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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

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Methinks CTA Stroke is a radiological computer aided triage and notification software, 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.

Methinks CTA Stroke 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 to PACS and/or 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 an image viewer. Methinks CTA Stroke is intended to analyze terminal ICA, MCA-M1 and MCA-M2 vessels for LVOs.

Images that are previewed 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. Methinks CTA Stroke 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.

Methinks CTA Stroke is a software-only device which is intended to be used by trained physicians involved in the management of Acute Stroke (AS) patients at emergency settings or other departments across the stroke care pyramid model. They include trained physicians such as emergency physicians, neurologists, general radiologists, neurovascular interventionists, neuroradiologists and any trained stroke professionals.

The target patients (intended patient population) are male and female in the adult population (above 21 years old) with suspected Acute Stroke.

The Methinks CTA Stroke device analyzes Computed Tomography Angiography (CTA) images from the intended patient population to identify suspected Large Vessel Occlusions (LVO). This information is to be used in conjunction with other patient information by a professional to assist with triage/prioritization of medical images.

The input of the software is Computed Tomography Angiography (CTA) in DICOM format from patients suspected of Acute Stroke. The outputs of the software are notifications sent to the trained physicians intended to be used in conjunction with other patient information for professional judgment to assist with triage/prioritization.

Here is a comprehensive breakdown of the acceptance criteria and the study proving the Methinks CTA Stroke device meets those criteria, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Study Details for Methinks CTA Stroke

Context: The Methinks CTA Stroke device is a radiological computer-aided triage and notification software that uses an AI algorithm to analyze CT angiogram images for findings suggestive of a Large Vessel Occlusion (LVO) and notifies a neurovascular specialist.

1. Table of Acceptance Criteria and Reported Device Performance

The direct acceptance criteria (pre-specified performance goals) are explicitly stated in the document for Sensitivity and Specificity. The time to notification is also presented as a performance metric.

Note: While the document states "Sensitivity and specificity exceed the pre-specified performance goals for LVO," the specific numerical thresholds for these goals are not provided in the extract.

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

• Test Set Sample Size: 336 cases
  • LVO Positive: 110 cases
  • LVO Negative: 226 cases
• Data Provenance: Retrospective, blinded, multicenter, multinational study. Institutions included in the validation study were different from institutions included in training, ensuring separation and representativity. This was verified by checking countries, states, and ZIP codes. The specific countries are not mentioned beyond "multinational."

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: Two primary readers, with a third expert for adjudication. (Total of 3 experts involved in establishing ground truth for any given case of disagreement)
• Qualifications of Experts: US board-certified neuroradiologists. (No years of experience are specified).

4. Adjudication Method for the Test Set

• Method: Majority vote (2+1 adjudication). Ground truth was established by two US board-certified neuroradiologists. If they disagreed regarding LVO findings, a third ground truther established the final ground truth based on the majority vote.

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

• The provided document does not indicate that an MRMC comparative effectiveness study was done looking at how human readers improve with AI vs. without AI assistance. The study focuses solely on the standalone performance of the AI algorithm.

6. Standalone Performance (Algorithm Only)

• Yes, a standalone performance study was done. The reported Sensitivity and Specificity values (98.2% and 91.6% respectively) represent the performance of the AI algorithm itself in identifying LVOs, without human-in-the-loop assistance for the core performance metrics.

7. Type of Ground Truth Used

• Ground Truth Type: Expert consensus. Specifically, it was established by two US board-certified neuroradiologists, with a third neuroradiologist for adjudication in case of disagreement.

8. Sample Size for the Training Set

• The document does not explicitly state the sample size used for the training set. It only mentions that "Institutions included in the validation study were different from institutions included in training," but the training dataset size is not provided.

9. How Ground Truth for the Training Set Was Established

• The document does not explicitly describe how ground truth for the training set was established. It only details the ground truth establishment process for the test set. It is implied that similar expert review would have been used, but no specific methodology or number of readers are provided for the training data.

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Rapid CTA 360 is a radiological computer aided triage and notification software indicated for use in the analysis of CTA adult head images. The device is intended to assist hospital networks and trained clinicians in workflow triage by flagging and communication of suspected positive Large and Medium Vessel Occlusion findings in head CTA images including the ICA (C1-C5), MCA (M1-M3), ACA, PCA, Basilar and Vertebral vascular segments.

Rapid CTA 360 uses an AI software algorithm to analyze images and highlight cases with suspected occlusion on a server or standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected LVO and MVO findings. Notifications include compressed preview images. These 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 is not intended to be used as a diagnostic device.

The results of Rapid CTA 360 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 responsible for viewing full images per the standard of care.

Rapid CTA 360 device is a radiological computer-assisted Triage and Notification Software device using AI/ML. The Rapid CTA 360 processing module operates within the integrated Rapid Platform to provide triage and notification of suspected large and medium vessel neuro-occlusions. The Rapid CTA 360 software analyzes input Head and Neck CTA images that are provided in DICOM format and provides notification of suspected positive results. The device does not alter the original medical image and is not intended to be used as a diagnostic device.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) Clearance Letter:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: 403 CTA cases
• Data Provenance: The data was collected from multiple sites (not explicitly stated which countries, but the training data was primarily US, which might suggest a similar distribution for the test set or at least a representative one). The cases were selected to cover patient demographics (age, gender), manufacturer distributions (GE, Toshiba, Siemens, Philips scanners), and confounders. The data was "collected and blinded prior to use, per internal data management procedures which includes isolation of development and product validation cohorts," implying a retrospective collection, but carefully separated from the training data.

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

• Number of Experts: 3 experts
• Qualifications of Experts: Not explicitly stated beyond "experts."

4. Adjudication method for the test set

• Adjudication Method: 2 out of 3 (2:3 concurrence). This means that for a case to be considered positive or negative for ground truth, at least two of the three experts had to agree on the finding.

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

• No MRMC comparative effectiveness study involving human readers with and without AI assistance was mentioned in the provided text. The study focused on the standalone performance of the AI device.

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

• Yes, a standalone performance validation was explicitly stated as being conducted: "Final device validation included standalone performance validation, per the special controls."

7. The type of ground truth used

• Ground Truth Type: Expert consensus. The document states, "ground truth established by 3 experts (2:3 concurrence)."

8. The sample size for the training set

• Training Set Sample Size: 6264 cases

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

• The document implies that the ground truth for the training set was established through expert review and annotation, as the cases were used for "Algorithm development, including training and testing." It mentions the selection criteria for cases (demographics, scanner manufacturers, confounders) which would likely lead to expert-verified labels as ground truth, but the exact method (e.g., specific number of experts, adjudication) for the training set is not detailed in the same way as for the test set.

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Methinks NCCT Stroke is a radiological computer aided triage and notification software indicated for use in the analysis of (1) non-contrast head CT (NCCT) images. The device is intended to assist hospital networks and trained physicians in workflow triage by flagging and communicating suspected positive findings of (1) Intracranial Hemorrhage (ICH) and (2) Large Vessel Occlusion (LVO) of the ICA, MCA-M1 and MCA-M2.

Methinks NCCT Stroke uses an artificial intelligence algorithm to analyze images and highlight cases with suspected (1) ICH and (2) LVO 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 and/or notifications. Notifications include 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 Methinks 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. Methinks NCCT Stroke is for adults only.

Methinks NCCT Stroke is a radiological computer-assisted triage and notification software device. The device receives Non-Contrast Computed Tomography (NCCT) images and processes them to provide triage and notification prioritization of suspected Intracranial Hemorrhage (ICH) and Large Vessel Occlusion (LVO) of the ICA, MCA-M1 and MCA-M2. The Methinks NCCT Stroke device is an AI/ML Software as a Medical Device. The outputs of the device are intended to be used by trained clinicians in the prioritization of patients with suspected ICH and/or LVO.

The provided FDA 510(k) clearance letter for the Methinks NCCT Stroke device details the acceptance criteria and the study that proves the device meets these criteria. Here's a breakdown of the requested information:

Acceptance Criteria and Reported Device Performance

Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implied by the reported performance metrics, primarily sensitivity (Se) and specificity (Sp), for both Intracranial Hemorrhage (ICH) and Large Vessel Occlusion (LVO) detection. The document states that "Sensitivity and specificity exceed the pre-specified performance goals for ICH and LVO," although the exact numerical "goals" are not explicitly stated. The performance of the device against human readers is also an implicit acceptance criterion.

Study Information

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

   • ICH Test Set: 358 cases (132 ICH Positive, 226 ICH Negative)
   • LVO Test Set: 335 cases (110 LVO Positive, 225 LVO Negative)
   • Data Provenance: Retrospective, blinded, multicenter, multinational study.

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

   • The document implies that ground truth for the initial performance evaluation (Se and Sp for ICH and LVO) was established through "expert reader truthing of the data." The number and qualifications of these specific experts for ground truth establishment are not explicitly stated beyond "expert reader."
   • For the reader study, there were 4 readers involved: 2 "expert neuroradiologists" and 2 "general radiologists (non-experts)." Their specific years of experience or other detailed qualifications are not provided beyond these labels.

3. Adjudication method for the test set:

   • The document mentions "expert reader truthing of the data" for establishing ground truth but does not specify a detailed adjudication method (e.g., 2+1, 3+1). For the reader study, the individual performance of the readers is provided, implying that their interpretations were compared against the established ground truth, but not that they formally adjudicated for the ground truth itself within the study.

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 MRMC comparative study was done comparing the device's performance to human readers (radiologists) without AI assistance.
   • Effect Size of AI vs. Human Readers (Standalone AI vs. Human Alone):
     • LVO Sensitivity:
       • Methinks NCCT-LVO: 73.6%
       • Expert Neuroradiologists (R1 + R2): 50.0%
       • General Radiologists (R3 + R4): 37.7%
     • Difference in Sensitivity (Effect Size):
       • Methinks NCCT-LVO vs. Experts: 23.6% (95%CI: 8.5% - 38.7%), showing superiority of the device.
       • Methinks NCCT-LVO vs. Non-experts: 35.9% (95%CI: 16.0% - 42.9%), also showing superiority of the device.
   • The study does not report how much human readers improve with AI assistance (i.e., human-in-the-loop performance). It focuses on the standalone performance of the AI compared to human readers working without AI.

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

   • Yes, a standalone performance evaluation of the Methinks NCCT Stroke algorithm was done for both ICH and LVO detection. The reported sensitivity and specificity metrics (e.g., ICH Se: 94.7%, Sp: 99.5%; LVO Se: 76.4%, Sp: 91.1%) are for the algorithm only.

6. The type of ground truth used:

   • The ground truth for the test set was established by "expert reader truthing of the data." This implies a consensus of medical experts, likely radiologists or neuroradiologists, reviewing the images. It is not explicitly stated if pathology, surgical findings, or long-term clinical outcomes were used to confirm the ground truth.

7. The sample size for the training set:

   • The document does not specify the sample size for the training set. It only mentions the test set sizes.

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 mentions the "expert reader truthing of the data" in the context of the performance validation (test set).

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BriefCase-Triage is a radiological computer aided triage and notification software indicated for use in the analysis of CT chest, abdomen, or chest/abdomen exams with contrast (CTA and CT with contrast) in adults or transitional adolescents aged 18 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of suspected positive findings of Aortic Dissection (AD) pathology.

BriefCase-Triage 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-Triage are intended to be used in conjunction with other patient information and based on their professional judgment, to assist with triage/ prioritization.

Briefcase-Triage is a radiological computer-assisted triage and notification software device. The software is based on an algorithm programmed component and is intended to run on a linux-based server in a cloud environment.

The Briefcase-Triage receives filtered DICOM Images, and processes them chronologically by running the algorithms on each series to detect suspected cases. Following the AI processing, the output of the algorithm analysis is transferred to an image review software (desktop application). When a suspected case is detected, the user receives a pop-up notification and is presented with a compressed, low-quality, grayscale image that is captioned "not for diagnostic use, for prioritization only" which is displayed as a preview function. This 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 users with worklist prioritization facilitates efficient 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.

Here's a detailed breakdown of the acceptance criteria and study findings for BriefCase-Triage, based on the provided FDA 510(k) clearance letter:

1. Table of Acceptance Criteria and Reported Device Performance

• Sensitivity: 92.7% (95% CI: 88.2%, 95.8%). The lower bound (88.2%) is > 80%.
• Specificity: 92.8% (95% CI: 89.2%, 95.4%). The lower bound (89.2%) is > 80%.

Additional Operating Points (AOPs) meeting criteria:

• AOP1: Sensitivity 95.6% (95% CI: 91.8%-98.0%), Specificity 88.2% (95% CI: 84.0%-91.6%)
• AOP2: Sensitivity 94.1% (95% CI: 90.0%-96.9%), Specificity 89.8% (95% CI: 85.8%-93.0%)
• AOP3: Sensitivity 89.3% (95% CI: 84.2%-93.2%), Specificity 94.7% (95% CI: 91.6%-97.0%)
• AOP4: Sensitivity 86.3% (95% CI: 80.9%-90.7%), Specificity 97.7% (95% CI: 95.3%-99.1%) |
  | Secondary Endpoints (Comparability with Predicate) | Time-to-notification metric for the Briefcase-Triage software should demonstrate comparability with the predicate device. | Briefcase-Triage (Subject Device): Mean time-to-notification = 10.7 seconds (95% CI: 10.5-10.9)
  Predicate AD: Mean time-to-notification = 38.0 seconds (95% CI: 35.5-40.4)

The subject device's time-to-notification is faster than the predicate, demonstrating comparability and improvement in time savings. |

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

• Sample Size: 509 cases.
• Data Provenance:
  • Country of origin: 5 US-based clinical sites.
  • Retrospective or Prospective: Retrospective.
  • Data Sequestration: Cases collected for the pivotal dataset were "all distinct in time or center from the cases used to train the algorithm," and "Test pivotal study data was sequestered from algorithm development activities."

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

• Number of Experts: Three (3) senior board-certified radiologists.
• Qualifications: "Senior board-certified radiologists." (Specific years of experience are not provided.)

4. Adjudication Method for the Test Set

• The text states "the ground truth, as determined by three senior board-certified radiologists." This implies a consensus-based adjudication, likely 3-0 or 2-1 (majority vote), but the exact method (e.g., 2+1, 3+1) is not explicitly detailed.

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

• Was it done? No.
• The study primarily focused on the standalone performance of the AI algorithm compared to ground truth and a secondary comparison of time-to-notification with a predicate device. It did not evaluate human reader performance with and without AI assistance.

6. Standalone Performance Study

• Was it done? Yes.
• The study evaluated the algorithm's performance (sensitivity, specificity, PPV, NPV, PLR, NLR) in identifying AD pathology without human intervention as a primary and secondary endpoint. The device's output is "flagging and communication of suspected positive findings" and "notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification," confirming a standalone function.

7. Type of Ground Truth Used

• Ground Truth: Expert Consensus, specifically "as determined by three senior board-certified radiologists."

8. Sample Size for the Training Set

• The document states, "The algorithm was trained during software development on images of the pathology." However, it does not specify the sample size for the training set. It only mentions that the pivotal test data was "distinct in time or center" from the training data.

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

• "As is customary in the field of machine learning, deep learning algorithm development consisted of training on labeled ("tagged") images. In that process, each image in the training dataset was tagged based on the presence of the critical finding."
• While it indicates images were "labeled ("tagged")" based on the "presence of the critical finding," it does not explicitly state who established this ground truth for the training set (e.g., experts, pathology, etc.). It's implied that medical professionals were involved in the labeling process, but no specific number or qualification is provided for the training set.

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Annalise Enterprise is a device designed to be used in the medical care environment to aid in triage and prioritization of studies with features suggestive of the following findings:

• pleural effusion* [1]
• pneumoperitoneum* [2]
• pneumothorax
• tension pneumothorax
• vertebral compression fracture* [3]

*See additional information below.

The device analyzes studies using an artificial intelligence algorithm to identify findings. It makes study-level output available to an order and imaging management system for worklist prioritization or triage.

The device is not intended to direct attention to specific portions of an image and only provides notification for suspected findings.

Its results are not intended:

• to be used on a standalone basis for clinical decision making
• to rule out specific findings, or otherwise preclude clinical assessment of chest X-ray studies

Intended modality:
Annalise Enterprise identifies suspected findings in digitized (CR) or digital (DX) chest X-ray studies.

Intended user:
The device is intended to be used by trained clinicians who are qualified to interpret chest X-ray studies as part of their scope of practice.

Intended patient population:
The intended population is patients who are 22 years or older.

Additional information:
The following additional information relates to the findings listed above:

[1] Pleural effusion

• specificity may be reduced in the presence of scarring and/or pleural thickening
• standalone performance evaluation was performed on a dataset that included supine and erect positioning
• use of this device with prone positioning may result in differences in performance

[2] Pneumoperitoneum

• standalone performance evaluation was performed on a dataset that included supine and erect positioning where most cases were of unilateral right-sided and bilateral pneumoperitoneum
• use of this device with prone positioning and for unilateral left-sided pneumoperitoneum may result in differences in performance

[3] Vertebral compression fracture

• intended for prioritization or triage of worklists of Bone Health and Fracture Liaison Service program clinicians
• standalone performance evaluation was performed on a dataset that included only erect positioning
• use of this device with supine positioning may result in differences in performance

Annalise Enterprise is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on chest X-ray studies in the medical care environment. The findings identified by the device include pneumothorax, tension pneumothorax, pleural effusion, pneumoperitoneum and vertebral compression fracture.

Radiological findings are identified by the device using an AI algorithm – a convolutional neural network trained using deep-learning techniques. Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers including. This dataset, which contained over 750,000 chest X-ray imaging studies, was labelled by trained radiologists regarding the presence of the findings of interest.

The performance of the device's AI algorithm was validated in a standalone performance evaluation, in which the case-level output from the device was compared with a reference standard ('ground truth'). This was determined by two ground truthers, with a third truther used in the event of disagreement. All truthers were US board-certified radiologists.

The device interfaces with image and order management systems (such as PACS/RIS) to obtain chest X-ray studies for processing by the AI algorithm. Following processing, if any of the clinical findings of interest are identified in the study, the device provides a notification to the image and order management system for prioritization of that study in the worklist. This enables users to review the studies containing features suggestive of these clinical findings earlier than in the standard clinical workflow. It is important to note that the device will never decrease a study's existing priority in the worklist. This ensures that worklist items will never have their priorities downgraded based on AI results.

The device workflow is performed parallel to and in conjunction with the standard clinical workflow for interpretation of chest X-ray studies. The device is intended to aid in prioritization and triage of radiological medical images only.

The Annalise Enterprise device is designed to aid in the triage and prioritization of chest X-ray studies by identifying features suggestive of several findings. The following outlines the acceptance criteria and the study conducted to prove the device meets these criteria.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for each finding are implicitly demonstrated by the reported Area Under the Curve (AUC), Sensitivity, and Specificity values, aiming for high performance in triaging positive cases while minimizing false positives. The reported device performance for each finding at various operating points is:

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

• Test Set Sample Size: The standalone performance evaluation was conducted on a total dataset of 3,252 cases.
• Data Provenance: The data was collected retrospectively and anonymized. Cases were collected consecutively from four US hospital network sites. The datasets included a variety of patient demographics (gender, age, ethnicity, race) and technical parameters (imaging equipment make, model), indicating a diverse geographic (US) and technical (various scanner manufacturers: Agfa, Carestream, Fujifilm, GE Healthcare, Kodak, Konica Minolta, McKesson, Philips, Siemens, Varian) origin.

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

• Number of Experts: At least two ABR-certified radiologists were used for each de-identified case. A third radiologist was used in the event of disagreement.
• Qualifications: All truthers were US board-certified radiologists who interpret chest X-rays as part of their regular clinical practice and were protocol-trained.

4. Adjudication Method for the Test Set

The adjudication method used was 2+1 consensus. Each deidentified case was annotated by at least two ground truthers (radiologists), and consensus was determined by these two. In the event of disagreement between the first two, a third ground truther was used to resolve the discrepancy and establish the final ground truth.

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

The provided information does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to assess how human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the AI algorithm and its impact on triage effectiveness (turn-around time).

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

Yes, a standalone performance evaluation was done. The key results table and associated metrics (AUC, Sensitivity, Specificity) are specifically for the device's AI algorithm independent of human intervention. The study describes "case-level output from the device was compared with a reference standard ('ground truth')", confirming a standalone evaluation.

7. Type of Ground Truth Used

The ground truth used was expert consensus, established by multiple US board-certified radiologists using a 2+1 adjudication method.

8. Sample Size for the Training Set

The training dataset used to train the Convolutional Neural Network (CNN) algorithm contained over 750,000 chest X-ray imaging studies.

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

The studies in the training dataset were labelled by trained radiologists regarding the presence of the findings of interest. The document does not specify the exact number of radiologists or the specific consensus or adjudication method used for the training set, only that they were "trained radiologists".

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syngo.CT LVO Detection is a radiological post-processing application for the analysis of CT angiography (CTA) head images. syngo.CT LVO Detection supports computer-aided triage, and it addresses vascular abortions in the CTA of the brain, commonly referred to as large vessel occlusion (LVO), in the ICA, M1, and M2 segment. It is intended for all patient populations of age ≥ 22 years, without any of the following contraindications: old infarcts or other diseases impacting the brain vasculature (for example, brain tumors), metal artifacts (for example, coils), surgical signs in the images. The output for triage is intended for informational purposes only. It is not intended for diagnostic use and does not alter the original medical image.

The subject device syngo.CT LVO Detection is an image processing software that utilizes artificial intelligence learning algorithms to support qualified clinicians (Radiologists, Neuroradiologists, Neurologists) in prioritization of CT-angiography images by algorithmically identifying findings suspicious of a large vessel occlusion and providing notification to the user. syngo.CT LVO Detection provides a reproducible detection of large vessel occlusions (LVO) on contrast-enhanced CT examinations of the head for detection of ICA, M1, and M2 vessel occlusions in patients suspected of having stroke related circulation occlusion. syngo.CT LVO Detection analyses CT-angiography (CTA) images of the head. The subject device provides a pipeline for the analysis and identification of potential LVO The output which can be send to an external notification device does not highlight or direct attention of the reading physician to any portion of the image.

Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) clearance letter for syngo.CT LVO Detection:

Acceptance Criteria and Reported Device Performance

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Rayvolve PTX-PE is a radiological computer-assisted triage and notification software that analyzes chest x-ray images (Postero-Anterior (PA) or Antero-Posterior (AP)) of patients 18 years of age or older for the presence of pre-specified suspected critical findings (pleural effusion and/or pneumothorax).

Rayvolve PTX-PE uses an artificial intelligence algorithm to analyze the images for features suggestive of critical findings and provides study-level output available in DICOM node servers for worklist prioritization or triage.

As a passive notification for prioritization-only software tool within the standard of care workflow, Rayvolve PTX-PE does not send a proactive alert directly to a trained medical specialist.

Rayvolve PTX-PE is not intended to direct attention to specific portions of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making.

Rayvolve PTX-PE is a software-only device designed to help healthcare professionals. It's a radiological computer-assisted triage and notification software that analyzes chest x-ray imaqes (Postero-Anterior (PA) or Antero-Posterior (AP)) of patients of 18 years of age or older for the presence of pre-specified suspected critical findings (pleural effusion and/or pneumothorax). It is intended to work in combination with DICOM node servers.

Rayvolve PTX-PE has been developed to use the current edition of the DICOM image standard. DICOM is the international standard for transmitting, storing, retrieving, printing, processing, and displaying medical imaging.

Using the DICOM standard allows Rayvolve PTX-PE to interact with existing DICOM node servers (eg .: PACS), and clinical-grade image viewers. The device is designed to run on a cloud platform and be connected to the radiology center's local network. It can also interact with the DICOM Node server.

When remotely connected to a medical center DICOM Node server, the software utilizes Al-based analysis algorithms to analyze chest X-rays for features suggestive of critical findings and provide study-level outputs to the DICOM node server for worklist prioritization. Following receipt of chest X-rays, the software device automatically analyzes each image to detect features suggestive of pneumothorax and/or pleural effusion.

Rayvolve PTX-PE filters and downloads only X-rays with organs determined from the DICOM Node server.

As a passive notification for prioritization-only software tool within the standard of care workflow, Rayvolve PTX-PE does not send a proactive alert directly to a trained health professional. Rayvolve PTX-PE is not intended to direct attention to a specific portion of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making.

Rayvolve PTX-PE does not intend to replace medical doctors. The instructions for use are strictly and systematically transmitted to each user and used to train them on Rayvolve's use.

AZmed's Rayvolve PTX-PE is a radiological computer-assisted triage and notification software designed to analyze chest x-ray images for the presence of suspected pleural effusion and/or pneumothorax. The device's performance was evaluated through a standalone study to demonstrate its effectiveness and substantial equivalence to a predicate device (Lunit INSIGHT CXR Triage, K211733).

Here's a breakdown of the acceptance criteria and the study proving the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for Rayvolve PTX-PE are implicitly derived from demonstrating performance comparable to or better than the predicate device, especially regarding AUC, sensitivity, and specificity for detecting pleural effusion and pneumothorax, as well as notification time. The predicate's performance metrics are used as a benchmark.

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

• Sample Size: The test set for the standalone study consisted of 1000 radiographs for the Pneumothorax group and 1000 radiographs for the Pleural Effusion group. For each group, positive and negative images represented approximately 50%.
• Data Provenance: The document does not explicitly state the country of origin of the data or whether it was retrospective or prospective.

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

The document does not provide details on the number of experts or their specific qualifications (e.g., years of experience as a radiologist) used to establish the ground truth for the test set.

4. Adjudication Method for the Test Set

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

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 conducted. The performance assessment was a standalone study evaluating the algorithm's performance only. The document explicitly states: "AZmed conducted a standalone performance assessment for Pneumothorax and Pleural Effusion in worklist prioritization and triage." Therefore, there is no effect size of how much human readers improve with AI vs. without AI assistance reported in this document.

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

Yes, a standalone performance assessment (algorithm only without human-in-the-loop) was performed. The results presented in the table above and in the "Bench Testing" section are from this standalone evaluation.

7. The Type of Ground Truth Used

The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data). However, for a diagnostic AI device, it is standard practice to establish ground truth through a panel of qualified medical experts (e.g., radiologists) providing consensus reads, often with access to additional clinical information or follow-up. Given the nature of the findings (pleural effusion and pneumothorax on X-ray), it is highly likely that expert interpretations served as the ground truth.

8. The Sample Size for the Training Set

The document does not specify the sample size used for the training set of the AI model. The provided information focuses on the performance evaluation using an independent test set.

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

The document does not detail how the ground truth for the training set was established. This information is typically proprietary to the developer's internal development process and is not always fully disclosed in 510(k) summaries.

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JLK-SDH is a notification-only, parallel workflow tool that is intended to assist trained radiologists to identify and communicate images of specific patients to a specialist, independent of the standard of care workflow.

JLK-SDH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate user 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 head for subdural hemorrhage (SDH) and sends notifications to a clinician that a suspected SDH has been identified and recommends a review of those images. Images can be previewed and compressed through PACS and mobile applications.

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.

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

JLK-SDH is a radiological computer-assisted triage and notification (CADt) software package compliant with the DICOM standard. The device functions as a Non-Contrast Computed Tomography (NCCT) processing module, providing triage and notification for suspected hemispheric subdural hemorrhage (SDH). It serves as a notification-only, parallel workflow tool for hospital networks and trained clinicians. The device helps to identify and communicate specific patient images to trained radiologists, independent of the standard of care workflow. Utilizing an artificial intelligence algorithm, the system automatically receives and analyzes NCCT studies for image features indicating the presence of SDH and sends a notification to alert a radiologist of the case.

This algorithm, hosted on JLK servers, is designed to analyze non-contrast CT images of the head acquired on CT scanners and forwarded to JLK servers. The mobile software module that enables user to receive and toggle notifications for suspected subdural hemorrhages identified by the JLK-SDH Image Analysis Algorithm. Users can view a patient list, and nondiagnostic CT scans through the mobile application. Image viewing through the mobile application interface is for non-diagnostic purposes only.

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

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for the Test Set and Data Provenance

• Sample Size: 560 NCCT scans
  • 174 SDH positive cases
  • 386 SDH negative cases
• Data Provenance: Retrospective study. Scans were obtained from various regions in the U.S.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Three.
• Qualifications: All truthers were US board-certified neuroradiologists.

4. Adjudication Method for the Test Set

• Adjudication Method: 2+1 truther scheme. Ground truth was determined by two neuroradiologists, with a third neuroradiologist intervening in cases of disagreement. (28 cases were sent to the third truther).

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

• Was an MRMC study done? No, the text describes a standalone performance evaluation of the device's AI algorithm.

6. Standalone (Algorithm Only) Performance

• Was a standalone performance study done? Yes. The performance data section explicitly states, "JLK, Inc. performed a standalone performance in accordance with the §892.2080 special controls to demonstrate adequate clinical performance of the JLK-SDH module."

7. Type of Ground Truth Used

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

8. Sample Size for the Training Set

• Sample Size: 29,524 non-contrast CT (NCCT) scans
  • 3,330 patients had SDH
  • 11,732 had different kinds of intracranial hemorrhage (IPH, IVH, SAH, or EDH)
  • 14,462 patients did not have any intracranial hemorrhage

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

• The document does not explicitly detail the exact method for establishing ground truth for the training set. It only mentions that the images "had been obtained in patients with and without intracranial hemorrhage" and categorizes them by the type of hemorrhage. While it suggests clinical diagnoses, the specific process (e.g., expert review, clinical reports, pathology) used to label these training cases is not described.

Clarification on "Acceptance Criteria"
The document states that the "primary endpoints, sensitivity and specificity, both exceeded 80%." This implies that >80% for both sensitivity and specificity served as the acceptance criteria for the standalone performance study. For time-to-notification, the acceptance criterion was to 'meet the target' established by the predicate device.

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EFAI CHESTSUITE XR MALPOSITIONED ETT ASSESSMENT SYSTEM (EFAI ETTXR) is a radiological computer-aided triage and notification software indicated for use in the analysis of chest X-ray (CXR) images in adults. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of vertically malpositioned endotracheal tube (ETT) in relation to the carina. Findings are flagged when the ETT distal tip is assessed as being more than 7 cm above the carina, less than 3 cm above the carina, or when it is below the carina (i.e in the right or left mainstem bronchus). The device assesses solely the vertical position of the ETT distal tip relative to the carina, does not factor patient positioning, and cannot detect esophageal intubation. The device is tested in the single lumen endotracheal tube, while it may trigger a false prioritization alert in the case of properly positioned double lumen ETT.

EFAI ETTXR analyzes cases using algorithms to identify suspected malpositioned ETT findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI ETTXR is not intended to direct attention to specific portions of an image or to anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out malpositioned ETT or otherwise preclude clinical assessment of chest radiographs.

EFAI CHESTSUITE XR MALPOSITIONED ETT ASSESSMENT SYSTEM (EFAI ETTXR) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze chest radiographs and alerts the PACS/RIS workstation once images with features suggestive of malpositioned ETT are identified.

Through the use of EFAI ETTXR, a radiologist is able to review studies with features suggestive of malpositioned ETT earlier than in standard of care workflow.

The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original chest radiographs. The device aims to aid in prioritization and triage of radiological medical images only.

Here's a breakdown of the acceptance criteria and the study details for the EFAI ETTXR device, based on the provided document:

Acceptance Criteria and Reported Device Performance

Study Details

1. Sample Size Used for the Test Set and Data Provenance:
* Sample Size: 940 studies (each patient included only one study).
* Data Provenance: Retrospective, consecutively collected from multiple clinical sites across the United States. None of the studies were used in model development or analytical validation.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:
* Number of Experts: Three.
* Qualifications: U.S. board-certified radiologists.

3. Adjudication Method for the Test Set:
* Method: Majority agreement among the three U.S. board-certified radiologists.
* Resulting Ground Truth: 259 positive cases for malpositioned ETT, 681 negative cases (316 correctly positioned ETTs, 365 with no ETT).

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:
* No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not explicitly described in this document. The study described is a standalone performance validation of the AI model.

5. If a Standalone (Algorithm Only) Performance Study Was Done:
* Yes, a standalone performance validation study was done. The document states: "The observed results of the standalone performance validation study demonstrated that EFAI ETTXR by itself, in the absence of any interaction with a clinician, can provide case-level notifications with features suggestive of malpositioned ETT with satisfactory results."

6. The Type of Ground Truth Used:
* Expert Consensus: The ground truth was established by the majority agreement of three U.S. board-certified radiologists.

7. The Sample Size for the Training Set:
* The document does not specify the exact sample size for the training set. It mentions that "None of the studies [in the test set] was used as part of the EFAI ETTXR model development or analytical validation testing," implying a separate training set was used, but its size is not 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 implies the use of "deep learning techniques" and a "database of images" for the algorithm. It's common in AI development studies for the training set ground truth to also be established by expert review, but this is not detailed for EFAI ETTXR's training data.

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