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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 finding: mass effect . The device analyzes studies using an artificial intelligence algorithm to identify the finding. 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 the suspected finding. 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 CTB studies

Annalise Enterprise CTB Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on non-contrast brain CT studies in the medical care environment. The findings identified by the device include mass effect. 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 and models. 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 neuroradiologists. The device interfaces with image and order management systems (such as PACS/RIS) to obtain noncontrast brain CT studies for processing by the AI algorithm. Following processing, if any of the radiological findings of interest are identified in a non-contrast brain CT 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 radiological 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 non-contrast brain CTs. The device is intended to aid in prioritization and triage of radiological medical images only.

Here is a summary of the acceptance criteria and study proving device performance, based on the provided text:

Device Name: Annalise Enterprise CTB Triage Trauma
Manufacturer: Annalise-AI Pty Ltd.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" for sensitivity and specificity in a separate table. Instead, it provides the results of the standalone performance study in a table, implying these values meet the unstated criteria for demonstrating safety and effectiveness. The comparison section further states that these results are "substantially equivalent to those of the predicate device."

Additional Performance Metric (Triage Effectiveness):

• Triage Turn-around Time: 81.6 seconds (95% CI: 80.3 - 82.9)

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

• Sample Size:
  • For slice thickness ≤1.5mm: 626 cases (493 positive, 133 negative for mass effect)
  • For slice thickness >1.5mm & ≤5.0mm: 762 cases (594 positive, 168 negative for mass effect)
• Data Provenance: Retrospective, anonymized study. Collected consecutively from five US hospital network sites.

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

• Number of Experts: At least two neuroradiologists for initial review, with a third neuroradiologist for adjudication in case of disagreement.
• Qualifications: All experts were US board-certified neuroradiologists, ABR-certified, and protocol-trained.

4. Adjudication Method for the Test Set

• Method: Consensus determined by two ground truthers. If there was a disagreement between the initial two ground truthers, a third ground truther was used to reach consensus (2+1 method).

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

• An MRMC study was not explicitly described as being performed for AI-assisted human reader performance improvement.
• The performance assessment focused on standalone performance of the AI algorithm and a triage effectiveness (turn-around time) study, which was an internal bench study. The document states the AI "enables users to review the studies containing features suggestive of these radiological findings earlier than in the standard clinical workflow," implying an effect on human workflow, but doesn't quantify reader improvement in an MRMC setting.

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

• Yes, a standalone performance evaluation was done. The sections under "Performance Testing" describe comparing the device's case-level output with a reference standard (ground truth). The results in the table above (Sensitivity, Specificity) are from this standalone performance evaluation.

7. The Type of Ground Truth Used

• Type: Expert consensus (from multiple board-certified neuroradiologists).

8. The Sample Size for the Training Set

• The document states, "The test dataset used during the standalone performance evaluation was newly acquired and independent from the training dataset used in model development."
• The sample size of the training set is not explicitly provided in the given text. It only mentions that "Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers and models."

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

• The document does not explicitly describe how the ground truth for the training set was established. It only details the ground truth establishment for the test set used in the standalone performance evaluation.

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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 finding:
· vasogenic edema
The device analyzes studies using an artificial intelligence algorithm to identify the finding. 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 the suspected finding.
Its results are not intended:

• · to be used on a standalone basis for clinical decision making
• · to rule out a specific finding, or otherwise preclude clinical assessment of CTB studies
  Intended modality:
  Annalise Enterprise identifies the suspected finding in non-contrast brain CT studies.
  Intended user:
  The device is intended to be used by trained clinicians who, as part of their scope of practice, are qualified to interpret brain CT studies.
  Intended patient population: The intended population is patients who are 22 years or older.

Annalise Enterprise CTB Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on non-contrast brain CT studies in the medical care environment. The findings identified by the device include vasogenic edema.
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 and models. 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 neuroradiologists.
The device interfaces with image and order management systems (such as PACS/RIS) to obtain noncontrast brain CT studies for processing by the AI algorithm. Following processing, if any of the radiological findings of interest are identified in a non-contrast brain CT 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 radiological 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 non-contrast brain CTs. The device is intended to aid in prioritization and triage of radiological medical images only.

Here is a detailed breakdown of the acceptance criteria and study that proves the device meets the acceptance criteria, based on the provided document.

1. Acceptance Criteria and Reported Device Performance

The core acceptance criteria for this device, a Radiological Computer-Aided Triage and Notification Software, are demonstrated through its standalone performance in identifying vasogenic edema. The performance is assessed using Sensitivity and Specificity at different operating points for two slice thickness ranges.

Table of Acceptance Criteria (Implicit) and Reported Device Performance for Vasogenic Edema Detection:

Beyond these specific metrics, an additional performance metric focused on Triage Effectiveness (Turn-around time) was assessed. The reported performance was:

• Triage Turn-around Time: 81.6 seconds (95% CI: 80.3 - 82.9)

The implication is that these reported values meet or exceed
the pre-defined acceptance criteria, thereby demonstrating "effective triage within a clinician's queue based on high sensitivity and specificity," and "substantially equivalent to those of the predicate device" for the standalone performance and turn-around time.

2. Sample Sizes and Data Provenance

• Test Set Sample Size:
  • For slice thickness ≤1.5mm: 167 cases (60 positive, 107 negative)
  • For slice thickness >1.5mm & ≤5.0mm: 293 cases (99 positive, 194 negative)
• Data Provenance: The test data was retrospective and anonymized, collected from five US hospital network sites.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: At least two neuroradiologists, with a third neuroradiologist for adjudication in case of disagreement.
• Qualifications of Experts: All truthers were US board-certified neuroradiologists and were protocol-trained.

4. Adjudication Method for the Test Set

The adjudication method used was 2+1. Each de-identified case was annotated in a blinded fashion by at least two (2) ABR-certified and protocol-trained neuroradiologists. Consensus was determined by these two initial ground truthers. A third (1) ground truther was used in the event of disagreement between the first two.

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

An MRMC comparative effectiveness study was not explicitly mentioned in the provided text for assessing human reader improvement with AI assistance. The study focused on the standalone performance of the AI algorithm and its triage effectiveness (measured by turn-around time) rather than how human readers' performance improved. The device's intended use is for triage and prioritization, not necessarily to assist in the primary diagnosis or interpretation task of the radiologist per se (e.g., drawing attention to specific portions of an image is explicitly stated as not intended).

6. Standalone Performance (Algorithm Only)

Yes, a standalone performance evaluation was done. The document explicitly states: "Standalone performance was assessed via a retrospective, anonymized study of adult patient, DICOMcompliant non-contrast brain CT cases." The metrics provided in the table (Sensitivity and Specificity) represent this standalone performance of the AI algorithm.

7. Type of Ground Truth Used

The ground truth used was expert consensus. It was determined by multiple ABR-certified neuroradiologists through a consensus and adjudication process.

8. Sample Size for the Training Set

The document states: "The test dataset used during the standalone performance evaluation was newly acquired and independent from the training dataset used in model development." However, the 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 states that the AI algorithm was "trained using deep-learning techniques" and that "Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers and models." However, no specific information is provided on how the ground truth for this training set was established.

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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 finding:

· obstructive hydrocephalus

The device analyzes studies using an artificial intelligence algorithm to identify the finding. 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 the suspected finding.

Its results are not intended:

• · to be used on a standalone basis for clinical decision making
• · to rule out a specific finding, or otherwise preclude clinical assessment of CTB studies

Intended modality: Annalise Enterprise identifies the suspected finding in non-contrast brain CT studies.

Intended user:

The device is intended to be used by trained clinicians who, as part of their scope of practice, are qualified to interpret brain CT studies.

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

Annalise Enterprise CTB Triage - OH is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on non-contrast brain CT studies in the medical care environment. The findings identified by the device include obstructive hydrocephalus.

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 Toshiba, GE Medical Systems, Siemens. Philips, and Canon Medical Systems. This dataset, which contained over 200.000 CT brain imaging studies, was annotated by qualified and trained radiologists.

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 neuroradiologists.

The device interfaces with image and order management systems (such as PACS/RIS) to obtain noncontrast brain CT studies for processing by the AI algorithm. Following processing, if any of the radiological findings of interest are identified in a non-contrast brain CT 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 radiological 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 non-contrast brain CTs. The device is intended to aid in prioritization and triage of radiological medical images only.

The provided text describes the acceptance criteria and the study that proves the device meets those criteria for the Annalise Enterprise CTB Triage - OH device.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the device's performance are implicitly defined by the reported sensitivity and specificity values in the pivotal standalone performance study. The device is intended to establish effective triage for "obstructive hydrocephalus." At various operating points, the device demonstrates high sensitivity and specificity.

In addition to the sensitivity and specificity, the device demonstrated a triage turn-around time of 81.6 (95% CI: 80.3 - 82.9) seconds, which was considered substantially equivalent to the predicate device.

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

The test set for the standalone performance evaluation was:

• Sample Size:
  • 175 cases for slice thickness ≤1.5mm (75 positive, 100 negative)
  • 191 cases for slice thickness >1.5mm & ≤5.0mm (84 positive, 107 negative)
• Data Provenance: Retrospective, anonymized cases collected consecutively from five US hospital network sites, including both community hospitals and academic medical centers. The dataset was newly acquired and independent from the training dataset.

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

• Number of Experts: At least two neuroradiologists, with a third neuroradiologist used in the event of disagreement.
• Qualifications: US board-certified neuroradiologists, ABR-certified and protocol-trained (for the ground truth determination process).

4. Adjudication Method for the Test Set

The adjudication method used was 2+1 consensus. Ground truth was determined by two ground truthers, and a third ground truther was used in the event of disagreement.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its Effect Size

No MRMC study comparing human readers with AI vs. without AI assistance was explicitly mentioned. The study focused on the standalone performance of the algorithm and its triage effectiveness (turn-around time) as an aid to prioritize worklists. The statement "[The device] enables users to review the studies containing features suggestive of these radiological findings earlier than in the standard clinical workflow" implies an improvement in workflow, but a direct MRMC study quantifying effect size on human reader performance was not provided in this document.

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

Yes, a standalone performance evaluation was conducted. The performance data presented in the table above (sensitivity and specificity) are results from this standalone evaluation.

7. The Type of Ground Truth Used

The ground truth used was expert consensus, specifically from US board-certified neuroradiologists using a 2+1 adjudication method.

8. The Sample Size for the Training Set

The training dataset contained over 200,000 CT brain imaging studies.

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

The images used to train the algorithm were sourced from datasets that were annotated by qualified and trained radiologists.

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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:

• · acute subdural/epidural hematoma*
  · acute subarachnoid hemorrhage *

· intra-axial hemorrhage*

· intraventricular hemorrhage*

*These findings are intended to be used together as one device.

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 CTB studies

Intended modality:

Annalise Enterprise identifies suspected findings in non-contrast brain CT studies.

Intended user:

The device is intended to be used by trained clinicians who, as part of their scope of practice, are qualified to interpret brain CT studies.

Intended patient population:

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

Annalise Enterprise CTB Triage Trauma is a software workflow tool which uses an artificial intelligence (Al) algorithm to identify suspected findings on non-contrast brain CT studies in the medical care environment. The findings identified by the device include acute subdural/ epidural hematoma, acute subarachnoid hemorrhage, intra-axial hemorrhage, and intraventricular hemorrhage.

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 Toshiba, GE Medical Systems, Siemens, Philips, and Canon Medical Systems. This dataset, which contained over 200,000 CT brain imaging studies, was labelled by trained radiologists regarding the presence of the four 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 neuroradiologists.

The device interfaces with image and order management systems (such as PACS/RIS) to obtain noncontrast brain CT studies for processing by the AI algorithm. Following processing, if any of the clinical findings of interest are identified in a non-contrast brain CT 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 non-contrast brain CTs. The device is intended to aid in prioritization and triage of radiological medical images only.

Here is a summary of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

The results for sensitivity and specificity above were presented across different operating points and slice thickness ranges, demonstrating the device's performance for each specific finding. The submission states that these results demonstrate the device establishes effective triage based on high sensitivity and specificity and are "substantially equivalent to those of the predicate device."

Specific acceptance criteria are not explicitly defined as pass/fail thresholds in the provided text. Instead, the reported performance metrics (sensitivity, specificity, and turn-around time) are presented as proof of meeting the requirements for 'Radiological computer aided triage and notification software' and supporting substantial equivalence.

Study Information

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

   • Standalone Performance Evaluation (retrospective):
     • Total cases: 1,485 cases for slice thickness ≤1.5mm (1,003 positive, 482 negative) and 1,878 cases for slice thickness >1.5mm (1,257 positive, 621 negative).
     • Provenance: Collected consecutively from five US hospital network sites. The test dataset was newly acquired and independent from the training dataset.
   • Triage Effectiveness Study (internal bench study):
     • Total cases: 277 cases positive for any of the findings eligible for prioritization.
     • Provenance: Collected from multiple data sources spanning a variety of geographical locations, patient demographics, and technical characteristics.

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

   • Number of Experts: At least two neuroradiologists, with a third used in case of disagreement.
   • Qualifications: US board-certified neuroradiologists, ABR-certified, and protocol-trained.

3. Adjudication method for the test set:

   • Consensus determined by two ground truthers, and a third ground truther in the event of disagreement (2+1 adjudication). The cases were annotated in a blinded fashion.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • A multi-reader multi-case (MRMC) comparative effectiveness study was not specifically described in the provided text. The performance assessment focused on standalone performance and triage effectiveness (turn-around time).

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

   • Yes, a standalone performance evaluation was done. The case-level output from the device's AI algorithm was compared directly with the reference standard (ground truth).

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

   • Expert Consensus: The ground truth was determined by the consensus of multiple ABR-certified and protocol-trained neuroradiologists.

7. The sample size for the training set:

   • "Over 200,000 CT brain imaging studies."

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

   • The training dataset, containing over 200,000 CT brain imaging studies, "was labelled by trained radiologists regarding the presence of the four findings of interest."

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Annalise Enterprise CXR Triage Trauma is a software workflow tool designed to aid the clinical assessment of adult chest X-ray cases with features suggestive of pleural effusion and pneumoperitoneum in the medical care environment.

The device analyzes cases using an artificial intelligence algorithm to identify findings. It makes case-level output available to a PACS or RIS for worklist prioritization or triage.

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

The device is not intended to direct attention to specific portions of an image or to anomalies other than pleural effusion and pneumoperitoneum.

Its results are not intended to be used on a standalone basis for clinical decision making nor is it intended to rule out specific critical findings, or otherwise preclude clinical assessment of X-ray cases.

Standalone performance evaluation of the device was performed on a dataset that included supine and erect positioning. Use of this device with prone positioning may result in differences in performance.

Standalone performance evaluation of the device was performed on a dataset where most cases were of unilateral right sided and bilateral pneumoperitoneum. Use of this device for unilateral left sided pneumoperitoneum may result in differences in performance.

Specificity may be reduced for pleural effusion in the presence of scarring and and/or pleural thickening.

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

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 across three continents, including a range of equipment manufacturers and models. 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 CXR studies for processing by the AI algorithm. Following processing, if any of the clinical findings of interest are identified in a CXR 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 CXRs. The device is intended to aid in prioritization and triage of radiological medical images only.

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

Acceptance Criteria and Device Performance

The document describes the performance of the Annalise Enterprise CXR Triage Trauma device in identifying pleural effusion and pneumoperitoneum. The acceptance criteria are implicitly demonstrated by the reported sensitivities and specificities at various operating points, which are presented as evidence of effective triage and substantial equivalence to the predicate device. While explicit "acceptance criteria" are not listed as pass/fail thresholds in the provided text, the performance metrics below are what the sponsor used to demonstrate the device met the requirements.

Table of Reported Device Performance:

Additionally, the device achieved an average triage turn-around time under 30.0 seconds. This implies an implicit acceptance criterion for speed, aiming for a rapid triage capability.

Study Details

1. Sample Sizes and Data Provenance

• Test Set Sample Size: A total of 1269 CXR cases (582 positive, 687 negative) were used across two independent cohorts.
• Data Provenance: The data was collected retrospectively and anonymized from four US hospital network sites. The cases were collected consecutively. The text also mentions that training data was sourced from "datasets across three continents."

2. Number of Experts and Qualifications for Ground Truth

• Number of Experts: For ground truth determination, a minimum of two experts were used, with a third expert used in case of disagreement.
• Qualifications: All experts (ground truthers) were ABR-certified and protocol-trained radiologists. No specific years of experience are mentioned.

3. Adjudication Method for the Test Set

• The adjudication method for establishing ground truth was 2+1 consensus. This means two ground truthers blinded to each other's assessments first reviewed the cases, and a third ground truther was consulted to resolve any disagreements.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned as being performed to show how human readers improve with AI vs. without AI assistance. The performance evaluation focused on the standalone performance of the AI algorithm for triage effectiveness.

5. Standalone Performance (Algorithm Only)

• Yes, a standalone performance evaluation was performed. The reported sensitivities, specificities, and AUC values in the table above demonstrate the algorithm's performance without integration into human workflow for diagnostic purposes, although it is evaluated for its "triage effectiveness."

6. Type of Ground Truth Used

• The ground truth was established by expert consensus of ABR-certified and protocol-trained radiologists.

7. Sample Size for the Training Set

• The specific sample size for the training set is not provided in the given text. It only states that the training dataset was "independent from the training dataset used in model development" relative to the test set and sourced from "datasets across three continents."

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

• The text does not explicitly detail how the ground truth for the training set was established. It only states that the AI algorithm was "trained using deep-learning techniques" and that the "Images used to train the algorithm were sourced from datasets across three continents." It can be inferred that a similar expert review process, likely by radiologists, would have been used to label the training data, but this is not explicitly stated in the provided excerpt.

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Annalise Enterprise CXR Triage Trauma is a software workflow tool designed to aid the clinical assessment of adult chest X-ray cases with features suggestive of vertebral compression fracture in the medical care environment.

The device analyzes cases using an artificial intelligence algorithm to identify findings. It makes case-level output available to a PACS or RIS for worklist prioritization or triage intended for clinicians in Bone Health and Fracture Liaison Service programs.

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

The device is not intended to direct attention to specific portions of an image or to anomalies other than vertebral compression fracture.

Its results are not intended to be used on a standalone basis for clinical decision making nor is it intended to rule out specific critical findings, or otherwise preclude clinical assessment of X-ray cases.

Standalone performance evaluation of the device 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 CXR Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on chest X-ray (CXR) studies in the medical care environment. The findings identified by the device include vertebral compression fractures.

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 across three continents, including a range of equipment manufacturers and models. 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 CXR studies for processing by the AI algorithm. Following processing, if the clinical finding of interest is identified in a CXR 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 CXRs. The device is intended to aid in prioritization and triage of radiological medical images only.

The provided text describes the Annalise Enterprise CXR Triage Trauma device, an AI-powered software tool designed to aid in the clinical assessment and triage of adult chest X-ray cases for vertebral compression fracture. Here's a breakdown of its acceptance criteria and the study proving its performance:

Acceptance Criteria and Reported Device Performance

Study Details

1. Sample Size and Data Provenance:

   • Test Set Sample Size: 589 CXR cases (272 positive for vertebral compression fracture, 317 negative).
   • Data Provenance: Retrospective, anonymized study. Collected consecutively from four U.S. hospital network sites. The cases were collected from multiple data sources spanning a variety of geographical locations.

2. Number of Experts and Qualifications for Ground Truth:

   • Number of Experts: At least two ABR-certified radiologists for initial annotation, with a third radiologist for disagreement resolution.
   • Qualifications: All truthers were U.S. board-certified radiologists who were protocol-trained.

3. Adjudication Method for Test Set:

   • Consensus was determined by two ground truthers. A third ground truther was used in the event of disagreement. This is commonly referred to as a 2+1 adjudication method.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No MRMC comparative effectiveness study was explicitly mentioned or detailed in the provided text regarding how human readers improve with AI vs without AI assistance. The study described is primarily a standalone performance evaluation of the AI algorithm. The device's role is described as a "workflow tool" for worklist prioritization or triage, implying indirect assistance, rather than direct human-AI collaborative interpretation.

5. Standalone Performance:

   • Yes, a standalone (algorithm only without human-in-the-loop) performance evaluation was done. The performance results (AUC, Sensitivity, Specificity) listed in the table above pertain to this standalone evaluation.

6. Type of Ground Truth Used:

   • Expert consensus (blinded annotations by ABR-certified radiologists with adjudication).

7. Training Set Sample Size:

   • The exact sample size for the training set is not explicitly stated. However, it is mentioned that "Images used to train the algorithm were sourced from datasets across three continents, including a range of equipment manufacturers and models." The test dataset was "newly acquired and independent from the training dataset used in model development."

8. How Ground Truth for Training Set Was Established:

   • The document states that the AI algorithm was a "convolutional neural network trained using deep-learning techniques." While it mentions the source of the training data (datasets across three continents), it does not explicitly detail the method for establishing ground truth for the training set (e.g., expert review, pathology, or other means). The focus of the provided text is on the validation of the test set performance.

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