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CINA-CSpine is a radiological computer aided triage and notification software indicated for use in the analysis of cervical spine CT images.

The device is intended to assist hospital networks and appropriately trained physician specialists by flagging and communication of suspected positive findings compatible with acute cervical spine fractures including non-displaced fracture lines and/or displaced fracture fragments.

CINA-CSpine uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings on a standalone application in parallel to the ongoing standard of care image interpretation. The device is not designed to detect vertebral compression fractures.

The user is presented with notifications for cases with suspected findings. Notifications include compressed preview images that are meant for informational purposes only, and are not intended for diagnostic use beyond notification. The device does not alter the original medical image, and it is not intended to be used as a diagnostic device.

The results of CINA-CSpine are intended to be used in conjunction with other patient information and based on professional judgment to assist with triage/prioritization of medical images.

Notified clinicians are ultimately responsible for reviewing full images per the standard of care.

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

CINA-CSpine runs on a standard "off the shelf" server/workstation and consists of CSpine Image Processing Application, which can be integrated, deployed and used with the CINA Platform (cleared under K200855) or other medical image communications devices. CINA-CSpine receives cervical spine CT scans identified by the CINA Platform or other medical image communications device, processes them using deep learning algorithms involving execution of multiple computational steps to identify the suspected positive findings compatible with acute cervical spine fractures and generates results files to be transferred by CINA Platform or a similar medical image communications device for output to a PACS system or workstation for worklist prioritization.

To identify the suspected presence of cervical fractures, the device uses a deep learning model trained end-to-end on 1,338 cases acquired from US and France, representing a distribution of fracture presentations, locations and acquisition protocols, including multiple scanner models from Siemens, Philips, GE and Canon/Toshiba. Additional deep learning models are used to locate the individual vertebrae to exclude images that do not conform to the expected field of view.

DICOM images are received, recorded and filtered before processing. The series are processed chronologically by running algorithms on each series to detect suspected positive findings of a cervical spine fracture, then active notifications on the flagged series are sent to the worklist application.

The Worklist Application displays the active notification of new studies with suspected findings when they come in. All the cervical spine CT studies which include at least 5 visible cervical vertebrae received by CINA-CSpine device are displayed in the worklist and those on which the algorithms have detected a suspected finding are marked with an icon (i.e., active notification). In addition, a compressed, grayscale, unannotated image that is marked "not for diagnostic use" is displayed as a preview function. This compressed preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for diagnostic use beyond notification.

Presenting the trained physician specialist with notification facilitates earlier triage by allowing image prioritization in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.

The CINA platform is an example of medical image communications platform for integrating and deploying the CINA-CSpine image processing application. The medical image communications device (i.e., the technical platform) provides the necessary requirements for interoperability based on the standardized DICOM protocol and services to communicate with existing systems in the hospital radiology department such as CT modalities or other DICOM nodes (DICOM router or PACS for example). It is responsible for transferring, converting formats, notifying of suspected findings and displaying medical device data such as radiological data. The medical image communications server includes the Worklist client application, which receives notifications from the CINA-CSpine Image Processing application.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: 328 clinical anonymized cases.
• Data Provenance: Retrospective, multicenter, multinational. Data was acquired from:
  • US: 60.4% of cases, including a U.S. teleradiology company with a database from various U.S. hospitals across different territories.
  • OUS: 39.6% of cases.
  • Scanner Manufacturers: GE (31.1%), Philips (21.6%), Siemens (28.7%), Canon (18.3%), and 36 different scanner models.
  • Time Periods: The validation dataset was from independent sites and different time periods compared to the training data.

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

• Number of Experts: Three.
• Qualifications of Experts: US-board-certified expert radiologists.

4. Adjudication Method for the Test Set

The ground truth was established by the consensus of the three US-board-certified expert radiologists. This implies a 3-expert consensus (e.g., all 3 agree, or majority vote if specific rules were defined for disagreement, which is not further detailed).

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

No, an MRMC comparative effectiveness study was not reported. The study focused on the standalone performance of the AI device and compared its performance metrics (Sensitivity, Specificity, Time-to-Notification) to those reported for the predicate device. There is no mention of human readers improving with AI assistance.

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

Yes, a standalone performance testing was performed. The described study evaluated the software's performance (Sensitivity and Specificity) in detecting cervical spine fractures on non-contrast CT scans without human intervention in the initial detection process.

7. Type of Ground Truth Used

Expert Consensus: The ground truth was established by the consensus of three US-board-certified expert radiologists.

8. Sample Size for the Training Set

The deep learning model was trained end-to-end on 1,338 cases.

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

The document states that the training data was acquired from US and France, representing a distribution of fracture presentations, locations, and acquisition protocols. However, it does not explicitly detail how the ground truth was established for this training set (e.g., if it was also expert consensus, based on pathology reports, or other methods). It can be inferred that it would likely follow a similar rigorous annotation process to establish "true" fracture presence, but specific details are not provided.

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CINA-VCF is a radiological computer aided triage and notification software indicated for use in patients aged 50 years and over undergoing non-enhanced or contrast-enhanced CT scans which include the chest and/or abdomen.

The device is intended to assist hospital networks and appropriately trained medical specialists within the standard-of-care bone health setting in workflow triage by flagging and communication of suspected positive cases of Vertebral Compression Fractures (VCF) findings.

CINA-VCF uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings on a standalone application in parallel to the ongoing standard of care image interpretation. The device does not alter the original medical image, and it is not intended to be used as a diagnostic device.

The results of CINA-VCF are intended to be used in conjunction with other patient information and based on professional judgment to assist with triage/prioritization of medical images. Notified clinicians are ultimately responsible for reviewing full images per the standard of care.

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

CINA-VCF runs on a standard "off the shelf" server/workstation and consists of VCF Image Processing Application, which can be integrated, deployed and used with the CINA Platform (cleared under K200855) or other compatible medical image communications devices. CINA-VCF receives nonenhanced or contrast-enhanced CT scans (which include the chest and/or abdomen) identified by the CINA Platform or other compatible medical image communications device, processes them using algorithmic methods involving execution of multiple computational steps to identify suspected presence of Vertebral Compression Fractures (VCF) findings and generates results files to be transferred by CINA Platform or a similar medical image communications device for output to a PACS system or workstation for worklist prioritization.

DICOM images are received, recorded and filtered before processing. The series are processed chronologically by running algorithms on each series to detect suspected positive findings of a vertebral compressions fracture (VCF).

The device uses deep learning models to detect VCF at the T1-L5 level. The models were trained endto-end on a dataset of 886 series collected from multiple centers in the USA and France satisfying the device protocol and representing a large distribution of scanner models from Siemens, Philips, GE and Canon (formerly Toshiba), acquisition protocols, spine presentation and fracture location and severity. Additional models, trained on subsets of this dataset, are used to locate the spine, identify the vertebra bodies and exclude vertebra which have been subjected to vertebroplasty or contains orthopedic material.

The Worklist Application displays all incoming suspect cases, each notified case is marked with an icon. In addition, compressed, grayscale, unannotated images that are captioned "not for diagnostic use" is displayed as a preview function. This compressed preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for diagnostic use beyond notification.

Presenting the specialist with worklist prioritization facilitates earlier triage by allowing prioritization of 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 CINA Platform is an example of medical image communications platform for integrating and deploying the CINA-VCF image processing applications. It provides the necessary requirements for interoperability based on the standardized DICOM protocol and services to communicate with existing systems in the hospital radiology department such as CT modalities or other DICOM nodes (DICOM router or PACS for example). It is responsible for transferring, converting formats, notifying of suspected findings and displaying medical device data such as radiological data. The CINA Platform server includes the Worklist client application which receives notifications from the CINA-VCF Image Processing application.

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

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: 474 clinical anonymized cases.
• Data Provenance: Retrospective, multinational study. Data provided from multiple US (66.9%) and OUS (33.1%) clinical sites. The data included 180 (37.9%) positive cases (CT with VCF) and 294 (62.1%) negative cases.
• Patient Demographics: Mean age 72.1 ± 10.1 years old (MIN = 50 yo and MAX = 100 yo), 50.8% female. Data accounted for race/ethnicity in the intended US patient population.
• Image Acquisition: Acquired by 4 different scanner makers and 38 different scanner models. Various scanner parameters were considered, including slice thickness, number of detector rows, kVp ranges, contrast vs. non-contrast, imaging protocol (chest and/or abdomen), and reconstruction kernel (soft/standard).

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

• Number of Experts: Three.
• Qualifications: US-board-certified expert radiologists.

4. Adjudication Method for the Test Set

• Method: Consensus of three US-board-certified expert radiologists. A case was considered positive if at least one moderate or severe vertebral compression fracture located within the thoracic or lumbar spine was identified by the experts.

5. If a Multi-reader Multi-case (MRMC) Comparative Effectiveness Study Was Done

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted to evaluate human readers with and without AI assistance for effect size. The study focused on the standalone performance of the AI device and compared its time-to-notification to a predicate device, not directly to human reader performance with or without the AI.

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

• Yes, a standalone performance testing was performed. The study describes "Avicenna.Al conducted a retrospective, multinational and blinded study with the CINA-VCF application... to evaluate the software's performance."

7. The Type of Ground Truth Used

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

8. The Sample Size for the Training Set

• Sample Size: 886 series.

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

• The device uses deep learning models that "were trained end-to-end on a dataset of 886 series collected from multiple centers in the USA and France satisfying the device protocol and representing a large distribution of scanner models... and fracture location and severity." While the text describes the dataset, it does not explicitly state how the ground truth for this training set was established. It implies that the "device protocol" guided the selection of data, likely with some form of expert labeling or pre-existing clinical reports classifying the fractures, similar to the test set ground truth, but this is not directly stated.

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

The Software automatically reorients images, segments and analyzes ASPECTS Regions of Interest (ROIs). CINA-ASPECTS extracts image data for the ROI(s) to provide analysis and computer analytics based on morphological characteristics. The imaging features are then synthesized by an artificial intelligence algorithm into a single ASPECT (Alberta Stroke Program Early CT) Score.

CINA-ASPECTS is indicated for evaluation of patients presenting for diagnostic imaging workup with known MCA or ICA occlusion, for evaluation of extent of disease. Extent of disease refers to the number of ASPECTS regions affected which is reflected in the total score. This device provides information that may be useful in the characterization of early ischemic (acute) brain tissue injury during image interpretation.

CINA-ASPECTS provides a comparative analysis to the ASPECTS standard of care radiologist assessment using the ASPECTS region definitions and highlighting ROIs and numerical scoring.

Limitations:

1. CINA-ASPECT is not intended for primary interpretation of CT images. It is used to assist physician evaluation.
2. CINA-ASPECT has been validated in patients with known MCA or ICA unilateral occlusion prior to ASPECTS scoring.
3. CINA-ASPECTS is not suitable for use on brain scans displaying neurological pathologies other than acute stroke, such as tumors or abscesses, traumatic brain injuries, hemorrhagic transformation and hematoma.
4. Use of CINA-ASPECT in clinical settings other than brain ischemia within 12 hours from time last known well, caused by known ICA or MCA occlusions has not been tested.
5. CINA-ASPECTS has only been validated and is intended to be used in patient populations aged over 21.
6. CINA-ASPECTS has been validated and is intended to be used with images acquired with Canon Medical Systems Corporation, GE Healthcare, Philips Healthcare and Siemens Healthineers scanners.

Contraindications/Exclusions/Cautions:

• Patient motion: Excessive patient motion leading to artifacts that make the scan technically inadequate.
• Important streak artifacts and noisy images: Presence of important streak artifact and significant noise within the NCCT images that make the scan technically inadequate.
• Hemorrhagic Transformation, Hematoma.

CINA-ASPECTS is a standalone computer-aided diagnosis (CADx) software that processes noncontrast head CT (NCCT).

CINA-ASPECTS is a standalone executable program that may be run directly from the commandline or through integration, deployment and use with medical image communications devices. The software does not interface directly with any CT scanner or data collection equipment; instead, the software receives non-contrast head CT (NCCT) scans identified by medical image communications devices, processes them using algorithmic methods involving execution of multiple computational steps to provide an automatic ASPECT score based on the case input file for the physician.

The score includes which ASPECT regions are identified based on regional imaging features derived from non-contrast computed tomography (NCCT) brain image data and overlaid onto brain scan images. The results are generated based on the Alberta Stroke Program Early CT Score (ASPECTS) guidelines and provided to the clinician for review and verification. At the discretion of the clinician, the scores may be adjusted based on the clinician's judgment.

Series are processed by running the CINA-ASPECTS Image Processing Applications on noncontrast head CT images (NCCT) to perform the:

• Reorientation, tilt-correction of the input imaging data;
• Delineation of predefined regions of interest on the corrected input data and computing numerical values characterizing underlying voxel values within those regions;
• Visualizing the voxels which have contributed to the ASPECTS score (also referred to as a 'heat map'); and
• Labeling of these delineated regions and providing a summary score reflecting the number of regions with early ischemic change as per ASPECTS guidelines.

The CINA-ASPECTS User Interface Agent provides the ASPECTS information to the clinician to review and edit. It also requires the confirmation by a clinician that a Large Vessel Occlusion (LVO) is detected. This confirmation is used by the CINA-ASPECTS to limit the detection of areas of early ischemic changes to the infarcted brain hemisphere selected by the user. The final summary score together with the regions selected and underlying voxel values are then stored in DICOM format to be transferred by the medical image communications device for output to a Picture Archiving and Communication System (PACS) or workstation.

The CINA-ASPECTS device is made of two components:

• The CINA-ASPECTS image processing application which reads the input file and generates an automatic ASPECT score and the applications outputs
• A CINA-ASPECTS UI Agent which provides the ASPECTS information to the clinician to review and edit for final archiving.

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

CINA-ASPECTS Device Acceptance Criteria and Performance Study

1. Table of Acceptance Criteria and Reported Device Performance

The provided document details two main studies: a Standalone Performance Testing and a Clinical Multi-Reader Multi-Case (MRMC) Performance Study. The acceptance criteria aren't explicitly listed as a separate table with pass/fail metrics in the summary, but rather are demonstrated through the successful outcomes of these studies. The performance metrics reported are measures of the device's accuracy and utility.

Note: The FDA 510(k) summary typically presents a high-level overview. Specific numerical acceptance thresholds (e.g., "sensitivity must be > X%") are often detailed in the full submission but are not fully elaborated here. Instead, the document states that the device "met all design requirements and specifications" and "achieved its primary endpoint," implying successful adherence to pre-defined acceptance criteria.

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

• Sample Size: 200 clinical anonymized NCCT cases.
• Data Provenance: Retrospective, multinational, multi-vendor dataset from 5 clinical sites in two countries (US and France). Acquired by 4 different scanner makers (GE, Siemens, Canon, Philips) and 27 different scanner models.

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

The document mentions that the MRMC study evaluated the performance of "8 clinical readers" and that the "clinical data demonstrates that CINA-ASPECTS shows a significant improvement in the agreement between the readers and a reference standard." However, it does not explicitly state the number or qualifications of experts used to establish the ground truth specifically for the standalone performance test.

For the MRMC study readers, it states: "The panel of readers consisted of 4 expert neuroradiologists and 4 non-experts from different specialties (stroke neurologist, general radiologist, neurointensivist, vascular neurologist), representing the intended use population." While these readers contributed to the "aided" and "unaided" performance evaluation, they are not explicitly designated as the ground truth setters for the test set. The term "reference standard" is used, implying a separate, likely expert-derived, ground truth, but its specifics are not detailed here.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method (e.g., 2+1, 3+1) used to establish the ground truth for the test set. It mentions agreement with a "reference standard" in the context of the MRMC study, but not how that reference standard was formed.

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

• Was an MRMC study done? Yes, a retrospective, multinational, multi-vendor, and blinded Clinical Multi-Reader Multi-Case (MRMC) Performance study was conducted.
• Effect size of how much human readers improve with AI vs without AI assistance:
  • Agreement with reference standard: With CINA-ASPECTS, readers agreed, on average, with almost ½ a region (4.1%, [95% Cl: 3.3% -4.9%]) more per scan than without CINA-ASPECTS.
  • Overall ROC AUC: Improved significantly from 0.75 (Unaided arm) to 0.79 (Aided arm).
  • Reduced variation: The range in the ROC AUC between users was narrower when assisted by the software.
  • Time spent: Mean time spent per case among all readers was significantly reduced when using CINA-ASPECTS.

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

• Was a standalone study done? Yes. "Standalone performance testing was conducted to comply with special controls for this device type."

7. Type of Ground Truth Used

The document states that in the standalone performance testing, "The CINA-ASPECTS device performed properly and matched with the ground truth." For the MRMC study, it refers to improvement in "agreement between the readers and a reference standard."
However, the specific methodology for establishing this "ground truth" or "reference standard" (e.g., expert consensus of several independent radiologists, pathology results, outcomes data) is not explicitly detailed in the provided text. It is implied to be expert-derived, given the context of radiological assessment.

8. Sample Size for the Training Set

The document states, "The validation dataset was separated from the one used for the algorithm training/testing and has never been used in any way in the development of the software device." However, the sample size for the training set is not provided in this summary.

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

The document describes how the validation dataset was separated from the training/testing data but does not specify how the ground truth for the training set was established.

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CINA-iPE is a radiological computer-aided triage and notification software indicated for use in patients undergoing contrast-enhanced CT scans (not dedicated CTPA protocol) for other clinical indications than pulmonary embolism suspicion, including at least a part of the lung. The device is intended to assist hospital networks and appropriately trained radiologists in workflow triage by flagging and communicating suspected positive findings for incidental Pulmonary Embolism (iPE). The device is indicated for adults and transitional adolescents (18 to 21 years old but treated as adults).

CINA-iPE uses an artificial intelligence algorithm to analyze images and highlight cases with detected incidental PE on a standalone application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected incidental PE findings. The device is not designed to detect PE in subsegmental arteries.

Notifications include compressed preview images that are meant for informational purposes only and are not intended for diagnostic use beyond notification. The device does not alter the original medical image, and it is not intended to be used as a diagnostic device.

The results of CINA-iPE 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 ultimately responsible for reviewing full images per the standard of care.

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

CINA-iPE runs on a standard "off the shelf" server/workstation and consists of an Image Processing Application, which can be integrated, deployed, and used with the CINA Platform (cleared under K200855) or other medical image communications devices. CINA-iPE receives contrast-enhanced CT scans (not dedicated CTPA protocol) including at least a part of the lung identified by the CINA Platform or other medical image communications device, processes them using deep learning algorithms involving the execution of multiple computational steps to identify the suspected presence of an incidental pulmonary embolism and generates results files to be transferred by CINA Platform or a similar medical image communications device for output to a PACS system or worklist prioritization.

To identify the suspected presence of pulmonary embolisms, the device uses a deep learning model trained end-to-end on 5.429 cases acquired from US and France, representing a distribution of PE sizes, locations and acquisition protocols, including multiple scanner models from Siemens, Philips, GE and Canon/Toshiba. Additional models are used to locate the aorta and main pulmonary artery, enabling assessment of the contrast timing. The lung's parenchyma is segmented to evaluate both the presence of the lungs in the field of view and to limit the region of interest for detecting the presence of pulmonary embolisms.

DICOM images are received, recorded and filtered before processing. The series are processed chronologically by running algorithms on each series to detect suspected positive findings of an incidental Pulmonary Embolism (iPE), then active notifications on the flagged series are sent to the Worklist Application.

The Worklist Application displays the active notification of new studies with suspected findings when they come in. All the contrast-enhanced CT studies received by CINA-iPE device are displayed in the worklist and those on which the algorithms have detected finding are marked with an icon (i.e., passive notification). In addition, a compressed, grayscale, unannotated image that is marked "not for diagnostic use" is displayed as a preview function. This compressed preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for diagnostic use beyond notification.

Presenting the radiologist with notification facilitates earlier triage by allowing prioritization of images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care image interpretation practice alone.

The CINA platform is an example of medical image communications platform for integrating and deploying the CINA-iPE image processing application. The medical image communications device (i.e., the technical platform) provides the necessary requirements for interoperability based on the standardized DICOM protocol and services to communicate with existing systems in the hospital radiology department such as CT modalities or other DICOM nodes (DICOM router or PACS for example). It is responsible for transferring, storing, converting formats, notifying of suspected findings and displaying medical device data such as radiological data. The medical image communications server includes the Worklist client application in which notifications from the CINA-iPE Image Processing application are received.

The provided text describes the acceptance criteria and the study conducted to prove that the CINA-iPE device meets these criteria.

Here's an organized breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The primary acceptance criteria for the CINA-iPE device were its Sensitivity and Specificity in identifying incidental Pulmonary Embolism (iPE), measured against a performance goal of 80%.

Additional Performance Data (Sub-group Analysis):

Time-to-Notification:

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

• Sample Size for Test Set: 381 clinical anonymized cases.
• Data Provenance: Retrospective, multinational study.
  • Country of Origin: Data was acquired from multiple U.S. and OUS (Outside US) clinical sites. Specifically, 56.4% (215) of cases came from U.S. clinical sources.
  • Retrospective/Prospective: Retrospective.
  • Independence: The independence of the standalone validation dataset from the training data was ensured using data from independent sites and different time periods.
  • Patient Demographics: 53.5% Male and 46.7% Female. Mean ± SD age: 64.5 ± 15.8 years (range: 18 - 99 years).
  • Scanner Diversity: Acquired primarily by 4 different scanner makers (GE-31.5%, Philips-28.3%, Siemens-26%, and Canon-13.9%) and 39 different scanner models.

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

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

4. Adjudication Method for the Test Set

The ground truth was established by consensus of the three US-board-certified expert radiologists. While the specific mechanism of reaching consensus (e.g., 2 majority, discussion, etc.) is not detailed, the term "consensus" implies agreement among the experts.

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

• Was an MRMC study done? The provided text does not explicitly state that a multi-reader multi-case (MRMC) comparative effectiveness study was done to evaluate how human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the algorithm and its time-to-notification effectiveness for triage/prioritization.
• Effect Size of Human Improvement: Not applicable, as an MRMC comparative effectiveness study was not described.

6. Standalone (Algorithm Only) Performance Study

• Was a standalone study done? Yes, a standalone performance testing study was conducted.
• Details: The study evaluated the CINA-iPE application's performance in identifying incidental pulmonary embolisms (iPE) on contrast-enhanced CT images. The primary endpoint was the device's Sensitivity and Specificity.

7. Type of Ground Truth Used

• Ground Truth Type: Expert Consensus. The ground truth was established by the consensus of three US-board-certified expert radiologists.

8. Sample Size for the Training Set

• Sample Size for Training Set: 5,429 cases.

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

• Method: The deep learning model was "trained end-to-end on 5.429 cases acquired from US and France, representing a distribution of PE sizes, locations and acquisition protocols." The precise method for establishing ground truth for training is not explicitly detailed but it's implied that these cases were labeled with PE sizes and locations, likely through expert review similar to the test set, but this is not explicitly stated.

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Cina is a radiological computer aided triage and notification software in the analysis of (1) not-enhanced head CT images and (2) CT angiography of the head.

The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communicating suspected positive findings of (1) head CT images for Intracranial Hemorthage (ICH) and (2) head CT angiography for large vessel occlusion (LVO) of the anterior circulation (distal ICA, MCA-M1 or proximal MCA-M2). Cina uses an artificial intelligence algorithm to analyze images and highlight cases with detected (1) ICH or (2) LVO on a standalone Web application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected ICH or LVO findings.

Notifications include compressed preview images that are meant for informational purposes only, and are not intended for diagnostic use beyond notification. The device does not alter the original medical image, and it is not intended to be used as a diagnostic device.

The results of Cina are intended to be used in conjunction with other patient information and based on professional judgement to assist with triage/prioritization of medical images. Notified clinicians are ultimately reviewing full images per the standard of care.

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

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

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

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

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

1. Table of Acceptance Criteria and Reported Device Performance

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

• ICH Test Set: 814 clinical anonymized cases
• LVO Test Set: 476 clinical anonymized cases
• Data Provenance: Retrospective, multinational study from 3 clinical sources (2 US and 1 OUS).
  • For LVO positive cases: 156 (83%) were US and 32 (17%) OUS.
  • The datasets contained a sufficient number of cases from important cohorts regarding imaging acquisitions (scanner makers: GE, Siemens, Philips, Toshiba/Canon; number of detector rows, gantry tilt, and slice thickness) and patient groups (age, sex, and US regions).

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

• Number of Experts: Three (3)
• Qualifications of Experts: US-board-certified neuroradiologist readers. (Specific years of experience not mentioned).

4. Adjudication Method for the Test Set

• Method: Concurrence of three US-board-certified neuroradiologist readers. (This implies a consensus or majority agreement method, often referred to as "3+0" or "majority rule" for establishing ground truth.)

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

• The provided document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study evaluating how human readers improve with or without AI assistance. The study described focuses on the standalone performance of the algorithm.

6. Standalone Performance Study

• Yes, a standalone (algorithm only) performance study was done. The sections "IX. Performance Testing" and "IX.2. Performance Testing" explicitly describe the evaluation of the Cina software's performance (Sensitivity, Specificity, AUC, Accuracy, and Time-to-notification) directly against the established ground truth.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus (established by the concurrence of three US-board-certified neuroradiologist readers, evaluating imaging findings). The text refers to "operators' visual assessments" which, in this context, refers to the expert readers' visual interpretation used to define the ground truth.

8. Sample Size for the Training Set

• The document does not explicitly state the sample size for the training set. It mentions the algorithm uses "an artificial intelligence algorithm with database of images" but does not provide the size of this database or how it was used for training versus testing.

9. How Ground Truth for the Training Set Was Established

• The document does not explicitly state how the ground truth for the training set was established. It only describes the method for the ground truth of the test set (concurrence of three neuroradiologists).

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CINA CHEST is a radiological computer aided triage and notification software indicated for use in the analysis of Chest and Thoraco-abdominal CT angiography. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communicating suspected positive findings of (1) Chest CT angiography for Pulmonary Embolism (PE) and (2) Chest or Thoraco-abdominal CT angiography for Aortic Dissection (AD).

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

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

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

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

DICOM images are received, recorded and filtered before processing. The series are processed chronologically by running algorithms on each series to detect suspected positive findings of a pulmonary embolism (PE) or an aortic dissection (AD), then notifications on the flagged series are sent to the Worklist Application.

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

Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) summary for CINA CHEST:

Acceptance Criteria and Reported Device Performance

Study Details

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

   • Pulmonary Embolism (PE): 396 clinical anonymized cases.
   • Aortic Dissection (AD): 298 clinical anonymized cases.
   • Data Provenance: Retrospective, multicenter study. Data was provided from multiple US clinical sites (230 US cities for PE, and 200 US cities for AD).

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

   • Number of Experts: "Several US-board-certified radiologist readers." The exact number is not specified beyond "several".
   • Qualifications: US-board-certified radiologists. No specific years of experience are mentioned.

3. Adjudication method for the test set:

   • The ground truth was established by "concurrence of several US-board-certified radiologist readers." This implies a consensus-based adjudication, but the specific method (e.g., majority vote, unanimous agreement, or an independent adjudicator in case of disagreement) is not explicitly detailed.

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

   • No, an MRMC comparative effectiveness study was not reported. The study described is a standalone performance evaluation of the CINA CHEST software against a ground truth. It assesses the device's ability to identify PE and AD cases for triage, not the improvement of human readers with AI assistance.

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

   • Yes, a standalone study was done. The document explicitly states: "Avicenna.Al conducted a retrospective, multicenter and blinded study with the CINA CHEST software with the primary endpoint to evaluate the software's performance..." and later refers to "The results of the standalone assessment study demonstrated an overall agreement (Accuracy)..." This confirms the study evaluated the algorithm's performance in isolation.

6. The type of ground truth used:

   • Expert Consensus. The ground truth was "established by concurrence of several US-board-certified radiologist readers."

7. The sample size for the training set:

   • The document does not specify the sample size for the training set. It only details the test set used for performance evaluation.

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

   • Since the training set sample size is not provided, the method for establishing its ground truth is also not detailed in this document. It is common for AI algorithms to be trained on data with ground truth established by expert radiologists or pathology, but this specific information is absent here.

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

CINA uses an artificial intelligence algorithm to analyze images and highlight cases with detected (1) ICH or (2) LVO on a standalone Web application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected ICH or LVO findings. Notifications include compressed preview images that are meant for informational purposes only, and are not intended for diagnostic use beyond notification. The device does not alter the original medical image, and it is not intended to be used as a diagnostic device.

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

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

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

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

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

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

1. Acceptance Criteria and Reported Device Performance

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

Table of Acceptance Criteria and Reported Device Performance

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