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VUNO Med-Chest X-ray Triage/VUNO Med-CXR Link Triage is a radiological computer-assisted triage and notification software that analyzes adult chest X-ray images for the presence of prespecified suspected critical findings (pleural effusion and/or pneumothorax). VUNO Med-Chest X-ray Triage/VUNO Med-CXR Link Triage uses an artificial intelligence algorithm to analyze images for features suggestive of critical findings and provides case-level output available in the PACS/ workstation for worklist prioritization or triage.

As a passive notification for prioritization-only software tool within standard of care workflow, VUNO Med-Chest X-ray Triage/VUNO Med-CXR Link Triage does not send a proactive alert directly to the appropriately trained medical specialists. VUNO Med-Chest X-ray Triage/VUNO Med-CXR Link Triage is not intended to direct attention to specific portions of an image or to anomalies other than pleural effusion and/or pneumothorax. Its results are not intended to be used on a stand-alone basis for clinical decision-making.

VUNO Med-Chest X-ray Triage/VUNO Med-CXR Link Triage is an automated computerassisted triage and notification software that analyzes adult chest X-ray images for the presence of pleural effusion and pneumothorax. It is based on an artificial intelligence analysis model, specifically a convolutional network (CNN), which employs deep learning technology to learn features from data.

The training data is sourced from 4 distinct sites of South Korea and India data provider, including medical imaging centers, data partners, and medical hospitals, and over 13 different modality manufacturers such as GE. Philps, FUJI, Canon, Samsung, SIEMENS, etc.

A "locked" algorithm is used, and the same input gives the same results every time. The software receives an image of a frontal chest radiograph and automatically analyzes it for the presence of pre-specified critical findings. If any findings are suspected, the image is flagged, and a passive notification is provided to the user. Subsequently, trained radiologists or healthcare professionals should make the final decision which is the standard of care at present. A user interface is provided for visualization, displaying the loaded image and any detected findings.

The data can be transmitted from Picture Archive and Communications Systems (PACS) using the DICOM protocol.

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

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Sizes and Data Provenance

• Test Set Sample Sizes:
  • Pleural Effusion: 1,200 scans (with pleural effusion) and 797 scans (without pleural effusion) for a total of 1,997 scans.
  • Pneumothorax: 716 scans (with pneumothorax) and 474 scans (without pneumothorax) for a total of 1,190 scans.
• Data Provenance: The test datasets were retrospectively collected chest X-rays. They were sourced from various regions of the US: Midwest, West, Northeast, and South. The text explicitly states that the test dataset is "independent of the training dataset, with each sourced from a different country." While the training set is from South Korea and India, the text indicates the test set is from the US.

3. Number of Experts and Qualifications for Ground Truth - Test Set

• For the predicate device (qXR-PTX-PE), the ground truth for pneumothorax performance testing was established by 3 ABR radiologists with a minimum of 5 years of experience.
• The document does not explicitly state the number or qualifications of experts used to establish the ground truth for the subject device's (VUNO Med-Chest X-ray Triage) test set. It only mentions the ground truth for the predicate device's test set was established by radiologists. It's common practice for the subject device to follow a similar ground truthing methodology, but this is not explicitly stated.

4. Adjudication Method for the Test Set

• The document does not specify an adjudication method (e.g., 2+1, 3+1) for establishing the ground truth for the test set. It only states that the ground truth for the predicate device was "established by 3 ABR radiologists." This could imply consensus or a majority vote, but it's not detailed.

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

• No, an MRMC comparative effectiveness study was not reported. The study focused on the standalone performance of the AI algorithm (VUNO Med-Chest X-ray Triage) and compared its performance metrics (AUC, sensitivity, specificity) against those of the predicate device (qXR-PTX-PE). There is no mention of human readers assisting or being compared to the AI.

6. Standalone (Algorithm Only) Performance

• Yes, a standalone performance study was done. The reported performance metrics (AUC, sensitivity, specificity) are for the VUNO Med-Chest X-ray Triage algorithm operating independently (without human-in-the-loop assistance for the reported metrics).

7. Type of Ground Truth Used

• The ground truth for the test set was established by expert consensus (specifically, by ABR radiologists for the predicate device, implying a similar method for the subject device). The presence or absence of the critical findings (pneumothorax and pleural effusion) was determined by these experts.

8. Sample Size for the Training Set

• The document mentions that the training data is sourced from "4 distinct sites of South Korea and India data provider," but it does not specify the sample size (number of images or patients) used for the training set.

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

• The document states that the AI algorithm "employs deep learning technology to learn features from data" and that the "training data is sourced from 4 distinct sites of South Korea and India data provider." However, it does not explicitly detail how the ground truth for the training set was established. It can be inferred that these images were expert-labeled, as is typical for supervised deep learning, but the specific process (e.g., number of readers, their qualifications, adjudication) is not described for the training set.

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The VUNO Med-DeepBrain is intended for automatic labeling, quantification of segmentable brain structures from a set of MR images. The software is intended to automate the current manual process of identifying, labeling and quantifying segmentable brain structures identified on MR images. The users are trained healthcare professionals who work with medical imaging.

The product is used in an office-like environment.

The VUNO Med-DeepBrain provides brain structural information based on the brain MR image. Input images for analysis are 3D T1 weighted brain MR images and 2D T2 flair brain MR images. Once the recommended images are uploaded, automated brain segmentation is performed and provides volumetric data of brain regions. It is displayed in the viewer with a color map.

VUNO Med-DeepBrain is intended for automatic labeling, visualization, and volumetric quantification of segmentable brain structures and lesions from a set of MR images. It takes a 3D T1 MR image as input and gives segmented brain structures and lesions, and volumetric quantification. The user interface is provided for the visualization. The segmented structures are displayed as a color map and the user can view regions by selecting the name of the region. The 2D T2 Flair MR image is taken for lesion quantification. In addition, the uploaded image can be compared to the normative percentile and prior images when applicable. The user can download and print the result in a report format. The data can be received and sent through the Picture Archive and Communications Systems (PACS) using the DICOM protocol.

Here's a breakdown of the acceptance criteria and study details for the VUNO Med-DeepBrain based on the provided text:

Acceptance Criteria and Device Performance

Study Details

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

• Sample Size: Not explicitly stated. The document mentions that "Whole brain regions including cortical and subcortical as well as WMH regions exceeded the criteria," but does not provide the number of scans or patients in the test set.
• Data Provenance: Not specified. The document does not mention the country of origin of the data or whether it was retrospective or prospective.

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

• Number of Experts: Singular ("compared to expert"). The document indicates ground truth was established by "expert," implying a single expert or a collective "expert opinion" without specifying the number.
• Qualifications of Experts: Not specified. It only states "expert" without further details on their qualifications (e.g., specific medical specialty, years of experience, board certification).

3. Adjudication method for the test set:

• Not explicitly stated. The document mentions comparison to "expert" but does not describe any adjudication process (like 2+1, 3+1, or none) if multiple experts were involved. Given the phrasing "compared to expert," it suggests a comparison against a single reference standard, possibly simplifying the need for adjudication.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or described. The study focused on standalone device performance against an expert.

5. (If MRMC was done) Effect size of how much human readers improve with AI vs without AI assistance:

• Not applicable, as an MRMC study comparing human readers with and without AI assistance was not reported.

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

• Yes, a standalone performance test was done. The "Segmentation Accuracy Test and Reproducibility Test" were conducted to evaluate the device's performance directly by comparing its output to an expert's segmentation ("compared to expert"). This represents the algorithm's performance without direct human intervention in the interpretation process.

7. The type of ground truth used:

• Expert Consensus / Manual Segmentation: The ground truth for segmentation accuracy was established by an "expert" (or experts) through manual segmentation, as the device's output (Dice Similarity Coefficient and volume errors) was compared against this expert's work. The document mentions "volume errors between manual segmentation and device output are analyzed," explicitly indicating manual segmentation as the reference.

8. The sample size for the training set:

• Not explicitly stated. The document mentions the device learns from "a large dataset" but does not provide the specific sample size for the training set.

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

• Not explicitly stated. The document mentions that the algorithm is "based on the machine technique in which the device learns the characteristics of brain MR images from a large dataset" and that "The subject device is based on the region parcellation principle of FreeSurfer, which is a silver standard." This implies that the training data likely had ground truth labels derived from expert-curated or FreeSurfer-processed segmentations, but the specific method for establishing this ground truth (e.g., manual annotation by experts, automated methods like FreeSurfer subsequently reviewed) for the training set is not detailed.

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