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3Dicom MD software is intended for use as a diagnostic and analysis tool for diagnostic images for hospitals. imaging centers, radiologists, reading practices and any user who requires and is granted access to patient image, demographic and report information.

3Dicom MD displays and manages diagnostic quality DICOM images.

3Dicom MD is not intended for diagnostic use with mammography images. Usage for mammography is for reference and referral only.

3Dicom MD is not intended for diagnostic use on mobile devices.

Contraindications: 3Dicom MD is not intended for the acquisition of mammographic image data and is meant to be used by qualified medical personnel.

3Dicom MD is a software application developed to focus on core image visualization functions such as 2D multi-planar reconstruction, 3D volumetric rendering, measurements, and markups. 3Dicom MD also supports real-time remote collaboration, sharing the 2D & 3D visualization of the processed patient scan and allowing simultaneous interactive communication modes between multiple users online through textual chat, voice, visual aids, and screen-sharing.

Designed to be used by radiologists and clinicians who are familiar with 2D scan images, 3Dicom MD provides both 2D and 3D image visualization tools for CT, MRI, and PET scans from different makes and models of image acquisition hardware.

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

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document states that the tested accuracy for the lowest clinical range (1-10mm) was found to be slightly inferior (98.8%) due to the resolution of the input scan and screen.

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

• Sample Size for Test Set: 81 Digital Reference Objects (test cases).
• Data Provenance: The Digital Reference Objects were "created...representative of the clinical range typically encountered in radiology practice." The text does not specify a country of origin or whether they were retrospective or prospective data in the clinical sense, as they are synthetically created for testing.

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

The document does not explicitly state the number of experts used to establish the ground truth for the test set or their specific qualifications. It mentions that "usability testing involving trained healthcare professionals" was performed, but this is distinct from establishing ground truth for the measurement accuracy tests. For the measurement accuracy tests, the ground truth was "known values" from the "Digital Reference Objects."

4. Adjudication Method for the Test Set

Not applicable. The ground truth for measurement accuracy was established using "known values" from Digital Reference Objects, not through expert adjudication.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or presented in the provided text. The study described focuses on the device's standalone measurement accuracy.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone performance study was done for the measurement accuracy. The reported percentages (e.g., 99.3% accuracy for length > 10mm) represent the device's performance in measuring known values from Digital Reference Objects. There is no indication of human-in-the-loop performance in these specific metrics.

7. Type of Ground Truth Used

The ground truth used for the measurement accuracy tests was known values from Digital Reference Objects. These objects were created to represent the clinical range.

8. Sample Size for the Training Set

The document does not provide information about the sample size for a training set. The descriptions focus on verification and validation activities for the device's performance, not on a machine learning model's training.

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

As no information about a training set for a machine learning model is provided, there is no description of how its ground truth was established.

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