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Brainomix 360 Triage Stroke is a radiological computer aided triage and notification software indicated for use in the analysis of non-contrast head CT (NCCT) images to assist hospital networks and trained clinicians in workflow triage by flagging and communicating suspected positive findings of head NCCT images for large vessel occlusion (LVO) of the intracranial ICA and M1 or intracranial hemorrhage (ICH). Specifically, the device is intended to be used for the triage of images acquired from adult patients in the acute setting, within 24 hours of the onset of the acute symptoms, or where this is unclear, since last known well (LKW) time. It is not intended to detect symmetrical bilateral MCA occlusions.

Brainomix 360 Triage Stroke uses an artificial intelligence algorithm to analyze images and highlight cases with detected NCCT LVO or ICH on the Brainomix server on premise or in the cloud in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected LVO or ICH findings via a web user interface or mobile application. Notifications include compressed preview images that are meant for informational purposes only and are not intended for diagnostic use beyond notification.

The device does not alter the original medical image, and it is not intended to be used as a primary diagnostic device. The results of Brainomix 360 Triage Stroke are intended to be used in conjunction with other patient information and based on professional judgment to assist with triage/prioritization of medical images. Notified clinicians are ultimately responsible for reviewing full images per the standard of care.

Cautions:

• All patients should get adequate care for their symptoms, including angiography and/or other appropriate care per the standard clinical practice, irrespective of the output of Brainomix 360 Triage Stroke.
• Brainomix 360 Triage Stroke is not intended to be a rule-out device and for cases that have been processed by the device without notification for "Suspected LVO" should not be viewed as indicating that LVO is excluded. All cases should undergo angiography, per the standard stroke workup.

Limitations:

1. Brainomix 360 Triage Stroke is not intended for mobile diagnostic use. Images viewed on a mobile platform are compressed preview images and not for diagnostic interpretation.
2. Brainomix 360 Triage Stroke does not replace the need for angiography in ischemic stroke workup - it provides workflow prioritization and notification only.
3. Brainomix 360 Triage Stroke has been validated and is intended to be used on Siemens, GE and Philips scanners.
4. Brainomix 360 Triage Stroke is not intended to be used on patients with recent (within 6 weeks) neurosurgery or endovascular neurointervention or recent (within 4 weeks) previous diagnosis of stroke.
5. Brainomix 360 Triage Stroke is not intended to detect symmetrical bilateral MCA occlusions.

Contraindications:
Brainomix 360 Triage Stroke is not suitable for use with scan data containing image features associated with:

• tumours or abscesses
• coils, shunts, embolization or movement artifacts
• intracranial vascular pathologies such as arterial aneurysms, arteriovenous malformations or venous thrombosis.

Brainomix 360 Triage Stroke (also referred to as Triage Stroke in this submission) is a radiological computer aided triage and notification software package compliant with the DICOM standard and running on an off-the-shelf physical or virtual server. Triage Stroke is a non-contrast CT processing software-only medical device which operates within the integrated Brainomix 360 Platform to provide triage and notification prioritization of suspected large vessel occlusion (LVO) or intracranial hemorrhage (ICH). The device uses machine learning algorithms such as advanced non adaptive imaging algorithms, artificial intelligence, and large data analytics.

Brainomix 360 Triage Stroke is available to users in three configurations, featuring three individual processing modules:

• Triage ICH Module, which can only flag positive findings of suspected ICH;
• Triage Stroke Module, which can flag positive findings of suspected ICH or LVO; And
• NCCT LVO Module, which can flag positive finding of suspected LVO

The Triage ICH Module automatically identifies suspected ICH, the NCCT LVO module automatically identifies suspected LVO, while the Triage Stroke Module automatically identifies suspected ICH or LVO on non-contrast CT (NCCT) imaging acquired from adult patients in the acute setting, within 24 hours of the onset of acute symptoms, or where this is unclear, since last known well (LKW) time. The output of the device is a priority notification to clinicians indicating the suspicion of just ICH for Triage ICH Module, the suspicion of just LVO for the NCCT LVO Module, and suspicion of ICH or LVO for Triage Stroke Module based on positive findings. Specifically, the ICH analysis algorithm is optimized to identify findings of hyperdense volume in the parenchyma typically associated with acute intracranial hemorrhage; and the NCCT LVO suspicion uses the combined analysis of the ASPECTS and hyperdense vessel sign (HDVS) algorithms to identify hyper attenuation in vessels and hypodense regions typically associated with a large vessel occlusion in a non-contrast CT scan.

Brainomix 360 Triage Stroke is not intended to detect symmetrical bilateral MCA occlusions. The device uses the basic services supplied by the Brainomix 360 Platform including DICOM processing, job management, imaging module execution and imaging output including notification and compressed image.

Brainomix 360 Triage Stroke notification capabilities enable clinicians to review and preview images via mobile app notification. Alternatively, intended users can also access the notification (a "Suspected LVO" or "Suspected hemorrhage" flag) and straightened images via the Brainomix 360 web user interface. Images that are previewed via mobile app are compressed, are for preview informational purposes only, and not intended for diagnostic use beyond notification.

The device is intended for use as an additional tool for assisting study triage within existing patient pathways. It does not replace any part of the current standard of care. It is designed to assist in prioritization of studies for reading within a worklist, in addition to any other pre-existing formal or informal methods of study prioritization in place. Specifically, it does not remove cases from a reading queue and operates in parallel to the standard of care. This device is not intended to replace the usual methods of communication and transfer of information in the current standard of care.

The Brainomix 360 Triage Stroke device is made available to the user through the Brainomix 360 Platform The Brainomix 360 Platform is a central control unit which coordinates the execution image processing modules which support various analysis methods used in clinical practice today:

• Brainomix 360 e-ASPECTS (K243294)
• Brainomix 360 e-CTA (K242123)
• Brainomix 360 e-CTP (K223555)
• Brainomix 360 e-MRI (K231656)
• Brainomix 360 Triage ICH (K231195)
• Brainomix 360 Triage LVO (K231837)
• Brainomix 360 Triage Stroke (K232496) (predicate device)

The provided document describes the acceptance criteria and the study that proves the device meets those criteria for the Brainomix 360 Triage Stroke device.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

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

• ICH Standalone Study Test Set: 341 cases (167 ICH positive; 174 ICH negative)
• LVO and ICH Standalone Study Test Set: 267 cases (112 LVO positive; 40 ICH positive; 115 Negative for ICH or LVO; 3 excluded due to technical inadequacy).
• Reader LVO Performance Study Test Set: The document does not explicitly state the number of cases used in the reader study. It refers to the same LVO and ICH Standalone Study data for performance metrics, suggesting the reader study was conducted on a subset or the entirety of that dataset.
• Data Provenance: Retrospective study. The document does not specify the country of origin of the data.

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

• Number of Experts: Three (3)
• Qualifications of Experts: Experienced US board-certified neuroradiologists.

4. Adjudication Method for the Test Set

• Method: Consensus of the three experienced US board-certified neuroradiologists.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• Yes, a reader study was conducted.
• Effect Size of Human Readers Improvement with AI vs. Without AI Assistance:
  The study compared the device's standalone LVO sensitivity to that of human readers without AI assistance (the document does not describe human readers using AI assistance).
  • Device's LVO Sensitivity: 69.64%
  • All Human Readers (Experts and Non-experts) LVO Sensitivity: 47.94%
  • Difference (Effect Size): The device's sensitivity was 20.52% (CI: 8.26-32.78%) higher than that of all human readers.
  • Non-expert Radiologists LVO Sensitivity: 47.18%
  • Difference (Effect Size for Non-experts): The device's sensitivity was 21.28% (CI: 5.84-36.72%) higher than that of non-expert (general) radiologists.
  • This indicates that the AI performs better than human readers alone for LVO detection in this study (i.e., human readers would need to improve significantly to match the AI's standalone performance, if this AI assistance was their only aid). The study primarily demonstrates the device's standalone performance in comparison to human unassisted performance rather than human improvement with AI.

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

• Yes, standalone performance studies were conducted.
  • A standalone study for ICH detection performance was conducted.
  • A standalone study for LVO and ICH detection performance was conducted.

7. The Type of Ground Truth Used

• Type of Ground Truth: Expert Consensus (consensus of three experienced US board-certified neuroradiologists).

8. The Sample Size for the Training Set

• The document mentions that the improved ICH algorithm uses "a different deep learning framework, CNN architecture, training data and post-processing capabilities of the algorithm." However, it does not specify the sample size of the training set used for the AI models.

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

• The document implies ground truth for the training data was established due to the mention of "training data." However, it does not explicitly detail the method for establishing ground truth for the training set, only for the test sets (expert consensus).

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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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Brainomix 360 e-ASPECTS is a computer-aided diagnosis (CADx) software device used to assist the clinician in the assessment and characterization of brain tissue abnormalities using CT image data.

The software automatically registers images and uses an atlas to segment and analyze ASPECTS regions. Brainomix 360 e-ASPECTS extracts image data from individual voxels in the image to provide analysis and computer analytics and relates the analysis to the atlas defined ASPECTS regions. The imaging features are then synthesized by an artificial intelligence algorithm into a single ASPECTS (Alberta Stroke Program Early CT) score.

Brainomix 360 e-ASPECTS is indicated for evaluation of patients presenting for diagnostic imaging workup for evaluation of extent of disease. Extent of disease refers to the number of ASPECTS regions affected which is reflected in the total score. Brainomix 360 e-ASPECTS provides information that may be useful in the characterization of ischemic brain tissue injury during image interpretation (within 24 hours from time last known well).

Brainomix 360 e-ASPECTS provides a comparative analysis to the ASPECTS standard of care radiologist assessment by providing highlighted ASPECTS regions and an automated editable ASPECTS score for clinician review. Brainomix 360 e-ASPECTS additionally provides a visualization of the voxels contributing to and excluded from the automated ASPECTS score, and a calculation of the voxel volume contributing to ASPECTS score.

Limitations:

1. Brainomix 360 e-ASPECTS is not intended for primary interpretation of CT images. It is used to assist physician evaluation.
2. The Brainomix 360 e-ASPECTS score should be only used for ischemic stroke patients following the standard of care.
3. Brainomix 360 e-ASPECTS has only been validated and is intended to be used in patient populations aged over 21 years.
4. Brainomix 360 e-ASPECTS is not intended for mobile diagnostic use. Images viewed on a mobile platform are compressed preview images and not for diagnostic interpretation.
5. Brainomix 360 e-ASPECTS has been validated and is intended to be used on Siemens Somatom Definition scanners.

Contraindications/ Exclusions/Cautions:

· Patient motion: Excessive patient motion leading to artifacts that make the scan technically inadequate.
· Hemorrhagic Transformation, Hematoma.

Brainomix 360 e-ASPECTS (also referred to as e-ASPECTS in this submission) is a medical image visualization and processing software package compliant with the DICOM standard and running on an off-the-shelf physical or virtual server.

Brainomix 360 e-ASPECTS allows for the visualization, analysis and post-processing of DICOM compliant Non-contrast CT (NCCT) images which, when interpreted by a trained physician or medical technician, may yield information useful in clinical decision making.

Brainomix 360 e-ASPECTS is a stand-alone software device which uses machine learning algorithms to automatically process NCCT brain image data to provide an output ASPECTS score based on the Alberta Stroke Program Early CT Score (ASPECTS) guidelines.

The post-processing image results and ASPECTS score are identified based on regional imaging features and overlayed onto brain scan images. e-ASPECTS provides an automatic ASPECTS score based on the input CT data for the physician. The score includes which ASPECTS regions are identified based on regional imaging features derived from NCCT brain image data. The results are generated based on the Alberta Stroke Program Early CT Score (ASPECTS) guidelines and provided to the clinician for review and verification. At the discretion of the clinician, the scores may be adjusted based on the clinician's judgment.

Brainomix 360 e-ASPECTS can connect with other DICOM-compliant devices, for example to transfer NCCT scans from a Picture Archiving and Communication System (PACS) to Brainomix 360 e-ASPECTS software for processing.

Results and images can be sent to a PACS via DICOM transfer and can be viewed on a PACS workstation or via a web user interface on any machine contained and accessed within a hospital network and firewall and with a connection to the Brainomix 360 e-ASPECTS software (e.g. a LAN connection).

Brainomix 360 e-ASPECTS notification capabilities enable clinicians to preview images through a mobile application or via e-mail.

Brainomix 360 e-ASPECTS email notification capabilities enable clinicians to preview images via e-mail notification with result image attachments. Images that are previewed via e-mail are compressed, are for informational purposes only, and not intended for diagnostic use beyond notification.

Brainomix 360 e-ASPECTS is not intended for mobile diagnostic use. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests.

Brainomix 360 e-ASPECTS provides an automated workflow which will automatically process image data received by the system in accordance with pre-configured user DICOM routing preferences.

Once received, image processing is automatically applied. Once any image processing has been completed, notifications are sent to pre-configured users to inform that the image processing results are ready. Users can then access and review the results and images via the web user interface case viewer or PACS viewer.

The core of e-ASPECTS algorithm (excluding image loading or result output format) can be summarised in the following 3 key steps of the processing pipeline:

• Pre-processing: brain extraction from the three dimensional (3D) non-enhanced contrast CT head dataset and its reorientation/normalization by 3D spatial registration to a standard template space.
• Delineation of the 20 (10 for each cerebral hemisphere) pre-defined ASPECTS regions of interest on the normalized 3D image.
• Image feature extraction and heatmap generation which consists of the computation of numerical values characterizing brain tissue, apply a trained predictive model to those features and generate a 3D heatmap from the models output for highlighting regions contributing towards the ASPECTS score.

The Brainomix 360 e-ASPECTS module is made available to the user through the Brainomix 360 platform. The Brainomix 360 platform is a central control unit which coordinates the execution image processing modules which support various analysis methods used in clinical practice today:

• Brainomix 360 e-ASPECTS (K221564) (predicate device)
• Brainomix 360 e-CTA (K192692)
• Brainomix 360 e-CTP (K223555)
• Brainomix 360 e-MRI (K231656)
• Brainomix 360 Triage ICH (K231195)
• Brainomix 360 Triage LVO (K231837)
• Brainomix 360 Triage Stroke (K232496)

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

Brainomix 360 e-ASPECTS Device Performance Study

The Brainomix 360 e-ASPECTS device underwent performance testing to demonstrate its accuracy and effectiveness. This included both standalone algorithm performance and a multi-reader multi-case (MRMC) study to assess the impact of AI assistance on human readers.

1. Acceptance Criteria and Reported Device Performance

Digital Phantom Validation (for "volume contributing to e-ASPECTS")

Standalone Performance Testing (for ASPECTS score accuracy)

Multi-Reader Multi-Case (MRMC) Study (Human + AI vs. Human only for ASPECTS score accuracy)

2. Sample Sizes and Data Provenance

• Digital Phantom Validation Test Set: n=110 synthetic datasets
• Standalone Performance Test Set: n=137 non-contrast CT scans
  • Data Provenance: From 3 different USA institutions (Siemens, GE, Philips, and Toshiba scanners).
  • Retrospective/Prospective: The data appears to be retrospective based on the description of patient admission dates (March 2012 and August 2023) and clinical context.
• MRMC Study Test Set: n=140 NCCT scans
  • Data Provenance: Cases collected from various clinical sites (specific countries not explicitly stated, but the mention of US neuroradiologists for ground truth suggests US data). Scanners included Siemens, GE, Philips, and Toshiba.
  • Retrospective/Prospective: The study used "retrospective data" (explicitly stated on page 12).
• Training Set Sample Size: The document does not specify the sample size for the training set. It mentions the algorithm is based on "machine learning" and a "trained predictive model" but provides no details on the training data.

3. Number of Experts and Qualifications for Ground Truth Establishment

• Standalone Performance Test Set: Three board-certified US neuroradiologists. No information on years of experience is provided.
• MRMC Study Test Set: Three board-certified US neuroradiologists for establishing the ground truth that human readers were compared against. No information on years of experience is provided.

4. Adjudication Method for the Test Set(s) Ground Truth

• Standalone Performance Test Set: "Consensus of three board-certified US neuroradiologists." This implies that the ground truth was established by agreement among the three experts. The specific method (e.g., 2-out-of-3, or discussion to reach full consensus) is not detailed, but "consensus" suggests agreement.
• MRMC Study Test Set: "Consensus of three board-certified US neuroradiologists." Similar to the standalone study, ground truth was established by consensus.

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

• Was it done?: Yes, an MRMC study was conducted.
• Effect Size: The study showed a 6.4% improvement in AUC for readers with e-ASPECTS support (85%) compared to without e-ASPECTS support (78%). This improvement was statistically significant (p=.03). There was also an improvement in sensitivity (from 61% to 72%) and a small improvement in specificity (from 96% to 98%). Cohen's Kappa and weighted Kappa also improved significantly.
• Readers: 7 clinical readers (1 "expert" neuroradiologist and 6 "non-expert" radiologists or neurologists).

6. Standalone Performance (Algorithm Only)

• Was it done?: Yes, a standalone performance testing was conducted.
• Performance Metrics: The algorithm achieved an AUC of 83% (95% CI: 80-86%), with a sensitivity of 69% (56-75%) and a specificity of 97% (80-97%) on a case-level as compared to expert consensus. Area under the curve (AUC) specifically refers to overall region-level performance.

7. Type of Ground Truth Used

• Digital Phantom Validation: Synthetic volumes/known phantom volumes.
• Standalone Performance Testing: Expert consensus (of three board-certified US neuroradiologists).
• MRMC Study: Expert consensus (of three board-certified US neuroradiologists).

8. Sample Size for the Training Set

The document does not provide a specific sample size for the training set. It only states that the device uses "machine learning algorithms" and a "trained predictive model."

9. How Ground Truth for Training Set Was Established

The document does not describe how the ground truth for the training set was established. It only refers to a "trained predictive model."

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Brainomix 360 e-CTA is an image processing software package to be used by trained professionals, including, but not limited to physicians and medical technicians. The software runs on standard "off the-shelf" hardware (physical or virtualized) and can be used to perform image viewing, processing, and analysis of images. Data and images are acquired through DICOM compliant imaging devices.

Brainomix 360 e-CTA provides viewing and analysis capabilities for imaging datasets acquired with CTA (CT Angiography).

Brainomix 360 e-CTA is not intended for mobile diagnostic use.

Brainomix 360 e-CTA vessel density asymmetry ratio applies only to the MCA region.

Brainomix 360 e-CTA is a medical image visualization and processing software package compliant with the DICOM standard and running on an off-the-shelf physical or virtual server.

Brainomix 360 e-CTA allows for the visualization, analysis and post-processing of DICOM compliant CTA images which, when interpreted by a trained physician or medical technician, may yield information useful in clinical decision making.

Brainomix 360 e-CTA provides a wide range of basic image viewing, processing and manipulation functions, through multiple output formats. Functionality includes image registration and visualization of large cerebral vessels to provide an analysis of hemispheric difference via contralateral comparison (displayed as a relative percentage).

Brainomix 360 e-CTA processes the images using Al/ML algorithms where the input channels will help the software distinguish bone from vessels and reduce image grain.

Brainomix 360 e-CTA automatically provides a colored overlay to provide a visual reference of the MCA hemisphere of the brain with lower vessel density, and corresponding contrast intensity measurements and estimated phase.

Brainomix 360 e-CTA can connect with other DICOM-compliant devices, for example to transfer CTA scans from a Picture Archiving and Communication System (PACS) to Brainomix 360 e-CTA software for processing. Results and images can be sent to a PACS via DICOM transfer and can be viewed on a PACS workstation or via a web user interface on any machine and accessed within a hospital network and firewall and with a connection to the Brainomix 360 e-CTA software (e.g. a LAN connection).

Brainomix 360 e-CTA notification capabilities enable clinicians to preview images via e-mail notification with result image attachments. Images that are previewed via e-mail are compressed, are for informational purposes only, and not intended for diagnostic use beyond notification.

Brainomix 360 e-CTA is not intended for mobile diagnostic use. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) submission document for Brainomix 360 e-CTA.

1. A table of acceptance criteria and the reported device performance

The document provides performance metrics, primarily focusing on Dice Similarity Coefficient (DSC) for vessel and parenchyma delineation, and Mean Absolute Error (MAE) for vessel density ratio. The "acceptance criteria" are implied by the "Summary performance metrics from full sample" and comparison to a previous version of the device.

Note on "Acceptance Criteria": The document explicitly states acceptance criteria for the digital phantom validation of vessel density ratio. For the AI/ML comparison, the criterion is implied as an improvement over the previous version of the device. For the standalone performance study, the document states "reaching for the vessel delineation and 0.999 for the parenchyma mask as the desired requirement," indicating these are the target performance levels.

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

• Test Set Sample Size: 308 Computed Tomography (CTA) brain scans (studies).
• Data Provenance:
  • Country of Origin: U.S.
  • Clinical Sites: Majority from Boston Medical Centre (BMC) or referring hospitals in the Massachusetts area (N=179). The remaining from Mayo Clinic Rochester (MCR; N=129).
  • Retrospective or Prospective: Retrospective study.

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

The document does not explicitly state the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") used to establish the ground truth for the standalone performance study. It mentions the "truthers" in Table 9 in the context of stenosis.

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

The document does not explicitly describe an adjudication method for the ground truth establishment in the standalone performance study.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study involving human readers assisting with AI vs. without AI assistance was not conducted or described in this document. The study presented is a standalone performance study of the algorithm and a comparison to the predicate device's algorithm.

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

Yes, a standalone performance study (algorithm only) was conducted to assess the performance of the vessel delineation and parenchyma mask generation. This is described in "4.3 Summary of Standalone Performance Study."

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

The document implies the ground truth for the standalone performance study was established by human experts, referred to as "truthers" (e.g., "truthed masks" or "stenosis as noted by the truthers"). However, the precise method (e.g., manual segmentation by expert, consensus of multiple experts) is not explicitly detailed. Given the assessment of segmentation performance (Dice Similarity Coefficient), it's highly likely that the ground truth involved expert-annotated segmentations.

8. The sample size for the training set

The document does not provide the sample size for the training set. The study focuses on evaluating the performance of the device's AI/ML algorithm on a separate test set.

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

The document does not provide information on how the ground truth for the training set was established. It only mentions that the device uses "AI/ML algorithms" to "distinguish bone from vessels" and "increase the quality of the vessel mask."

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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 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 examination of radiological findings that may be indicative of chest diseases e.g. when examining the pulmonary and thoracic tissue (i.e. lung parenchyma) in CT thoracic datasets. 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.

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 is an analysis of the acceptance criteria and study for the Brainomix 360 e-Lung device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Lung Segmentation Study:

  • Test Set Sample Size: 100 cases (N=38 from Boston Medical Center, N=62 from a commercial database).
  • Data Provenance: Retrospective study. Cases were selected from a research registry at Boston Medical Center and a commercial database of clinical imaging data. The demographic and clinical variables were enriched to allow generalizability. The hospital locations mentioned for the cases are Massachusetts, New York, Ohio, New Jersey, Wisconsin, Florida, Maryland, and South Dakota (all in the USA), indicating the data origin is the USA.

• Density Evaluation Study:

  • Test Set Sample Size: Not explicitly stated, but involved "synthetic digital phantom data" and a "real-world data bridging study."

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

• Lung Segmentation Study:
  • Number of Experts: Three (3)
  • Qualifications: Experienced US board certified radiologists.

4. Adjudication Method for the Test Set

• Lung Segmentation Study: The ground truth mask was generated from the consensus of the three experienced US board certified radiologists. This implies an adjudication method where agreement among the experts was used to define the ground truth. The specific "2+1" or "3+1" approach is not explicitly detailed, but "consensus" indicates multiple readers informing the final ground truth.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The study evaluated the device's accuracy against a ground truth created by human experts, not the improvement of human readers with AI assistance versus without AI assistance.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) was Done

• Yes, the lung segmentation study described is a standalone study. The device's lung mask generation was compared directly to the expert-derived ground truth, without human interaction with the device's output during the evaluation phase.

7. The Type of Ground Truth Used

• Lung Segmentation Study: Expert consensus, specifically from three experienced US board certified radiologists who segmented the lungs.
• Density Evaluation Study: Synthetic digital phantom data and a real-world data bridging study to establish pre-defined parameters.

8. The Sample Size for the Training Set

• The document does not explicitly state the sample size for the training set. It only describes the validation set (test set).

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

• The document does not explicitly describe how the ground truth for the training set was established. It focuses on the validation (test) set. Given the algorithm is described as "non-adaptive deterministic," it's possible that a formal "training set" with ground truth in the machine learning sense wasn't used for an adaptive algorithm, but rather a set of cases for algorithm development and refinement, which would typically involve expert annotations for ground truth. However, the document does not provide these details.

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Brainomix 360 Triage Stroke is a radiological computer aided triage and notification software indicated for use in the analysis of non-contrast head CT (NCCT) images to assist hospital networks and trained clinicians in workflow triage by flagging and communicating suspected positive findings of head NCCT images for large vessel occlusion (LVO) of the intracranial ICA and M1 and intracranial hemorrhage (ICH). Specifically, the device is intended to be used for the trage of images acquired from adult patients in the acute setting, within 24 hours of the acute symptoms, or where this is unclear, since last known well (LKW) time. It is not intended to detect isolated subarachnoid hemorrhage and symmetrical bilateral MCA occlusions.

Brainomix 360 Triage Stroke uses an artificial intelligence algorithm to analyze images and highlight cases with detected NCCT LVO or ICH on the Brainomix server on premise or in the cloud in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected LVO or ICH findings via a web user interface or mobile applications include compressed preview images that are meant for informational purposes only and are not intended for diagnostic use beyond notification.

The device does not alter the original mage, and it is not intended to be used as a primary diagnostic device. The results of Brainomix 360 Triage Stroke are intended to be used in conjunction with other patient information and based on professional judgment to assist with triage/prioritization of medical images. Notified clinicians are ultimately responsible for reviewing full images per the standard of care.

Brainomix 360 Triage Stroke is a radiological computer aided triage and notification software package compliant with the DICOM standard and running on an off-the-shelf physical or virtual server.

The Triage Stroke module is a non-contrast CT processing module which operates within the integrated Brainomix 360 Platform to provide triage and notification of suspected large vessel occlusion (LVO) and intracranial hemorrhage (ICH). Brainomix 360 Triage Stroke is a stand-alone software device which uses machine learning algorithms that uses advanced non adaptive imaging artificial intelligence, and large data analytics to automatically identify suspected LVO and ICH on non-contrast CT (NCCT) imaging acquired from adult patients in the acute setting, within 24 hours of the acute symptoms, or where this is unclear, since last known well (LKW) time. The output of the module is a priority notification to clinicians indicating the suspicion of LVO or ICH based on positive findings. Specifically, Brainomix 360 Triage Stroke's ICH analysis is optimized to identify findings of hyperdense volume in the parenchyma typically associated with acute intracranial hemorrhage; and the NCCT LVO suspicion uses the combined analysis of the ASPECTS and hyperdense vessel sign (HDVS) algorithms. It is not intended to detect isolated subarachnoid hemorrhage and symmetrical bilateral MCA occlusions. The Triage Stroke module uses the basic services supplied by the Brainomix 360 Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.

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

Acceptance Criteria and Device Performance

Device Name: Brainomix 360 Triage Stroke

Indications for Use: Radiological computer aided triage and notification software for analysis of non-contrast head CT (NCCT) images to assist workflow triage by flagging and communicating suspected positive findings of large vessel occlusion (LVO) of the intracranial ICA and M1 and intracranial hemorrhage (ICH).

Study Details

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

• Test Set Sample Size: 267 cases (40 ICH positive, 112 LVO positive, 115 negative for ICH or LVO, 3 excluded due to technical inadequacy).
• Data Provenance: Retrospective study. The country of origin is not explicitly stated, but the company is based in the United Kingdom.

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

• Number of Experts: Three (for LVO cases). The number of experts for ICH cases is implied by "previously truthed," likely referring to the K231195 submission, but not specified in this document.
• Qualifications of Experts: Experienced US board-certified neuroradiologists.

3. Adjudication Method for the Test Set

• Adjudication Method: Consensus (for LVO cases). For ICH cases, the method is "as described in the standalone study for our previously cleared device," but not detailed here.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• Was an MRMC study done? Yes, a reader study was conducted to compare NCCT LVO sensitivity of the device to that of radiologists.
• Effect Size of Human Readers Improvement with AI vs. Without AI Assistance:
  • The study compared the device's standalone sensitivity to the sensitivity of human readers. It did not directly measure how human readers improve with AI assistance (i.e., human-in-the-loop performance with AI vs. without AI).
  • Device's standalone sensitivity: 68.75%
  • All readers (experts and non-experts) sensitivity: 47.94% (95% Cl: 37.91-57.97%)
  • Difference between device's sensitivity and all readers: 20.52% (95% Cl: 8.26-32.78%)
  • General radiologists (non-experts) sensitivity: 47.18% (95% Cl: 33.62-60.75%)
  • Difference between device and non-expert sensitivity: 21.28% (95% Cl: 5.84-36.72%)
  • The study stated that the device passed "expert non-inferiority and non-expert superiority," implying the device performs at least as well as experts and better than non-experts in terms of sensitivity for LVO detection.

5. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

• Was a standalone study done? Yes. The core performance data for ICH and NCCT LVO (sensitivity and specificity) represent the algorithm's standalone performance.

6. The Type of Ground Truth Used

• ICH Cases: NCCT imaging with additional clinical information (as described in a previous submission for a related device).
• LVO Cases: Acute CTA imaging and additional clinical information.
• Method of Ground Truth Establishment: Expert consensus (for LVO cases).

7. The Sample Size for the Training Set

• The document does not specify the sample size for the training set. It mentions the algorithm "uses advanced non adaptive imaging artificial intelligence, and large data analytics," which implies a training phase, but no details on size are provided.

8. How the Ground Truth for the Training Set was Established

• The document does not explicitly state how the ground truth for the training set was established. It only describes the ground truth establishment for the test set.

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Brainomix Triage LVO is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of standard of care workflow.

Brainomix Triage LVO uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes CT angiogram images of the brain acquired in the acute setting, and sends notifications to a neurovascular specialist that a suspected large vessel occlusion (LVO) has been identified and recommends review of those images. Images can be previewed through a mobile application or via email. Brainomix Triaqe LVO is intended to analyze terminal ICA and MCA-M1 vessels for LVOs.

lmages that are previewed through the mobile application are compressed and are for informational purposes only and not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing noncompressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests.

Brainomix Triage LVO is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

Brainomix 360 Triage LVO is a radiological computer aided triage and notification (CADt) software package compliant with the DICOM standard and running on an off-the-shelf physical or virtual server.

The Triage LVO module is a CTA processing module which operates within the integrated Brainomix 360 Platform to provide triage and notification of suspected LVO. Brainomix 360 Triage LVO is a stand-alone software device which uses machine learning algorithms that uses advanced non adaptive imaging algorithms, artificial intelligence, and large data analytics to automatically identify suspected LVO on CTA imaging in the acute setting. The output of the module is a priority notification to clinicians indicating the suspicion of LVO based on positive findings. Specifically, Brainomix 360 Triage LVO is optimized to evaluate occlusions of the intracranial internal carotid artery (ICA) and proximal middle cerebral artery (M1 segment). The Triage LVO module uses the basic services supplied by the Brainomix 360 Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.

Brainomix 360 Triage LVO notification capabilities enable clinicians to review and preview images via mobile app notification. Alternatively, intended users can also access the notification (a "Suspected LVO" flag) and straightened images via the Brainomix 360 web user interface. Images that are previewed via mobile app are compressed, are for preview informational purposes only, and not intended for diagnostic use beyond notification.

The device is intended for use as an additional tool for assisting study triage within existing patient pathways. It does not replace any part of the current standard of care. It is designed to assist in prioritization of studies for reading within a worklist, in addition to any other pre-existing formal or informal methods of study prioritization in place. Specifically, it does not remove cases from a reading queue and operates in parallel to the standard of care. This device is not intended to replace the usual methods of communication and transfer of information in the current standard of care.

Brainomix 360 Triage LVO notification capabilities enable clinicians to preview compressed and informational images through via mobile application with preview of unprocessed image attachments. Alternatively, the user may review unprocessed images via web user interface on a radiology workstation.

Here's a breakdown of the acceptance criteria and the study proving the device's performance, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: 308 CTA scans (studies)
• Data Provenance: Retrospective study. Data were obtained from 14 different hospitals and clinics in the U.S. The majority of patients were scanned at Mayo Clinic Rochester (N=129) and Boston Medical Centre (N=179), with 56 scans transferred from 11 hospitals in the Massachusetts area. The patient cohort was enriched to ensure an approximately equal balance of LVO positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age and gender) for generalizability.

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

• Number of Experts: Two ground truthers, with a third ground truther used in the event of disagreement.
• Qualifications: All truthers were US board-certified neuroradiologists.

4. Adjudication Method for the Test Set

• Adjudication Method: 2+1 (Two ABR-certified neuroradiologists reviewed each case, and a third neuroradiologist provided consensus in the event of disagreement).

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

• Was it done? No, the document only describes a standalone performance evaluation of the device.
• Effect size of human readers improving with AI vs. without AI assistance: Not applicable, as no MRMC study was performed or reported.

6. Standalone Performance Study

• Was it done? Yes, a standalone performance evaluation was done. The study assessed the device's image analysis in terms of sensitivity and specificity against a ground truth established by expert neuroradiologists.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus by US board-certified neuroradiologists.

8. Sample Size for the Training Set

• Sample Size: Over 1600 CT brain imaging studies.

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

• Ground Truth Establishment: The dataset used to train the algorithm was labeled by "trained radiologists" regarding the presence of LVO.

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Brainomix 360 e-MRI is an image processing software package to be used by trained professionals, including but not limited to physicians and medical technicians.

The software runs on a standard off-the-shelf computer or a virtual platform, such as VMware, and can be used to perform image viewing, processing, and analysis of images. Data and images are acquired through DICOM compliant imaging devices. This includes DICOM files uploaded through a web browser interface.

Brainomix 360 e-MRI provides both viewing and analysis capabilities for imaging datasets acquired with MRI including Perfusion Weighted Imaging (PWI) and Diffusion Weighted Imaging (DWI).

The DWI MRI analysis capabilities are used to visualize local water diffusion properties from the analysis of diffusion-weighted MRI data.

The MRI PWI analysis capabilities are for visualization and analysis of dynamic imaging data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to tissue flow (perfusion) and tissue blood volume.

Brainomix 360 e-MRI software allows for visualization of DICOM compliant MRI (Mage Resonance) digital images. The software has been designed to run with off-the-shelf physical or virtual servers and provides for viewing, quantification, analysis, and reporting, as an aid to physician diagnosis.

The software system consists of platform functionality and the e-MRI processing module. It provides both analysis and viewing capabilities for functional and dynamic imaging datasets acquired with MR including Diffusion Weighted Imaging (DWI) and Dynamic Susceptibility Contrast (DSC), which is the term used in the Brainomix 360 e-MRI software for perfusion-weighted imaging technique. The DWI capabilities are for visualization of local water diffusion properties from the analysis of diffusion-weighted MR data. The DSC capabilities are for the characterization of perfusion parameters in the injection of a contrast bolus, and visualization of these parameters.

e-MRI provides a wide range of basic image viewing, processing and manipulation functions, through multiple output formats. The Brainomix 360 platform has been designed to connect with other DICOM-compliant devices. This functionality enables the transfer of MRI scans from a Picture Archiving and Communication System (PACS) to Brainomix 360 e-MRI software for processing.

Here's a breakdown of the acceptance criteria and study information for Brainomix 360 e-MRI, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on establishing substantial equivalence to a predicate device (iSchemaView's RAPID) and does not explicitly list quantitative acceptance criteria in terms of performance metrics (e.g., sensitivity, specificity, Dice score with thresholds) or present detailed reported device performance in this summary. Instead, the "Performance Testing Summary" section indicates that "extensive performance validation testing and validation testing was conducted" and that "Brainomix 360 e-MRI met all design requirements and specifications."

However, the "Technological Characteristics" table directly compares the functionalities and capabilities of the proposed device against the predicate. This comparison implicitly serves as a form of acceptance criteria, where the proposed device is deemed acceptable if its features align with or are a subset of the predicate's, and risks are appropriately managed.

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

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective nature) for the validation studies. It only generally refers to "extensive performance validation testing."

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

The document does not explicitly state the number of experts used to establish ground truth or their specific qualifications (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set:

The document does not describe any adjudication method (e.g., 2+1, 3+1, none) for establishing ground truth for the test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size:

The document does not mention or describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. Therefore, no effect size of human readers' improvement with vs. without AI assistance is provided. The submission focuses on standalone device performance and substantial equivalence to a predicate.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

Yes, a standalone performance evaluation was done. The "Performance Testing Summary" states: "extensive performance validation testing and validation testing was conducted for the Brainomix 360 e-MRI module. This performance validation testing demonstrated that the module provides accurate representation of key processing parameters under a range of clinically relevant parameters and perturbations associated with the intended use of the software." This implies that the algorithm's output was assessed independently.

7. The Type of Ground Truth Used:

The document does not explicitly specify the type of ground truth used (e.g., expert consensus, pathology, outcomes data). Given the nature of image processing software for analyzing MRI data (PWI and DWI), it is highly probable that ground truth would have been established through expert review and interpretation of the imaging data, potentially with reference to clinical outcomes or other diagnostic information, but this is not confirmed in the summary provided.

8. The Sample Size for the Training Set:

The document does not provide any information regarding the sample size for the training set. It focuses on the validation for substantial equivalence rather than the development process.

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

The document does not provide information on how the ground truth for the training set was established, as it does not address the training set at all.

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Brainomix 360 Triage ICH is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of standard of care workflow.

Brainomix 360 Triage ICH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device is intended to be used for the triage of non-contrast CT images of the brain acquired from adult patients in the acute setting, within 24 hours of the acute symptoms, or where this is unclear, since last known well (LKW) time. It is not intended to detect isolated subarachnoid hemorrhage. The device sends notifications to a neurovascular specialist that a suspected intracranial hemorrhage has been identified and recommends review of those images. Images can be previewed through a mobile application.

Images that are previewed through the mobile application are compressed and are for informational purposes only and not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests. Brainomix 360 Triage ICH is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

Limitations:

· Brainomix 360 Triage ICH is not intended for mobile diagnostic use. Images viewed on a mobile platform are preview images and not for diagnostic interpretation.

• · Brainomix 360 Triage ICH has been validated and is intended to be used on GE and Philips scanners.
• · Brainomix 360 Triage ICH is not intended to detect isolated subarachnoid hemorrhage.
• · Brainomix 360 Triage ICH is not intended to be used on patients with recent (within 6 weeks) neurosurgery or endovascular neurointervention or recent (within 4 weeks) previous diagnosis of intracranial hemorrhage.

Contraindications:

Brainomix 360 Triage ICH is not suitable for use with scan data containing image features associated with:

• · tumors or abscesses
• · coils, shunts, embolization or movement artefacts

Brainomix 360 Triage ICH is not intended to be used for analyzing CT images in intracranial vascular pathologies such as arterial aneurysms, arteriovenous malformations or venous thrombosis.

Brainomix 360 Triage ICH is a radiological computer aided triage and notification software package compliant with the DICOM standard and running on an off-the-shelf physical or virtual server.

The Triage ICH module is a non-contrast CT processing module which operates within the integrated Brainomix 360 Platform to provide triage and notification of suspected intracranial hemorrhage (ICH). Brainomix 360 Triage ICH is a stand-alone software device which uses machine learning algorithms that uses advanced non adaptive imaging algorithms, artificial intelligence, and large data analytics to automatically identify suspected ICH on non-contrast CT (NCCT) imaging acquired from adult patients in the acute setting, within 24 hours of the onset of the acute symptoms, or where this is unclear, since last known well (LKW) time. The module is a priority notification to clinicians indicating the suspicion of ICH based on positive findings. Specifically, Brainomix 360 Triage ICH is optimized to detect and evaluate hyperdense volume in the parenchyma typically associated with acute intracranial hemorrhage (ICH). The Triage ICH module uses the basic services supplied by the Brainomix 360 Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.

Brainomix 360 Triage ICH notification capabilities enable clinicians to review images via mobile app notification. Alternatively, intended users can also access the notification (a "Suspected hemorrhage" flag) and straightened images via the Brainomix 360 web user interface. Images that are previewed via mobile app are compressed, are for preview informational purposes only, and not intended for diagnostic use beyond notification.

The device is intended for use as an additional tool for assisting study triage within existing pathways. It does not replace any part of the current standard of care. It is designed to assist in prioritization of studies for reading within a worklist, in addition to any other pre-existing formal methods of study prioritization in place. Specifically, it does not remove cases from a reading queue and operates in parallel to the standard of care. This device is not intended to replace the usual methods of communication and transfer of information in the current standard of care.

Here's a breakdown of the acceptance criteria and the study details for the Brainomix 360 Triage ICH device, based on the provided text:

1. Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

• Sample Size: 341 non-contrast Computed Tomography (NCCT) scans (studies).
• Data Provenance: The scans were obtained from 30 different hospitals and clinics in the U.S. The majority of patients (N=237) were scanned at Boston Medical Centre, with the remainder from 29 other referral hospitals in Massachusetts State. The data is retrospective.

3. Number of Experts and Qualifications for Ground Truth Establishment

• Number of Experts: Not explicitly stated, but mentioned as "experienced US board certified neuroradiologists."
• Qualifications of Experts: "Experienced US board certified neuroradiologists."

4. Adjudication Method for Test Set

• The text does not explicitly describe an adjudication method like 2+1 or 3+1. It states the ground truth was "established by experienced US board certified neuroradiologists," implying a consensus or individual expert review process without detailing a specific adjudication protocol.

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

• No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not done. The study assessed the standalone performance of the AI algorithm and compared its notification time to the standard of care as reported by the predicate device.

6. Standalone Performance Study (Algorithm Only)

• Yes, a standalone performance study was done. The "retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH."

7. Type of Ground Truth Used

• Expert Consensus: The ground truth was "established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain."

8. Sample Size for Training Set

• The document does not explicitly state the sample size used for the training set.

9. How Ground Truth for Training Set Was Established

• The document does not explicitly state how the ground truth for the training set was established. It only mentions that the device uses "machine learning algorithms that uses advanced non adaptive imaging algorithms, artificial intelligence, and large data analytics."

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Brainomix 360 e-CTP is an image processing software package to be used by trained professionals, including but not limited to physicians and medical technicians.

The software runs on a standard off-the-shelf computer or a virtual platform, such as VMware, and can be used to perform image viewing, processing, and analysis of images. Data and images are acquired through DICOM compliant imaging devices. This includes DICOM files uploaded through a web browser interface.

Brainomix 360 e-CTP provides viewing and analysis capabilities for imaging datasets acquired with CT Perfusion.

The CT Perfusion analysis capabilities are for visualization and analysis of dynamic imaging data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to tissue flow (perfusion) and tissue blood volume.

Brainomix 360 e-CTP software allows for visualization of DICOM compliant CT (Computed Tomography) digital images. The software has been designed to run with off-the-shelf physical or virtual servers and provides for viewing, quantification, analysis, and reporting, as an aid to physician diagnosis.

The software consists of one processing module:

• 1. e-CTP Module- provides both analysis and viewing capabilities for brain CT Perfusion datasets for characterization of perfusion parameters in the image following the injection of a contrast bolus, and visualization of these parameters.

The provided text does not contain detailed information about the acceptance criteria or a specific study proving the device meets those criteria. It mentions "extensive performance validation testing and software verification and validation testing was conducted" and that "this performance validation testing demonstrated that the module provides accurate representation of key processing parameters under a range of clinically relevant parameters and perturbations associated with the intended use of the software. Software performance, validation and verification testing demonstrated that the Brainomix 360 e-CTP met all design requirements and specifications."

However, it does not provide the following information from your request:

1. A table of acceptance criteria and the reported device performance: No specific criteria or performance metrics are listed.
2. Sample size used for the test set and the data provenance: Not mentioned.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not mentioned.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not mentioned.
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: Not mentioned.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The document states, "Brainomix 360 e-CTP software medical device may be used as a stand-alone tool," but it does not describe a standalone performance study.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not mentioned.
8. The sample size for the training set: Not mentioned.
9. How the ground truth for the training set was established: Not mentioned.

In summary, while the document asserts that testing was conducted and the device met requirements, it lacks the specific details requested regarding acceptance criteria, study design, and results.

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