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Horos MD™ is a software device intended for viewing images acquired from computed tomography (CT), computed radiography (CR), magnetic resonance (MR), ultrasound (US) and other DICOM compliant medical imaging systems when installed on suitable commercial standard hardware. Images and data can be stored, communicated, processed, and displayed within the system. It is intended for use as a diagnostic and review tool by trained healthcare professionals.

This device is not to be used for mammography.

It is the User's responsibility to operate the device in accordance with the software and hardware requirements listed in the instructions for use, in particular ensuring that monitor (display) quality, ambient light conditions, and image compression ratios are consistent with the clinical application.

The Horos MD™ is an interactive image display and navigation software device for diagnosis of medical images. It provides both 2D and 3D image visualization tools for CT and MRI scans from different makes and models of image acquisition hardware. It does not produce any original medical images and does not contain controls for the direct operation of a diagnostic imaging system. Horos MD™ conforms to the DICOM standard to allow the sharing of medical images with other digital imaging systems such as PACS (Picture Archiving and Communication System).

The Horos MD™ software device runs on the macOS X platform, taking advantage of its optimized 3D graphic capabilities, which are provided by the METAL framework developed and maintained by Apple Inc.

The user interface of the software follows Apple's Human Interface Guidelines (HIG) in order to create a user interface that is intuitive and easy to use for users who are familiar with other Apple products. Typical users of this device are radiologists and clinicians who are familiar with 2D scan images.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Note: The acceptance criteria for accuracy are not explicitly defined as thresholds (e.g., "must be greater than 99%"). Instead, the performance data is presented, implying that these values are acceptable and demonstrate sufficient accuracy for the intended use. The overall acceptance criterion is "substantial equivalence" to the predicate device, demonstrated through non-clinical bench testing.

Study Details

The provided text describes a non-clinical bench performance test to demonstrate the accuracy of measurement functions and software verification and validation to ensure overall functionality and cybersecurity.

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

• Test set sample size: For the measurement accuracy testing, 75 Digital Reference Objects were used.
• Data provenance: Not explicitly stated, but "Digital Reference Objects" implies synthetically generated data with known values, not clinical patient data from a specific country. This is a common approach for bench testing. The study is retrospective in the sense that these objects were created for the purpose of testing the device.

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

• Number of experts: Not applicable. The ground truth for the measurement accuracy tests was established using known values of the Digital Reference Objects, not expert interpretation.
• Qualifications of experts: Not applicable.

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

• Adjudication method: Not applicable. The ground truth for the measurement accuracy tests was deterministic (known values of Digital Reference Objects). For software verification and validation, the method would typically involve testing against predefined requirements, not expert adjudication.

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:

• MRMC study: No. The provided text explicitly states, "No clinical testing was required to demonstrate safety or effectiveness for the subject device as the device’s non-clinical (bench) testing was sufficient to support the intended use of the device." This device is a medical image management and processing system, not an AI-powered diagnostic aid that enhances human reader performance.
• Effect size: Not applicable.

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

• Standalone study: Yes, in a way. The measurement accuracy tests were performed on the algorithm's output against known values. The software verification and validation also represent standalone testing of the software's functionality and security. The device itself is "software device intended for viewing, storing, communicating, processing, and displaying medical images," which operates in a 'standalone' computational manner, though it is a tool for human healthcare professionals.

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

• Type of ground truth: For the measurement accuracy tests, the ground truth was based on known values derived from the design of the Digital Reference Objects. For software verification, the ground truth is adherence to predefined software requirements and specifications.

8. The sample size for the training set:

• Training set sample size: Not applicable. This device is described as a "Medical Image Management and Processing System" that provides tools for image visualization and measurement. It is not an AI/ML model that would typically require a training set in the context of learning from data to perform a diagnostic task. The software functionality is based on deterministic algorithms for image manipulation and measurement.

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

• How ground truth was established: Not applicable, as there is no training set mentioned for this type of device.

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