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The e-Lung software provides reproducible CT values for pulmonary tissue, which is essential for providing quantitative support in the examination of radiological findings. These radiological findings can then be evaluated by the physician in conjunction with a range of ancillary information to form a potential diagnosis or list of likely diagnoses. The e-Lung software package is intended to be a workflow enhancement and visualization tool for the assessment of CT thoracic datasets. e-Lung can be used to support the physician when examining the pulmonary and thoracic tissue (i.e. lung parenchyma) in CT thoracic datasets. 3D segmentation, volumetric measurements, density evaluations, and reporting tools are combined with a dedicated workflow.

Brainomix 360 e-Lung is a software package compliant with the DICOM standard and running on an off-the-shelf physical or virtual server. e-Lung is a CT processing module which operates within the integrated Brainomix 360 platform.

Brainomix 360 e-Lung is a stand-alone software device which uses a set of image processing algorithms to perform evaluation (3D segmentation and isolation of sub-compartments, volumetric measurements, and density evaluations), editing, and reporting tools which are combined with a dedicated workflow.

e-Lung can be used to support the physician in the documentation of radiological findings that may be indicative of chest diseases when examining the pulmonary and thoracic tissue (i.e. lung parenchyma) in CT thoracic datasets. These radiological findings are then evaluated in conjunction with a range of ancillary information to form a potential diagnosis or list of likely diagnoses.

e-Lung is designed to analyze pulmonary CT slice data and display analysis results. Each voxel of the scan is measured by Hounsfield units (HU), a measurement of x-ray attenuation that is applied to each volume element in three-dimensional space. The HU are utilized to distinguish between air, water, tissue and bone, such distinction is common in the industry.

e-Lung provides computed tomography (CT) viewing, and parenchymal density analysis in one application. e-Lung provides quantitative measurements and tabulates quantitative properties.

e-Lung focuses on what is visible to the eye and applies volumetric methods that might otherwise be too time consuming to use.

The software does not perform any function which cannot be accomplished by a trained user utilizing manual tracing methods; the software does not reconstruct a 3D rendering image of the lung; the intent of the software is to enhance the workflow by saving time and automating potential error prone manual tasks.

e-Lung has functions for loading, and saving datasets, and will generate screen displays, computations and aggregate statistics. e-Lung data output may be exported to a CSV, Excel or PDF file.

Here's a summary of the acceptance criteria and study details for the Brainomix 360 e-Lung device, based on the provided text:

Acceptance Criteria and Device Performance

The device's performance was evaluated based on the accuracy of its lung segmentation algorithm compared to a predicate device.

Study Details

1. Sample size used for the test set and the data provenance: The document does not explicitly state the numerical sample size for the test set, but it implies a cohort of lung images used for the Dice Similarity Coefficient comparison. The provenance of the data (country of origin, retrospective/prospective) is not specified.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Ground truth for the test set was established by the consensus of three experienced US board-certified radiologists.

3. Adjudication method for the test set: Ground truth was established by the consensus of the three radiologists. This implies a method where all three radiologists agreed, or a majority rule was applied for cases of disagreement, though the specific process is not further detailed.

4. 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: A multi-reader multi-case (MRMC) comparative effectiveness study focusing on how human readers improve with AI vs. without AI assistance was not explicitly mentioned. The study described is a head-to-head comparison of the AI/ML algorithm (proposed device) against a predicate device algorithm, not a comparison of human reader performance with and without AI assistance.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Yes, the study clearly describes a standalone performance evaluation of the AI/ML segmentation algorithm. It was a "head-to-head comparison" between the proposed device's algorithm and the predicate device's algorithm for lung mask generation, compared against a ground truth.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The ground truth used was expert consensus from three experienced US board-certified radiologists who segmented the lungs following their usual standard of care.

7. The sample size for the training set: The sample size for the training set is not specified in the provided document.

8. How the ground truth for the training set was established: The document does not specify how the ground truth for the training set was established. It only details the ground truth establishment for the test set used in the validation study.

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