truth

• Bench Testing: A single expert "in luminance and illuminance testing" validated the test plan. Their specific qualifications (e.g., years of experience, certifications) are not detailed beyond being an "expert."
• Clinical Testing: The ground truth was established by "certified Radiologists." The number of radiologists is referred to as "All radiologists," implying at least more than one, but a specific count is not provided. Their qualifications are stated as "certified Radiologists," but no further details (e.g., years of experience, subspecialty) are given.

4. Adjudication Method

The document does not describe a formal adjudication method (e.g., 2+1, 3+1) for either the bench testing or the clinical testing. For clinical testing, it states "All radiologists indicated" a consensus, but the process to reach that consensus (e.g., independent review followed by discussion, or a single collective assessment) is not specified.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a MRMC comparative effectiveness study was not explicitly described. The clinical study was an "Image Quality Demonstration study" where radiologists performed a "side-by-side comparison of the predicate and Viewing Client Mobile to evaluate and compare the overall image quality." This appears to be a qualitative comparison rather than a formal MRMC study to quantify performance improvement with/without AI assistance.

6. Standalone Performance

Yes, a standalone performance assessment was conducted. The "Viewing Client Mobile" is the device being assessed in isolation (i.e., its performance in displaying images), with radiologists acting as the "human-in-the-loop" for qualitative assessment. The study was focused on the device's ability to display images acceptably for diagnostic reads by human radiologists, not on an algorithm's diagnostic output without human review.

7. Type of Ground Truth Used for Test Set

• Bench Testing: The ground truth was established by conformance to "test guidelines provided in AAPM Assessment of Display Performance for Medical Imaging Devices (2005)" and internal specifications validated by an expert.
• Clinical Testing: The ground truth for the clinical study was expert consensus based on the qualitative assessment of image quality by "certified Radiologists" compared to a predicate device. It was not pathology, or outcomes data.

8. Sample Size for the Training Set

The document does not mention a training set. This device is a medical image viewer and processing software, not an AI/CAD algorithm that typically requires a large training dataset for model development.

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

Not applicable, as no training set is mentioned for an AI/CAD algorithm. The device is a viewer; its functionality is based on display and manipulation of existing DICOM data, not on learning from a dataset to make diagnostic predictions.

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