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Rapid NCCT Stroke is a radiological computer aided triage and notification software indicated for use in the analysis of (1) nonenhanced head CT (NCCT) images. The device is intended to assist hospital networks and trained clinicians in workflow triage by flagging and communicating suspected positive findings of (1) head CT images for Intracranial Hemorrhage (ICH) and (2) NCCT large vessel occlusion (LVO) of the ICA and MCA-M1.

Rapid NCCT Stroke uses an artificial intelligence algorithm to analyze images and highlight cases with detected (1) ICH or (2) NCCT LVO on the Rapid 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 ICH or LVO findings via PACS, email or mobile device. 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 Rapid NCCT 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. Rapid NCCT Stroke is for Adults only.

Rapid NCCT Stroke (RNS) is a radiological computer-assisted triage and notification software device. RNS is a non-enhanced CT (NCCT) processing module which operates within the integrated Rapid Platform to provide triage and notification of suspected intracranial hemorrhage (ICH) and NCCT Large Vessel Occlusion (LVO) of the ICA and MCA-M1. The RNS is an AI/ML SaMD. The output of the module is a priority notification to clinicians indicating the suspicion of ICH or NCCT LVO. ICH analysis uses the ICH Algorithm to identify findings within the ICH algorithm; and the NCCT LVO suspicion uses the combined analysis of the ASPECTS and Hyperdense Vessel Sign (HVS) algorithms. The RNS module uses the basic services supplied by the Rapid Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.

The Rapid NCCT Stroke device is a radiological computer-aided triage and notification software for detecting intracranial hemorrhage (ICH) and large vessel occlusion (LVO) on non-enhanced head CT (NCCT) images.

Here's an analysis of its acceptance criteria and the study that proves it:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Sizes and Data Provenance

• Test Set Sample Size: 254 cases. These cases included:
  • ICH Positive: 26
  • LVO Positive: 115
  • Negative for ICH and LVO: 103
  • Excluded: 10 (due to age and technical inadequacy)
• Data Provenance: The study was a "retrospective, blinded, multicenter, multinational study." This indicates that the data was collected from multiple centers in various countries and that the analysis was performed on existing, pre-collected data. Specific countries are not mentioned.

3. Number of Experts and Qualifications for Ground Truth

• Ground Truth Establishment: The document mentions "expert reader truthing of the data." The specific number of experts is not explicitly stated for the ground truth establishment, but it is implied that multiple experts were involved given "expert reader truthing."
• Qualifications of Experts: The document refers to "human readers" including "neuroradiologists and general radiologists" in the context of the secondary clinical endpoints. This suggests that the experts involved in establishing ground truth would likely possess similar qualifications in radiology, with expertise in neurological imaging, to accurately identify ICH and LVO.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method like 2+1 or 3+1. It states that the ground truth was established by "expert reader truthing." This implies that a consensus or a well-defined process was used by the experts to determine the definitive diagnoses, but the specific mechanics of that process (e.g., number of readers, tie-breaking rules) are not detailed.

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

Yes, a form of MRMC comparative effectiveness study was done for LVO.

• Effect Size of Human Readers' Improvement with AI vs. without AI Assistance: The study did not directly assess how much human readers improve with AI assistance. Instead, it compared the standalone performance of the Rapid NCCT Stroke device to the performance of human readers (both general radiologists and a broader group of "all readers," which included experts) in identifying LVO.
  • Expert Non-inferiority: The device demonstrated non-inferiority to "overall readers" (experts and non-experts). The device's sensitivity was 0.635, while the sensitivity for "all readers" was 0.436. The difference in sensitivity (device vs. all readers) was 0.199 (95% CI: 0.055-0.34), indicating the device performed better than the overall human readers.
  • Non-expert Superiority: The device demonstrated superiority to "general radiologists". The device's sensitivity was 0.635, while the sensitivity for general radiologists was 0.409. The difference in sensitivity (device vs. general radiologists) was 0.226 (95% CI: 0.071-0.381), indicating the device performed better than general radiologists.
  • These results show that the standalone device performed better than human readers in terms of sensitivity for LVO detection. The study design doesn't provide an effect size for human reader improvement with AI assistance (i.e., a human-in-the-loop scenario).

6. Standalone (Algorithm Only) Performance Study

Yes, an algorithm-only standalone performance study was done.

• The reported sensitivities and specificities for ICH (Se: 0.962, Sp: 0.974) and LVO (Se: 0.635, Sp: 0.951) refer to the standalone performance of the Rapid NCCT Stroke device.
• The ICH algorithm's performance was noted to be "consistent with the ICH standalone module performance (K221456)," further confirming standalone evaluation.
• The comparison against human readers (secondary clinical endpoints) also used the device's standalone output for comparison.

7. Type of Ground Truth Used

The ground truth used was expert consensus (referred to as "expert reader truthing of the data").

8. Sample Size for the Training Set

The document does not provide the sample size for the training set. It only describes the test set used for performance validation.

9. How 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 focuses solely on the validation study and the ground truth for its test set.

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Viz 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.

Viz LVO uses an artificial intelligence algorithm to analyze images for findings suggestive of a pre-specified 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 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 has been identified and recommends review of those images. Images can be previewed through a mobile application. Viz LVO is intended to analyze terminal ICA and MCA-M1 vessels for LVOs.

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. Viz 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.

Viz LVO is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to analyze images for findings suggestive of a suspected large vessel occlusion and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Viz LVO was previously granted a de-novo as ContaCT (DEN170073); following the granting of the denovo the device name was changed to Viz LVO.

Viz LVO is a combination of software modules that allow for detection and notification of patients with a suspected large vessel occlusion. Viz LVO consists of an algorithm and mobile application software module.

The Viz LVO Image Analysis Algorithm (LVO Detection Algorithm) is a locked, artificial intelligence machine learning (AI/ML) software algorithm that analyzes CTA images of the head for a suspected large vessel occlusion (LVO). The LVO Detection Algorithm is hosted on Viz.ai's Backend Server and analyzes applicable stroke-protocoled CTA images of the head that are acquired on CT scanners and are forwarded to Viz.ai's Backend Server. Upon detection of a suspected LVO, the LVO Detection Algorithm sends a notification of the suspected finding.

The Viz LVO Mobile Notification Software is a software module that enables the end user to receive and toggle notifications for suspected large vessel occlusions identified by the LVO Detection Algorithm. The LVO Mobile Notification Software module is implemented into Viz.ai's generic nondiagnostic DICOM image viewer, Viz VIEW (formerly referred to as the Imaging Viewing Software in the previous submission, DEN170073), which displays CT scans that are sent to Viz.ai's Backend Server. When the Viz LVO Mobile Notification Software module is enabled for a user, the user can receive and toggle the notifications for patients with a suspected LVO, view a unique list of patients with a suspected LVO (as determined by the LVO Detection Algorithm), and view the non-diagnostic CT scan of the patient through the Viz VIEW mobile application. Image viewing through the mobile application interface is for informational purposes only and is not for diagnostic use.

I am sorry, but the provided text does not contain the requested information about acceptance criteria and the study that proves the device meets them. The document is a 510(k) summary for Viz LVO, a medical device, and it primarily focuses on establishing substantial equivalence to a predicate device (ContaCT) for a change in its indications for use.

Here's what the document does state regarding performance data:

• "Performance data was not included as part of the premarket notification. Supporting software verification and validation (V&V) testing were provided to demonstrate implementation of the device changes."

This indicates that this specific submission (K223042) did not involve new clinical performance studies to establish acceptance criteria or demonstrate device performance beyond software verification and validation to support the changes to the device. The substantial equivalence is based on the previously cleared predicate device (ContaCT DEN170073).

Therefore, I cannot provide:

1. A table of acceptance criteria and reported device performance.
2. Sample size and data provenance for a test set.
3. Number and qualifications of experts for ground truth.
4. Adjudication method.
5. MRMC comparative effectiveness study results.
6. Details of a standalone performance study.
7. Type of ground truth used.
8. Sample size for the training set.
9. How ground truth for the training set was established.

To find this information, you would typically need to consult the original 510(k) submission or de novo application for the predicate device, ContaCT (DEN170073), which likely contained the initial performance studies and acceptance criteria.

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CINA is a radiological computer aided triage and notification software indicated for use in the analysis of (1) non-enhanced head CT images and (2) CT angiographies of the head. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communicating suspected positive findings of (1) head CT images for Intracranial Hemorrhage (ICH) and (2) CT angiographies of the head for large vessel occlusion (LVO).

CINA uses an artificial intelligence algorithm to analyze images and highlight cases with detected (1) ICH or (2) LVO on a standalone Web application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected ICH or LVO findings. 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 diagnostic device.

The results of CINA 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.

CINA is a radiological computer-assisted triage and notification software device.

The software system is based on algorithm-programmed components and is comprised of a standard off-the-shelf operating system and additional image processing applications.

DICOM images are received, recorded and filtered before processing. The series are processed chronologically by running algorithms on each series to detect suspicious results of an intracranial hemorrhage (ICH) or a large vessel occlusion (LVO), then notifications on the flagged series are sent to the Worklist Application.

The Worklist Application (on premise) displays the pop-up notifications of new studies with suspected findings when they come in, and provides both active and passive notifications. Active notifications are in the form of a small pop-up containing patient name, accession number and the type of suspected findings (ICH or LVO). All the non-enhanced head CT images and head CT angiographies studies received by CINA device are displayed in the worklist and those on which the algorithms have detected a suspected finding (ICH or LVO) are marked with an icon (i.e., passive notification). In addition, a compressed, small black and white image that is marked "not for diagnostic use" is displayed as a preview function. This compressed preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for primary diagnosis beyond notification. Presenting the radiologist with notification facilitates earlier triage by allowing one to prioritize images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.

Here's a breakdown of the acceptance criteria and study details for the CINA device, based on the provided FDA 510(k) summary:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are implied by the performance goals for sensitivity and specificity. The reported performance for CINA met or exceeded these goals and was comparable to or better than the predicate/reference devices.

Table of Acceptance Criteria and Reported Device Performance

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Viz 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 care workflow.

Viz 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 analyzes non-contrast CT images of the brain acquired in the acute setting, and sends notifications to a neurovascular or neurosurgical specialist that a suspected intracranial hemorrhage has been identified and recommends review of those images. Images can be previewed through a mobile application.

lmages that are previewed through the mobile application may be 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. Viz 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.

Viz ICH is contraindicated for analyzing non-contrast CT scans that are acquired on scanners from manufacturers other than General Electric (GE) or its subsidiaries (i.e. GE Healthcare). This contraindication applies to NCCT scans that conform to all applicable Patient Inclusion Criteria, are of adequate technical image quality, and would otherwise be expected to be analyzed by the device for a suspected ICH.

Viz ICH is a software-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to an appropriate specialist, such as a neurovascular specialist or neurosurgeon, independent of the standard of care workflow. The system automatically receives and analyzes non-contrast CT (NCCT) studies of patients for image features that indicate the presence of an intracranial hemorrhage (ICH) using an artificial intelligence algorithm, and upon detection of a suspected ICH, sends a notification so as to alert a specialist clinician of the case.

Viz ICH consists of backend and mobile application component software. The Backend software includes a DICOM router and backend server. The DICOM router transmits NCCT images of the head acquired on a local healthcare network to the Backend Server. The Backend Server receives, stores, processes and serves received NCCT scans. The Backend Server also includes an artificial intelligence algorithm that analyzes the received NCCT images for image characteristics that indicate an intracranial haemorrhage (ICH) and, upon detection, sends a notification of the suspected finding to pre-determined specialists.

The Viz ICH Mobile Application software receives notifications generated by the Backend of suspected image findings and allows the notification recipient to view the analyzed NCCT images through a non-diagnostic viewer, as well as patient information that was embedded in the image metadata. Image viewing through the mobile application is for informational purposes only and is not intended for diagnostic use.

Here's a summary of the acceptance criteria and study details for Viz ICH, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: 261 non-contrast Computed Tomography (NCCT) scans (studies). Approximately equal numbers of positive (47%) and negative (53%) cases were included.
• Data Provenance: Retrospective study. Data obtained from two clinical sites in the U.S.

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

• Number of Experts: Not explicitly stated, but "trained neuro-radiologists" were used.
• Qualifications of Experts: "Trained neuro-radiologists". Specific years of experience are not mentioned.

4. Adjudication method for the test set

• The document implies a consensus-based ground truth ("ground truth, as established by trained neuro-radiologists"). However, the specific adjudication method (e.g., 2+1, 3+1) is not detailed.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study with human readers was not described. The study focused on the standalone performance of the AI algorithm and a comparison of notification times.

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

• Yes, a standalone performance study of the image analysis algorithm was conducted. The sensitivity and specificity reported are for the algorithm only.

7. The type of ground truth used

• Expert consensus, established by "trained neuro-radiologists," in the detection of intracranial hemorrhage (ICH).

8. The sample size for the training set

• The sample size for the training set is not provided in the document. The information focuses only on the test set.

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

• The method for establishing ground truth for the training set is not described in the provided document.

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BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of head CTA images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive findings of Large Vessel Occlusion (LVO) pathologies.

BriefCase uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings on a standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected findings. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device.

The results of BriefCase are intended to be used in conjunction with other patient information and based on their professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

BriefCase is a radiological computer-assisted triage and notification software device. The software system is based on an algorithm programmed component and is comprised of a standard off-the-shelf operating system, the Microsoft Windows server 2012 64bit, and additional applications, which include PostgreSQL. DICOM module and the BriefCase Image Processing Application. The device consists of the following three modules: (1) Aidoc Hospital Server (AHS) for image acquisition; (2) Aidoc Cloud Server (ACS) for image processing; and (3) Aidoc Worklist Application for workflow integration, installed on the radiologist' desktop and provides the user interface in which notifications from the BriefCase software are received.

DICOM images are received, saved, filtered and de-identified before processing. Series are processed chronologically by running an algorithm on each series to detect suspected findings and then notifications on flagged series are sent to the Worklist desktop application, thereby prompting preemptive triage and prioritization. The user may opt to filter out notifications by pathology, e.g. a chest radiologist may choose to filter out notifications on Large Vessel Occlusion (LVO) cases, and a neuro-radiologist would opt to divert Pulmonary Embolism (PE) notifications. In addition, where several medical centers are linked to a shared PACS, a user may read cases for a certain center but not for another, and thus may opt to filter out notification by center. Activating the filter does not impact the order in which notifications are presented in the Aidoc worklist application.

The Worklist Application displays the pop-up text notifications of new studies with suspected findings when they come in. Notifications are in the form of a small pop-up containing patient name, accession number and the relevant pathology (e.g., LVO). A list of all incoming cases with suspected findings is also displayed. Hovering over a notification or a case in the worklist pops up a compressed, small black and white, unmarked image that is captioned "not for diagnostic use" and is displayed as a preview function. This compressed preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for primary diagnosis beyond notification.

Presenting the radiologist with notification facilitates earlier triage by prompting the user to assess the relevant original images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.

Based on the provided text, here's a detailed description of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: 383 cases
• Data Provenance: The data was collected from 3 US-based clinical sites and was retrospective.

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

The document does not explicitly state the number of experts or their specific qualifications (e.g., years of experience) used to establish the ground truth for the test set. It only mentions "reviewers" identified "True Positive cases."

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for the test set.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted. The study evaluated the standalone performance of the AI algorithm and compared its notification time to a predicate device. It did not assess human reader performance with and without AI assistance.

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

Yes, a standalone study was done. The pivotal study evaluated the BriefCase software's performance in identifying LVOs, reporting its sensitivity and specificity, which implies an algorithm-only evaluation. The statement "The BriefCase time-to-notification includes the time to get the DICOM exam, de-identify it, upload it to the cloud, analyze and send a notification on a positive suspect case back to the worklist application" further supports an algorithm-only performance measurement before any human interaction.

7. The Type of Ground Truth Used

The ground truth was established by "reviewers." The document implies it's an expert consensus or expert interpretation of the images, as "True Positive cases (i.e., identified as positive both by the reviewers as well as the BriefCase device)" are mentioned. It does not mention pathology or outcomes data as ground truth.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set. It only states that the device uses an "artificial intelligence algorithm trained on medical images" and an "artificial intelligence algorithm with database of images."

9. 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 mentions that the algorithm was "trained on medical images."

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