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BriefCase-Triage is a radiological computer aided triage and notification software indicated for use in the analysis of contrast-enhanced CT images that include the brain, 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 cases of Brain Aneurysm (BA) findings that are 3.0 mm or larger.

BriefCase-Triage uses an artificial intelligence algorithm to analyze images and flag suspect cases in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for suspect cases. 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 professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

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.

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

Acceptance Criteria and Reported Device Performance

Note on Additional Operating Points (AOPs): The device also met performance goals (80% sensitivity and specificity) for three additional operating points (AOP1, AOP2, AOP3) with slightly varying sensitivity/specificity trade-offs (e.g., AOP3: Sensitivity 86.2%, Specificity 93.6%).

Study Details

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

• Sample Size: 544 cases
• Data Provenance: Retrospective, blinded, multicenter study from 6 US-based clinical sites. The cases were distinct in time or center from those used for algorithm training.

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

• Number of Experts: Three (3) senior board-certified radiologists.
• Qualifications: "Senior board-certified radiologists." (Specific number of years of experience not detailed in the provided text).

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

• The text states the ground truth was "determined by three senior board-certified radiologists." It doesn't explicitly describe an adjudication method like "2+1" or "3+1." This implies a consensus approach where all three radiologists agreed, or a majority rule, but the exact mechanism for resolving discrepancies (if any) is not specified.

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:

• No, an MRMC comparative effectiveness study was NOT done. The study's primary objective was to evaluate the standalone performance of the BriefCase-Triage software. The secondary endpoint compared the device's time-to-notification to that of the predicate device, but not its impact on human reader performance.

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

• Yes, a standalone performance study was done. The primary endpoints (sensitivity and specificity) measure the algorithm's performance in identifying Brain Aneurysm (BA) findings.

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

• Expert Consensus: The ground truth was "determined by three senior board-certified radiologists."

7. The sample size for the training set:

• Not explicitly stated. The document mentions the algorithm was "trained during software development on images of the pathology" and that "critical findings were tagged in all CTs in the training data set." However, the specific sample size for this training data is not provided.

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

• Manually labeled ("tagged") images: The text states, "As is customary in the field of machine learning, deep learning algorithm development consisted of training on manually labeled ('tagged') images. In that process, critical findings were tagged in all CTs in the training data set." It does not specify who performed the tagging or their qualifications, nor the method of consensus if multiple taggers were involved.

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BriefCase-Triage: CARE (Clinical AI Reasoning Engine) Multi-Triage CT Body is a radiological computer aided triage and notification software indicated for use in the analysis of contrast and non-contrast CT images of the chest, abdomen, and/or pelvis, 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 communicating suspected positive findings, per study, of:

1. Diverticulitis;
2. Abdominal-pelvic abscess;
3. Appendicitis;
4. Intestinal ischemia and/or pneumatosis;
5. Obstructive renal stone;
6. Small bowel obstruction;
7. Large bowel obstruction;
8. Spleen injury;
9. Liver injury;
10. Kidney injury;
11. Pelvic fracture.

The device flags cases with at least one suspected finding to assist with triage/prioritization of medical images. The device will provide a flag for each suspected finding within this study. A preview image will be provided for each distinct suspected finding.

BriefCase-Triage uses a foundation model-based artificial intelligence (AI) system to analyze images and highlight cases with detected findings 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 for each suspected finding that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical images 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 of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

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 images that match meta-data criteria according to BriefCase-Triage: CARE Multi-Triage CT Body's predefined set of parameters. Then, the BriefCase-Triage processes the series chronologically, identifying cases with suspected positive finding(s) and selecting key slice(s) for preview. BriefCase-Triage output consists of suspected positive flag/notification regarding the existence of each finding in the analyzed study. Each finding includes a Representative Key Slice. The Key Slice(s) may be presented to the users as compressed, low-quality, grayscale, preview images with the date and time imprinted. The previews are not annotated and are captioned with the disclaimer "Not for diagnostic use, for prioritization only" according to the device requirement from the Image Communication Platform (ICP).

Acceptance Criteria and Study Details for BriefCase-Triage: CARE Multi-triage CT Body

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the BriefCase-Triage: CARE Multi-triage CT Body device were primarily defined by performance goals for Area Under the Curve (AUC), Sensitivity (Se), and Specificity (Sp). The study demonstrated that the device met and exceeded these criteria for all 11 indications.

Note: The reported device performance for AUC, Sensitivity, and Specificity are ranges covering the minimum and maximum values observed across the 11 indications in the pivotal study. Detailed values for each indication are provided in the source text.

2. Sample Size and Data Provenance for the Test Set

• Sample Size: N = 280 for each of the 11 clinical indications, resulting in 1769 unique scans included across all device indications.
• Data Provenance: The data was collected from 6 US-based clinical sites. It was retrospective and the cases were distinct in time or center from the cases used to train the algorithm.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Three senior board-certified radiologists.
• Qualifications: The document specifically states "senior board-certified radiologists." No further details on years of experience were provided.

4. Adjudication Method for the Test Set

The adjudication method used to establish ground truth was based on the "consensus" of the three senior board-certified radiologists ("as determined by three senior board-certified radiologists"). This implies a consensus-based adjudication, but the specific mechanics (e.g., majority vote like 2+1, or requiring all three to agree) are not explicitly detailed.

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

No MRMC study comparing human readers with AI assistance versus without AI assistance was reported in this document. The study described is a standalone performance analysis of the algorithm.

6. Standalone Performance Study

Yes, a standalone performance study was done. The document states: "Aidoc conducted a retrospective, blinded, multicenter study with the Briefcase-Triage software to evaluate the standalone performance analysis individually for each of the 11 clinical indications supported by BriefCase-Triage: CARE Multi-triage CT Body device."

7. Type of Ground Truth Used

The ground truth was established by expert consensus of three senior board-certified radiologists.

8. Sample Size for the Training Set

The sample size for the training set is not explicitly provided in the given text. It is only mentioned that "the algorithm was trained during software development on images of the pathology."

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

The ground truth for the training set was established through labeled ("tagged") images. The document states: "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." The specific method or expert involvement in this tagging process is not detailed, but it implies human expert labeling.

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BriefCase-Triage is a radiological computer aided triage and notification software indicated for use in the analysis of abdominal CT images 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 Intra-abdominal free gas (IFG) pathologies.

BriefCase-Triage uses an artificial intelligence algorithm to analyze images and highlight cases with the detected findings 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 of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

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.

The algorithm was trained during software development on images of the pathology. 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.

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 BriefCase-Triage:

Acceptance Criteria and Reported Device Performance

Note: The document explicitly states that the primary endpoints were "sensitivity and specificity with an 80% performance goal." The reported performance for both sensitivity and specificity (94.2% and 94.6% respectively) significantly exceeds this 80% goal. The time-to-notification for the subject device is significantly faster than the predicate, demonstrating improved "time savings to the standard of care review."

Study Information

1. Sample Size Used for the Test Set and Data Provenance:
* Sample Size: 394 cases
* Data Provenance:
* Country of Origin: US (6 clinical sites)
* Retrospective/Prospective: Retrospective
* Additional Detail: Cases were distinct in time or center from the training data.

2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
* Number of Experts: 3
* Qualifications: Senior board-certified radiologists

3. Adjudication Method for the Test Set:
* The document states "as determined by three senior board-certified radiologists." While it doesn't explicitly state "2+1" or "3+1," this implies a consensus-based approach among the three experts. Without further detail, it's reasonable to infer a consensus was reached, or a specific rule for disagreement (e.g., majority) was applied.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
* No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted to assess how much human readers improve with AI vs. without AI assistance. The study focuses purely on the standalone performance of the AI algorithm.

5. Standalone Performance Study (Algorithm Only):
* Yes, a standalone study was performed. The "Pivotal Study Summary" describes evaluating "the software's performance to the ground truth," indicating a standalone performance assessment of the algorithm without human-in-the-loop performance measurement.

6. Type of Ground Truth Used:
* Expert consensus (as determined by three senior board-certified radiologists).

7. 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 provide a specific sample size for the training set.

8. How the Ground Truth for the Training Set Was Established:
* "each image in the training dataset was tagged based on the presence of the critical finding." This indicates that human experts (or a similar method to the test set ground truth) labeled the images in the training set for the presence of the pathology. However, the specific number and qualifications of these experts are not explicitly stated for the training 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

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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BriefCase-Triage is a radiological computer aided triage and notification software indicated for use in the analysis of contrast-enhanced images that include the lungs in adults or transitional adolescents age 18 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of suspect cases of incidental Pulmonary Embolism (iPE) pathologies.

BriefCase-Triage uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for suspect cases. 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 of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

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 breakdown of the acceptance criteria and the study details for the BriefCase-Triage device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Note on Additional Operating Points (AOPs): The document also describes four additional operating points (AOPs 1-4) designed to maximize specificity while maintaining a lower bound 95% CI of 80% for sensitivity. These AOPs achieved similar performance metrics, further demonstrating the device's flexibility.

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

• Sample Size: 498 cases
• Data Provenance: Retrospective, blinded, multicenter study. Cases were collected from 6 US-based clinical sites. The test data was distinct in time or center from the cases used for algorithm training and was sequestered from algorithm development activities.

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

• Number of Experts: Three (3)
• Qualifications of Experts: Senior board-certified radiologists.

4. Adjudication Method for the Test Set

The specific adjudication method (e.g., 2+1, 3+1) is not explicitly stated in the provided text. It only mentions that the ground truth was "determined by three senior board-certified radiologists."

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing AI assistance versus without AI assistance for human readers was not described for this device. The study primarily evaluated the standalone performance of the AI algorithm and its time-to-notification compared to the predicate device, not the improvement in human reader performance with AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-loop Performance) was done

Yes, a standalone performance study was done. The "Pivotal Study Summary" sections describe the device's sensitivity, specificity, and other metrics based on its algorithm's performance in identifying iPE cases compared to the ground truth established by radiologists. The device is intended to provide notifications and not for diagnostic use, suggesting its performance is evaluated in its autonomous triage function.

7. The Type of Ground Truth Used

The ground truth used was expert consensus, specifically "determined by three senior board-certified radiologists."

8. The Sample Size for the Training Set

The sample size for the training set is not explicitly stated in the provided text. It only mentions that the algorithm was "trained during software development on images of the pathology."

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

The ground truth for the training set was established through manual labeling ("tagging") by experts. The text states: "deep learning algorithm development consisted of training on manually labeled ("tagged") images. In that process, critical findings were tagged in all CTs in the training data set."

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BriefCase-Triage is a radiological computer aided triage and notification software indicated for use in the analysis of CT images with or without contrast that include the ribs, 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 communicating suspect cases of three or more acute Rib fracture (RibFx) pathologies.

BriefCase-Triage uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected RibFx 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 of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

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 linuxbased 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 Al 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.

Acceptance Criteria and Device Performance for BriefCase-Triage

1. Table of Acceptance Criteria and Reported Device Performance

Note: The acceptance criteria for sensitivity and specificity are extrapolated from the statement "As the AUC exceeded 0.95 and sensitivity and specificity both exceeded 80%, the study's primary endpoints were met."

2. Sample Size and Data Provenance for Test Set

• Sample Size for Test Set: 308 cases
• Data Provenance: Retrospective, multicenter study from 5 US-based clinical sites. The cases collected for the pivotal dataset were distinct in time or center from the cases used to train the algorithm.

3. Number and Qualifications of Experts for Ground Truth

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

4. Adjudication Method for Test Set

The adjudication method is not explicitly stated. The document mentions "ground truth, as determined by three senior board-certified radiologists," but does not detail how disagreements among these radiologists were resolved (e.g., 2+1, 3+1, or simple consensus).

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

No MRMC comparative effectiveness study was done to assess the effect of AI assistance on human readers' performance. The study focused on the standalone performance of the AI algorithm and its time-to-notification compared to a predicate device.

6. Standalone Performance Study

Yes, a standalone performance study was done. The "Pivotal Study Summary" section explicitly details the evaluation of the software's performance (AUC, sensitivity, specificity, PPV, NPV, PLR, NLR) in identifying RibFx without human intervention, comparing it to the established ground truth.

7. Type of Ground Truth Used

The ground truth used was expert consensus, determined by three senior board-certified radiologists.

8. Sample Size for Training Set

The sample size for the training set is not explicitly provided in the document. The document states, "The algorithm was trained during software development on images of the pathology. As is customary in the field of machine learning, deep learning algorithm development consisted of training on labeled ("tagged") images." It also mentions, "The cases collected for the pivotal dataset were all distinct in time or center from the cases used to train the algorithm, as was used for the most recent clearance (K230020)."

9. How Ground Truth for Training Set Was Established

The ground truth for the training set was established by labeling ("tagging") images based on the presence of the critical finding (three or more acute Rib fractures). This process is described as "each image in the training dataset was tagged based on the presence of the critical finding." The document does not specify who performed this tagging or the exact methodology for establishing the ground truth for the training set (e.g., expert consensus, pathology).

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BriefCase-Quantification is a radiological image management and processing system software indicated for use in the analysis of contrast-enhanced CT exams that include the aorta in adults or transitional adolescents aged 18 and older.

BriefCase-Quantification of Aortic Measurement (M-Aorta) is intended to assist hospital networks and appropriately trained medical specialists by providing the user with aortic diameter measurements across the aorta. BriefCase-Quantification is indicated to evaluate normal and aneurysmal aortas and is not intended to evaluate post-operative aortas.

The device provides the following assessments:

• Aortic measurements at 10 anatomical landmarks;
• Maximum aortic diameter of the abdominal aorta, descending aorta and ascending aorta.

The BriefCase-Quantification results are not intended to be used on a stand-alone basis for clinical decision-making or otherwise preclude clinical assessment of cases. These measurements are unofficial, are not final, and are subject to change after review by a radiologist. For final clinically approved measurements, please refer to the official radiology report. Clinicians are responsible for viewing full images per the standard of care.

BriefCase-Quantification is a radiological medical image management and processing device. The software consists of a single module based on an algorithm programmed component and is intended to run on a linux-based server in a cloud environment.

The BriefCase-Quantification receives filtered DICOM Images, and processes them chronologically by running the algorithm on relevant series to measure the maximum abdominal aortic diameter. Following the AI processing, the output of the algorithm analysis is transferred to an image review software (desktop application), and forwarded to user review in the PACS.

The BriefCase-Quantification produces images and tabular views of the landmarks and segments selected per installation to be displayed in the image review software, provided by the Image Communication Platform integrated with Briefcase-Quantification. The diameter marking is not intended to be a final output, but serves the purpose of visualization and measurement. The original, unmarked series remains available in the PACS as well. The preview image presents an unofficial and not final measurement, and the user is instructed to review the full image and any other clinical information before making a clinical decision. The image includes a disclaimer: "Not for diagnostic use. The measurement is unofficial, not final, and must be reviewed by a radiologist."

BriefCase-Quantification is not intended to evaluate post-operative aortas.

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

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance

• Test Set Sample Size: 212 cases
• Data Provenance: Retrospective, multicenter study. Cases were collected from 6 US-based clinical sites, including both academic and community centers. The cases were distinct in time and/or center from those used to train the algorithm.

3. Number of Experts and Qualifications for Ground Truth

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

4. Adjudication Method for the Test Set

The document states the ground truth was "determined by three US board-certified radiologists." While it doesn't explicitly detail a 2+1 or 3+1 method, the implication of "determined by" three experts suggests a consensus-based approach, likely one where all three agreed or a majority agreement was required for the final ground truth. It does not mention any specific adjudication process explicitly (e.g., if disagreements were resolved by a fourth reader).

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

• Was an MRMC study done? No. The study described is a standalone (algorithm-only) performance evaluation against expert-established ground truth. It does not compare human readers with and without AI assistance.
• Effect size of improvement: Not applicable, as no MRMC study was performed.

6. Standalone (Algorithm-Only) Performance

• Was a standalone performance study done? Yes. The "Pivotal Study Summary" describes the evaluation of "the software's performance ... compared to the ground truth," focusing on the algorithm's accuracy in measurement against expert consensus.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. Specifically, the ground truth was "determined by three US board-certified radiologists."

8. Sample Size for the Training Set

The document explicitly states: "The cases collected for the pivotal dataset were all distinct in time and/or center from the cases used to train the algorithm." However, the exact sample size for the training set is not provided in the given text.

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. It only mentions that the training cases were distinct from the test set cases.

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BriefCase-Triage is a radiological computer aided triage and notification software indicated for use in the analysis of CT scans that include the cervical spine, 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 linear lucencies in the cervical spine bone in patterns compatible with fractures.

BriefCase-Triage uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings 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 of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

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 linuxbased 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 Al 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.

The algorithm was trained during software development on images of the pathology. As is customary in the field of machine learning algorithm development consisted of training on manually labeled ("tagged") images. In that process, critical findings were tagged in all CTs in the training data set.

The Aidoc BriefCase-Triage device, intended for triaging cervical spine CT scans for fractures, underwent a retrospective, blinded, multicenter study to evaluate its performance.

Here's a breakdown of the acceptance criteria and study details:

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

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

• Sample Size: 487 cases
• Data Provenance: Retrospective, multicenter study from 6 US-based clinical sites. The cases collected for the pivotal dataset were all distinct in time or center from the cases used to train the algorithm.

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

• Number of Experts: Three
• Qualifications: Senior board-certified radiologists

4. Adjudication method for the test set

The document does not explicitly state the adjudication method (e.g., 2+1, 3+1). However, it implies that the ground truth was "as determined by three senior board-certified radiologists," suggesting a consensus-based approach among these experts.

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

There is no MRMC comparative effectiveness study presented in this document that evaluates human readers' improvement with AI assistance versus without AI assistance. The study focuses on the standalone performance of the AI device and its time-to-notification compared to a predicate device.

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

Yes, a standalone algorithm-only performance study was done. The primary endpoints (sensitivity and specificity) and secondary endpoints (time-to-notification, PPV, NPV, PLR, NLR) directly measure the performance of the BriefCase-Triage software itself.

7. The type of ground truth used

The ground truth was established by expert consensus (determined by three senior board-certified radiologists).

8. The 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 test pivotal study data was sequestered from algorithm development activities.

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

The ground truth for the training set was established by manually labeled ("tagged") images. The document states, "critical findings were tagged in all CTs in the training data set." This implies expert annotation of the training data.

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Briefcase-Triage is a radiological computer- aided triage and notification software indicated for use in the analysis of CTPA images, 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 communicating suspected positive cases of Pulmonary Embolism (PE) pathologies.

Briefcase-Triage uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected PE 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 of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

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 Al 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 breakdown of the acceptance criteria and study details for the Aidoc BriefCase-Triage device, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

Device Name: BriefCase-Triage (for Pulmonary Embolism - PE)

Additional Operating Points (AOPs) Performance:

Other reported secondary endpoints for the default operating point:

• NPV: 98.96% (95% CI: 98.21%- 99.4%)
• PPV: 74.79% (95% CI: 64.80%- 82.7%)
• PLR: 16.81 (95% CI: 10.43- 27.09)
• NLR: 0.059 (95% CI: 0.034- 0.103)

2. Sample Size and Data Provenance for the Test Set

• Sample Size: 499 cases
• Data Provenance: Retrospective, multicenter study from 6 US-based clinical sites. The cases were distinct in time or center from the cases used to train the algorithm.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Three (3)
• Qualifications: Senior board-certified radiologists.

4. Adjudication Method

• The document states "the ground truth as determined by three senior board-certified radiologists." It does not explicitly state the adjudication method (e.g., 2+1, consensus, majority vote). However, "determined by" implies that their collective judgment established the ground truth for each case.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described. The study focuses on the standalone performance of the AI algorithm against a ground truth established by experts, and a comparison of its notification time with a predicate device. It does not evaluate human reader performance with and without AI assistance.

6. Standalone Performance Study

• Yes, a standalone study (algorithm only without human-in-the-loop performance) was conducted. The primary endpoints (sensitivity and specificity) evaluated the device's ability to identify PE cases independently.

7. Type of Ground Truth Used

• Expert Consensus: The ground truth was "determined by three senior board-certified radiologists"

8. Sample Size for the Training Set

• The document states that the algorithm was "trained during software development on images of the pathology" and that the subject device was trained on a "larger data set" compared to the predicate. However, it does not specify the exact sample size of the training set.

9. How Ground Truth for the Training Set was Established

• "critical findings were tagged in all CTs in the training data set." This implies manual labeling (annotation) of findings by experts on the training images. While not explicitly stated, it is common practice that these tags are done by medical professionals.

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BriefCase-Quantification is a radiological image management and processing system software indicated for use in the analysis of CT exams with contrast, that include the abdominal aorta, in adults or transitional adolescents aged 18 and older.

The device is intended to assist appropriately trained medical specialists by providing the user with the maximum abdominal aortic diameter measurement of cases that include the abdominal aorta (M-AbdAo). BriefCase-Quantification is indicated to evaluate normal and aneurysmal abdominal aortas and is not intended to evaluate post-operative aortas.

The BriefCase-Quantification results are not intended to be used on a stand-alone basis for clinical decision-making or otherwise preclude clinical assessment of cases. These measurements are unofficial, are not final, and are subject to change after review by a radiologist. For final clinically approved measurements, please refer to the official radiology report. Clinicians are responsible for viewing full images per the standard of care.

BriefCase-Quantification is a radiological medical image management and processing device. The software consists of a single module based on an algorithm programmed component and is intended to run on a linux-based server in a cloud environment.

The BriefCase-Quantification receives filtered DICOM Images, and processes them chronologically by running the algorithm on relevant series to measure the maximum abdominal aortic diameter. Following the AI processing, the output of the algorithm analysis is transferred to an image review software (desktop application), and forwarded to user review in the PACS.

BriefCase-Quantification produces a preview image annotated with the maximum diameter measurement. The diameter marking is not intended to be a final output, but serves the purpose of visualization and measurement. The original, unmarked series remains available in the PACS as well. The preview image presents an unofficial measurement which is not final, and the user is instructed to review the full image and any other clinical information before making a clinical decision. The image includes a disclaimer: "Not for diagnostic use. The measurement is unofficial, not final, and must be reviewed by a radiologist."

BriefCase-Quantification is not intended to evaluate post-operative aortas.

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

Acceptance Criteria and Device Performance

Study Details

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

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

• Sample Size (Test Set): 162 cases
• Data Provenance: Retrospective, from 6 US-based clinical sites (both academic and community centers). The cases were distinct in time and/or center 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: Three (3)
• Qualifications: US board-certified radiologists

4. Adjudication method for the test set:
The text states the ground truth was "as determined by three US board-certified radiologists." It doesn't explicitly specify an adjudication method like 2+1 or 3+1, but implies a consensus or agreement among the three experts formed the ground truth.

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, a multi-reader multi-case (MRMC) comparative effectiveness study of human readers with and without AI assistance was not conducted. The study focused on the standalone performance of the algorithm against a human-established ground truth.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Yes, a standalone study of the algorithm's performance was done. The study evaluated the BriefCase-Quantification software's performance in providing maximum diameter measurements compared to a ground truth established by radiologists.

7. The type of ground truth used:
Expert consensus among three US board-certified radiologists.

8. The sample size for the training set:
The sample size for the training set is not explicitly stated. The text only mentions that the subject device's improved performance is "due to its training on a larger data set" compared to the predicate device, but does not provide a specific number for this larger data set. It also states the test set cases were "distinct in time and/or center from the cases used to train the algorithm."

9. How the ground truth for the training set was established:
The text does not explicitly describe how the ground truth for the training set was established. It only refers to a "larger data set" used for training.

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