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The Viz Subdural+ (Subdural Plus) device is intended for automatic labeling, visualization and quantification of collections in the subdural space from a set of Non-Contrast Head CT (NCCT) images. The software is intended to automate the current manual process of identifying, labeling and quantifying the volume of collections in the subdural space identified on NCCT images. Viz Subdural + provides volumes from NCCT images acquired at a single time point.

The Viz Subdural+ software is intended for labeling subdural collections and reporting the grayscale value of the collection, widest width of the subdural collection, and midline shift. The device output should be reviewed along with the patient's original images by a physician qualified to interpret brain CT images.

Viz Subdural+ is a software-only device that uses a locked artificial intelligence machine learning (AI/ML) algorithm to process and analyze non-contrast CT (NCCT) scans of the head to automatically measure the collections in the subdural region in the brain and midline shift.

The device output provides visual overlays of automatically measured subdural collections where the overlay opacity (intensity) corresponds to the grayscale value of the collection within the native NCCT, and reports the total volume and widest width of the subdural collections. The device also automates and reports the measure of midline shift.

The results of the automated measurement are provided in a summary series and segmentation series in DICOM format. The summary series consists of a summary table of subdural collections, snapshot of each collection and a midline shift measurement. The first slice of the Subdural+ summary series summarizes the measurement results of each subdural collection (volume and widest width), total volume and midline shift in tabular format. The summary series also contains a snapshot of each subdural collection and a snapshot of the midline shift measurement. The segmentation series shows an RGB overlay where a subdural collection is identified by a colored overlay with the color intensity corresponding to the HU values of the original image on each slice of the input series of the segmented region. On slices with an overlay representing a measured subdural collection, the volume of the subdural collection is provided. The midline shift is overlaid and provided on the slice where the midline shift is measured.

Images are automatically forwarded from the Healthcare Facility and sent to Viz.ai's Backend Server after acquisition at the CT scanner. Viz Subdural+ is hosted on Viz.ai's Backend Server and automatically analyzes applicable NCCT scans that are acquired on CT scanners and are forwarded to Viz.ai's Backend Server. The results of the analysis are exported in DICOM format and are sent to a DICOM destination (e.g., PACS) where they are available for review by radiologists, neurologists, neuro-surgeons, interventional neuroradiologists, or other appropriately trained professionals to assist in the measurement of subdural collection volume, widest subdural collection width and midline shift.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter for Viz Subdural+:

Acceptance Criteria and Reported Device Performance

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The Viz HDS device is intended for automatic labeling, visualization, and quantification of segmentable brain structures from a set of Non-Contrast CT (NCCT) head scans. The software is intended to automate the current manual process of identifying, labeling, and quantifying the volume of segmentable brain structures identified on NCCT images. Viz HDS provides volumes from NCCT scans acquired at a single time point. The Viz HDS software is indicated for use in the analysis of the following structures: Intracranial Hyperdensities, Lateral Ventricles and Midline Shift. The device output should be reviewed along with patient's original images by a physician.

Viz HDS is a software-only device that uses a locked artificial intelligence machine learning (AI/ML) algorithm to processes non-contrast head CT scans to outline intracranial hyperdensity areas, lateral ventricles (right and left), midline shift, and then quantify the volume of intracranial hyperdensity(ies), volume of lateral ventricle asymmetry ratio and distance of midline shift.

Viz HDS analyzes the head NCCT series in DICOM format and produces a summary series and a segmentation series in DICOM format. The summary series is a two-slice output: a single slice from the NCCT series with segmented areas overlaid on it, and a summary table providing the calculated measurements. The segmentation series shows an RGB overlay, on each slice of the input series, of the lateral ventricles and hyperdensity(ies) segmentation masks and a midline shift. For slices including hyperdensities or ventricle/ventricles, its volume would be mentioned in a color legend that is also overlaid on the slice. The colors are only for visual differentiation between the segmented regions, the colors don't have a meaning on their own. The device output is exported in DICOM format, which is sent to a pre-configured PACS destination together with the original NCCT series for review by a physician to aid in the assessment of measuring intracranial hyperdensity(ies), lateral ventricles, and midline shift.

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

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria and Device Performance for Viz HDS

Note: The text explicitly states "less than 7.5 mL" and "greater than 70%", confirming the device met the specified criteria.

2. Sample Size and Data Provenance for Test Set

• Sample Size for Test Set: Not explicitly stated in the provided text.
• Data Provenance: Not explicitly stated in the provided text. The text mentions "clinical site" for stratification, but not the origin of the data itself (e.g., country, specific hospitals).
• Retrospective or Prospective: Not explicitly stated.

3. Number and Qualifications of Experts for Ground Truth (Test Set)

• Number of Experts: Not explicitly stated. The text mentions "trained radiologists" was involved in establishing the ground truth.
• Qualifications of Experts: The experts were "trained radiologists." No further details on their experience (e.g., "10 years of experience") are provided.

4. Adjudication Method (Test Set)

• Adjudication Method: Not explicitly stated. The text only mentions that "ground truth as established by trained radiologists." It does not detail how disagreements among radiologists, if any, were resolved.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not explicitly mentioned as being performed. The study described compares the device's output to "ground truth as established by trained radiologists" and does not describe a scenario where human readers' performance with and without AI assistance was measured.
• Effect Size of Human Readers Improvement: Not applicable, as an MRMC study was not performed.

6. Standalone Performance (Algorithm Only)

• Standalone Performance: Yes, a standalone performance study was done. The study compares the Viz HDS's output directly to the established ground truth. This is a measure of the algorithm's performance without direct human intervention in the measurement process itself, although the output is intended for physician review.

7. Type of Ground Truth Used

• Type of Ground Truth: The ground truth was established by "expert consensus" from trained radiologists. The text states, "ground truth as established by trained radiologists."

8. Sample Size for Training Set

• Sample Size for Training Set: Not explicitly stated in the provided text.

9. How Ground Truth for Training Set Was Established

• Ground Truth for Training Set: Not explicitly stated in the provided text.

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Viz HCM is intended to be used in parallel to the standard of care to analyze recordings of 12-lead ECG made on compatible ECG devices. Viz HCM is capable of analyzing the ECG, detecting signs associated with hypertrophic cardiomyopathy (HCM), and allowing the user to view the ECG and analysis results. Viz HCM is indicated for use on 12-lead ECG recordings collected from patients 18 years of age or older. Viz HCM is not intended for use on patients with implanted pacemakers. Viz HCM is limited to analysis of ECG data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis. Viz HCM identifies patients for further HCM follow-up and does not replace the current standard of care methods for diagnosis of HCM. The results of the device are not intended to rule-out HCM follow-up.

The Viz HCM ECG Analysis Algorithm (HCM Algorithm) is a machine learning-based software algorithm that analyzes 12-lead electrocardiograms (ECGs) for characteristics suggestive of hypertrophic cardiomyopathy (HCM). The mobile software module enables the end user to receive and toggle notifications for ECGs determined by the Viz HCM ECG Analysis Algorithm to contain signs suggestive of HCM.

The Viz HCM is a Software as a Medical Device (SaMD) intended to analyze ECG signals collected as part of a routine clinical assessment, independently and in parallel to the standard of care. Viz HCM is a combination of software modules that consists of an ECG analysis software algorithm and mobile application software module.

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

Acceptance Criteria and Device Performance

The core acceptance criteria for the Viz HCM device are implicitly defined by the sponsor's performance metrics and the explicit special controls outlined by the FDA. The performance testing section provides the evidence that the device meets these criteria.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a De Novo request, specific pre-defined quantitative acceptance criteria (e.g., "Sensitivity must be > X%") are often not explicitly stated upfront in the narrative. Instead, the "Performance Testing" section presents the demonstrated performance as evidence for acceptance. The FDA then evaluates if this performance is acceptable given the device's intended use and risks.

Based on the provided text, the key performance metrics and their reported values are:

Implicit Acceptance Criteria (from Special Controls and Risk Analysis):

• Clinical Performance Testing (Special Control 1):
  • Device performs as intended under anticipated conditions of use.
  • Clinical validation uses a test dataset of real-world data from a representative patient population.
  • Data is representative of sources, quality, and encountered conditions.
  • Test dataset is independent from training/development data.
  • Sufficient cases from important cohorts (demographics, confounders, comorbidities, hardware/acquisition characteristics) are included for subgroup analysis.
  • Study protocols include ground truth adjudication processes.
  • Consistency of output demonstrated over the full range of inputs.
  • Performance goals justified in context of risks.
  • Objective performance measures reported with descriptive/developmental measures.
  • Summary-level demographic and subgroup analyses provided.
  • Test dataset includes a minimum of 3 geographically diverse sites (separate from training).
• Software Verification, Validation, and Hazard Analysis (Special Control 2):
  • Model description, inputs/outputs, patient population.
  • Integration testing in intended system.
  • Impact of sensor acquisition hardware on performance.
  • Input signal/data quality control.
  • Mitigations for user error/subsystem failure.
• Human Factors Assessment (Special Control 3):
  • Evaluates risk of misinterpretation of device output.
• Labeling (Special Control 4):
  • Summary of performance testing, hardware, patient population, results, demographics, subgroup analyses, minimum performance.
  • Device limitations/subpopulations where performance may differ.
  • Warning against ruling out follow-up based on negative finding.
  • Statement that output shouldn't replace full clinical evaluation.
  • Warnings on sensor acquisition factors impacting results.
  • Guidance for interpretation and typical follow-up.
  • Type of hardware sensor data used.

Study Details for Proving Acceptance

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

• Test Set Sample Size: 3,196 ECG cases (291 HCM-Positive and 2905 HCM-Negative).
• Data Provenance: Retrospective study. Data collected from 3 hospitals in the US (Boston, Massachusetts area - 2 sites; Salem, Massachusetts - 1 site). The Boston sites are described as racially and ethnically diverse, while the Salem site was predominantly Caucasian or Latino. Data was collected between July 1, 2017, and June 30, 2022.

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

• Number of Experts: A single cardiologist performed the initial chart and imaging review for each HCM-Positive or HCM-Negative case to establish the ground truth.
• Qualifications of Experts: Described as "cardiologist." No further details on their years of experience or specific board certifications are provided in the excerpt. A "second cardiologist" was used for a secondary assessment on a subset of cases to check agreement/consistency.

4. Adjudication Method for the Test Set

• Method: A single cardiologist established the ground truth for each case through chart and imaging review based on predefined guidelines (Cornell criteria or Sokolow-Lyon criteria).
• Consistency Check: A "secondary assessment" was performed on a selection of 60 cases (30 HCM-Positive, 30 HCM-Negative) where a second cardiologist independently truthed the cases to perform an analysis of agreement/consistency. The results of this agreement analysis are not detailed, but the method was a 1+1 adjudication for a subset. For the main test set, it was effectively a "none" (single expert review) or rather an individual expert labeling.

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

• No MRMC Study was described. The provided text focuses on the standalone performance of the algorithm and does not include a comparative effectiveness study involving human readers with and without AI assistance. The device is intended to be used "in parallel to the standard of care," suggesting it provides an additional signal, not necessarily assistance to human readers interpreting ECGs.

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

• Yes, a standalone performance study was done. The entire "PERFORMANCE TESTING" section, especially "SUMMARY OF CLINICAL INFORMATION," describes the performance of the Viz HCM algorithm in identifying suspected HCM from ECGs compared directly to the clinical ground truth established by cardiologists. The reported sensitivity, specificity, and PPV are all "algorithm-only" performance metrics.

7. The Type of Ground Truth Used

• Expert Consensus/Clinical Records Review: The ground truth for the test set was established by a cardiologist (single expert for primary truth, with a second expert for consistency check on a subset) who performed a chart and imaging review for each patient. This was based on "predefined guidelines using either the Cornell criteria or the Sokolow-Lyon criteria." ICD-10 codes were used for initial sampling, but the definitive ground truth was established by clinical review. This is a form of expert consensus/clinical documentation ground truth.

8. The Sample Size for the Training Set

• Training Set Sample Size: 301,106 patients, encompassing 831,329 ECG exams.
  • HCM positive patients: 4,470
  • HCM negative patients: 298,394

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

• The text states: "The data for algorithm development was collected from different US and Non-US (OUS) sources. The data contains both HCM Positive (obstructive and nonobstructive) and HCM Negative examples including random ECG samples (random control) and enrichment for conditions differential for and associated with HCM (negative controls)."
• It further clarifies that for HCM-Negative cases in the development (training and internal validation) dataset, absence of HCM was determined by the "lack of ICD-9/10 code for HCM."
• For HCM-Positive and HCM-Negative cases with available imaging, "additional chart review and review of imaging provided more confidence into the label."

In summary, for the training set, the ground truth was established primarily through ICD-9/10 codes, supplemented by chart review and imaging review where available. This suggests a semi-automated, large-scale labeling approach for the training data, potentially with manual review for confirmation or difficult cases.

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Viz LVO is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of standard of care workflow.

Viz LVO uses an artificial intelligence algorithm to analyze images for findings suggestive of a pre-specified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes CT angiogram images of the brain acquired in the acute setting, and sends notifications to a neurovascular specialist that a suspected large vessel occlusion has been identified and recommends review of those images. Images can be previewed through a mobile application. Viz LVO is intended to analyze terminal ICA and MCA-M1 vessels for LVOs.

Images that are previewed through the mobile application are compressed and are for informational purposes only and not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests. Viz LVO is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

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

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

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

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

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

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

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

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

Therefore, I cannot provide:

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

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

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The Viz RV/LV Software device is designed to measure the maximal diameters of the right and left ventricles of the heart from a volumetric CTPA acquisition and report the ratio of those measurements. Viz RV/LV analyzes cases using an artificial intelligence algorithm to identify the location and measurements of the ventricles. The Viz RV/LV software provides the user with annotated images showing ventricular measurements. Its results are not intended to be used on a stand-alone basis for clinical decision-making or otherwise preclude clinical assessment of CTPA cases.

The Viz RV/LV is a software-only device that uses a locked artificial intelligence machine learning (AI/ML) algorithm to measure the maximal diameters of the right and left ventricles of the heart from a computed tomography pulmonary angiogram (CTPA) and report the ratio of those measurements. Viz RV/LV produces an Annotated Image Series (Figure 1) and an RV/LV Summary Report (Figure 2) in DICOM format. The Annotated Image Series shows an RGB overlay on each slice of the input scan: The red and blue solid lines indicate the maximum ventricular diameter for each ventricle. The dashed line indicates a diameter measured on a slice that is within 10 slices of the global maximum ventricular diameter. The interventricular septum is marked in solid green on all images where diameters are marked. The maximal diameter is presented along with solid lines on slices with global maximum diameter. The RV/LV Summary Report summarizes the results of the ventricle analysis and shows the slices with the maximum right and left ventricular diameters. The lines measuring the maximum RV and LV diameters are displayed over the original CTPA slice image, along with the lengths of the largest RV and LV diameters, and the RV/LV ratio. Viz RV/LV is hosted on Viz.ai's Backend Server and analyzes applicable CTPA scans that are acquired on CT scanners and are forwarded to Viz.ai's Backend Server. The results of the analysis are exported in DICOM format are sent to a PACS destination for review by thoracic radiologists, general radiologists, pulmonologists, cardiologists, or other similar physicians to assist in the assessment of right ventricle enlargement.

Here's an analysis 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

The acceptance criteria are not explicitly stated as distinct numerical targets in the same way that a "performance goal" for MAE is mentioned. However, based on the wording, we can infer the primary acceptance criterion for the algorithm's accuracy from the clinical performance section.

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

• Test Set Sample Size: Not explicitly stated as a number of cases, but the study implies a sufficient sample for statistical analysis. It mentions "The 4 clinical sites used in the pivotal study" are a subset of 13 larger sites.
• Data Provenance:
  • Country of Origin: Not explicitly stated, but the submission is to the U.S. FDA, implying compliance with U.S. regulatory standards. Clinical sites are mentioned, suggesting real-world data collection.
  • Retrospective or Prospective: Not explicitly stated, but the mention of "4 clinical sites used in the pivotal study were a subset of a larger 13 sites used as part of the training data set" and "no overlapping data between the training sets and the pivotal study in terms of time and patient images" suggests that the test set data was collected independently from the training data, likely retrospectively for the purpose of this analysis, but drawn from existing clinical acquisitions.

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

• Number of Experts: Not explicitly stated as a specific number, but referred to as "trained radiologists" (plural), indicating more than one.
• Qualifications of Experts: Described as "trained radiologists." Specific experience (e.g., "10 years of experience") is not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly detailed. The text states, "There was a high degree of agreement between the different trained radiologist as demonstrated by statical analysis," suggesting that the ground truth was established through some form of consensus among these multiple radiologists. This could imply a majority vote, averaging, or a formal consensus meeting, but the specific method (e.g., 2+1, 3+1) is not provided.

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

• A MRMC comparative effectiveness study was not specifically described in the provided text. The study primarily focused on validating the standalone performance of the Viz RV/LV algorithm against a human-established ground truth. The role of the device is to "provide the user with annotated images showing ventricular measurements" and is "not intended to be used on a stand-alone basis for clinical decision-making or otherwise preclude clinical assessment of CTPA cases," suggesting it is a human-in-the-loop aid, but the study did not quantify the improvement of human readers with or without the AI assistance.

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

• Yes, a standalone performance study was done. The text explicitly states, "Clinical testing was performed as a study comparing the Viz RV/LV's output to the ground truth as established by trained radiologists." This means the algorithm's raw output was directly compared to the expert ground truth, without human intervention or modification of the algorithm's output.

7. The Type of Ground Truth Used

• Type of Ground Truth: Expert Consensus / Manual Measurements. The ground truth was "established by trained radiologists" and involved "measurements that were obtained manually."

8. The Sample Size for the Training Set

• Training Set Sample Size: Not explicitly stated as a number of cases, but it mentions that "a larger 13 sites used as part of the training data set." The absolute number of cases from these 13 sites is not given.

9. 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 mentions that the "4 clinical sites used in the pivotal study were a subset of a larger 13 sites used as part of the training data set," implying that the training data also came from clinical sources. One can infer that it likely involved similar methods of expert annotation or manual measurement, given the nature of the task and the validation approach. However, specific details are absent.

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Viz SDH is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of standard of care workflow.

Viz SDH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the head for subdural hemorrhage and sends notifications to a neurovascular or neurosurgical specialist that a suspected subdural hemorrhage has been identified and recommends review of those images can be previewed through a mobile application.

Images that are previewed through the mobile application may be compressed and are for informational purposes only and not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests. Viz SDH is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

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

Viz SDH is a combination of software modules that consists of an image analysis software algorithm and mobile application software module. The Viz SDH image analysis software algorithm is an artificial intelligence machine learning (AI/ML) software algorithm that analyzes non-contrast CT images of the head for a subdural hemorrhage. The Viz SDH Image Analysis Algorithm is hosted on Viz.ai's servers and analyzes applicable stroke-protocoled NCCT images of the head that are acquired on CT scanners and are forwarded to Viz.ai servers. Upon detection of a suspected subdural hemorrhage, the Viz SDH Image Analysis Algorithm sends a notification of the suspected finding.

Viz SDH includes a mobile software module that enables the end user to receive and toggle notifications for suspected subdural hemorrhages identified by the Viz SDH Image Analysis Algorithm. The Viz SDH mobile notification software module is implemented into Viz.ai's nondiagnostic DICOM image viewer, Viz VIEW, which displays CT scans that are sent to Viz.ai's servers. When the Viz SDH mobile notification software module is enabled for a user, the user can receive and toggle the notifications for patients with a suspected subdural hemorrhage, view a unique patient list of patients with a suspected subdural hemorrhage, and view the nondiagnostic CT scan of the patient through the Viz VIEW mobile application. Image viewing through the mobile application interface is for non-diagnostic purposes only.

The Viz SDH device's acceptance criteria and performance are detailed in the provided document.

1. Table of Acceptance Criteria and Reported Device Performance

The study also reported an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.96.
The time to notification for SDH was 1.15 ± 0.57 minutes.

2. Sample Size and Data Provenance

• Sample Size for Test Set: 542 non-contrast CT (NCCT) scans (studies).
• Data Provenance: The scans were obtained from three clinical sites in the U.S. The studies included approximately twice as many negative cases as positive cases (66.1% without SDH and 33.9% with SDH).
• Retrospective/Prospective: Not explicitly stated, but the description of "obtained from three clinical sites" and "included in the analysis" typically suggests retrospective data collection for regulatory studies of this nature.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: Trained neuro-radiologists.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for establishing ground truth from the neuro-radiologists. It only mentions that ground truth was "established by trained neuro-radiologists."

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

No MRMC comparative effectiveness study was mentioned where human readers' improvement with AI vs. without AI assistance was evaluated. The performance data presented is for the standalone algorithm; however, there is a comparison of the time to notification with a predicate device (Viz ICH) which was previously shown to be clinically meaningful in reducing notification time compared to standard of care.

6. Standalone Algorithm Performance Study

Yes, a standalone performance study was conducted. The sensitivity, specificity, and AUC values reported are for the Viz SDH algorithm without human-in-the-loop performance.

7. Type of Ground Truth Used

The ground truth was established by expert consensus, specifically by trained neuro-radiologists, who compared the Viz SDH's output to the actual NCCT images.

8. Sample Size for the Training Set

The sample size for the training set is not provided in the document. The performance data section focuses entirely on the test set.

9. How Ground Truth for the Training Set Was Established

The document does not provide information on how the ground truth for the training set was established. It only mentions the approach for the test set.

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Viz ANEURYSM (Viz ANX) is a radiological computer-assisted triage and notification software device for analysis of CT images of the head. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and prioritizing studies with suspected aneurysms during routine patient care.

Viz ANEURYSM uses an artificial intelligence algorithm to analyze images and highlight studies with suspected aneurysms in a standalone application for study list prioritization or triage in parallel to ongoing standard of care. The device generates compressed preview images that are meant for informational purposes only and not intended for diagnostic use. The device does not alter the original medical image and is not intended to be used as a diagnostic device.

Analyzed images are available for review through the standalone application. When viewed through the standalone application the images are for informational purposes only and not for diagnostic use. The results of Viz ANEURYSM, in conjunction with other clinical information and professional judgment, are to be used to assist with triage/prioritization of medical images. Radiologists who read the original medical images are responsible for the diagnostic decision. Viz ANEURYSM is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

Viz ANEURYSM is limited to detecting aneurysms at least 4mm in diameter.

Viz ANEURYSM (Viz ANX) is a radiological computer-assisted triage and notification software device for analysis of CTA images of the head. The software automatically receives and analyzes CT angiogram (CTA) imaging of the head for image features that indicate the presence of an aneurysm using an artificial intelligence algorithm, and prioritizes patient imaging in a standalone application for workflow triage and review by a radiologist in parallel to standard of care image interpretation.

Viz ANEURYSM is a combination of software modules that consists of an image analysis software algorithm and mobile application software module. The Viz ANEURYSM Image Analysis Algorithm is an artificial intelligence machine (Al/ML) software algorithm that analyzes CTA images of the head for an aneurysm. Images acquired during patient care are forwarded to Viz.ai's Backend server where they are analyzed by the Viz ANEURYSM artificial intelligence algorithm for an aneurysm.

Viz ANEURYSM includes a mobile software module that enables the end user to view cases identified by the Viz ANEURYSM algorithm to contain a suspected aneurysm. The Viz ANEURYSM mobile software module is implemented into Viz.ai's generic non-diagnostic DICOM image mobile viewing application, Viz VIEW, which displays CTA scans that are sent to the Backend server. When the Viz ANEURYSM mobile software module is enabled, studies determined by the algorithm to contain a suspected aneurysm are highlighted in the standalone mobile application for study list prioritization or triage in parallel to ongoing standard of care. The user can also view compressed preview images and a non-diagnostic preview of the analyzed CTA scan of the patient through the mobile application.

The preview images and additional patient imaging available through the standalone mobile application are meant for informational purposes only and not intended for diagnostic use. The results of Viz ANEURYSM, in conjunction with other clinical information and professional judgment, are to be used to assist with triage/prioritization of medical images. Radiologists who read the original medical images are responsible for the diagnostic decision.

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 & Reported Device Performance

2. Sample Size and Data Provenance

• Test Set Sample Size: 315 scans
  • 67 positive scans (21.3%)
  • 248 negative scans (78.7%)
• Data Provenance: Not explicitly stated regarding country of origin or whether it was retrospective or prospective.

3. Number of Experts and Qualifications for Ground Truth

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

4. Adjudication Method for the Test Set

• The text states ground truth was "established by trained neuro-radiologists." It does not specify a detailed adjudication method (e.g., 2+1, 3+1 consensus). It implies a consensus, but the process is not detailed.

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

• No MRMC comparative effectiveness study was done to show how much human readers improve with AI vs. without AI assistance.
• The study primarily focuses on the standalone performance of the AI algorithm.
• However, a time-to-notification analysis was performed, showing that the Viz ANEURYSM time-to-notification was faster than the standard of care time-to-notification for all 20 cases used in the time analysis.
  • Average time to notification (device): 219.8 seconds (3.67 minutes)
  • Median time to notification (device): 203.44 seconds (3.39 minutes)
  • Average time to notification (Standard of Care): 2613.0 seconds (43.6 minutes)
  • Median time to notification (Standard of Care): 1620.0 seconds (27.0 minutes)

6. Standalone (Algorithm Only) Performance Study

• Yes, a standalone performance study was done. The reported sensitivity, specificity, and AUC are all metrics of the algorithm's performance independent of human-in-the-loop assistance.

7. Type of Ground Truth Used

• Expert Consensus: Ground truth was established by "trained neuro-radiologists."

8. Sample Size for the Training Set

• The sample size for the training set is not provided in the document. The document only references the "image database" used for analysis, which appears to be the test set.

9. How Ground Truth for the Training Set Was Established

• The document does not provide information on how the ground truth for the training set was established. It only describes the ground truth establishment for the test set used to demonstrate performance.

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Viz ICH is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of standard of care workflow.

Viz ICH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the brain acquired in the acute setting, and sends notifications to a neurovascular or neurosurgical specialist that a suspected intracranial hemorrhage has been identified and recommends review of those images can be previewed through a mobile application.

lmages that are previewed through the mobile application may be compressed and are for informational purposes only and not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests. Viz ICH is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

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

Viz ICH is a combination of software modules that consists of an image analysis software algorithm and mobile application software module. The Viz ICH image analysis software algorithm is an artificial intelligence machine (AI/ML) software algorithm that analyzes non-contract CT images of the head for an intracranial hemorrhage. The Viz ICH Image Analysis Algorithm is hosted on Viz.ai's servers and analyzes applicable stroke-protocoled NCCT images of the head that are acquired on CT scanners and are forwarded to Viz.ai servers. Upon detection of a suspected intracranial hemorrhage, the Viz ICH Image Analysis Algorithm sends a notification of the suspected finding.

Viz ICH includes a mobile software module that enables the end user to receive and toggle notifications for suspected intracranial hemorrhages identified by the Viz ICH Image Analysis Algorithm. The Viz ICH mobile notification software module is implemented into Viz.ai's non-diagnostic DICOM image viewer, Viz VIEW, which displays CT scans that are sent to Viz.ai's servers. When the Viz ICH mobile notification software module is enabled for a user, the user can receive and toggle the notifications for patients with a suspected intracranial hemorrhage, view a unique patient list of patients with a suspected intracranial hemorrhage, and view the non-diagnostic CT scan of the patient through the Viz VIEW mobile application. Image viewing through the mobile application interface is for nondiagnostic purposes only.

Viz ICH Acceptance Criteria and Performance Study

This document describes the acceptance criteria for the Viz ICH device and the study conducted to demonstrate its performance.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance

• Test Set Sample Size: 387 Non-contrast Computed Tomography (NCCT) scans (studies).
• Data Provenance: Two clinical sites in the U.S. (Retrospective, as the study was to evaluate the performance of an already developed algorithm on existing data).

3. Number, Qualifications, and Adjudication of Experts for Ground Truth

• Number of Experts: Not explicitly stated, but referred to as "trained neuro-radiologists."
• Qualifications of Experts: "Trained neuro-radiologists." Specific years of experience are not mentioned.
• Adjudication Method: Not explicitly stated, but "ground truth as established by trained neuro-radiologists" implies a consensus or majority vote among multiple experts.

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

• Was an MRMC study done? No. The study focuses on the standalone performance of the AI algorithm.
• Effect size of human readers with AI vs. without AI assistance: Not applicable as no MRMC study was conducted. However, the study does mention the average time to alert:
  • Viz ICH: 0.49 ± 0.08 minutes
  • Standard of Care: 18.3 ± 14.2 minutes
    This reduction in alert time implies a significant improvement in the speed of notification for human specialists.

5. Standalone Performance

• Was a standalone (algorithm only) performance study done? Yes. The provided performance data (sensitivity, specificity, AUC) directly reflects the algorithm's performance compared to ground truth.

6. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus, specifically "ground truth as established by trained neuro-radiologists" for the presence or absence of intracranial hemorrhage.

7. Sample Size for Training Set

• Sample Size for Training Set: Not explicitly stated in the provided document.

8. How Ground Truth for Training Set Was Established

• How Ground Truth for Training Set Was Established: Not explicitly stated in the provided document. It is generally assumed that the training data ground truth would also be established by clinical experts, similar to the test set.

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Viz ICH is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of care workflow.

Viz ICH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the brain acquired in the acute setting, and sends notifications to a neurovascular or neurosurgical specialist that a suspected intracranial hemorrhage has been identified and recommends review of those images. Images can be previewed through a mobile application.

lmages that are previewed through the mobile application may be compressed and are for informational purposes only and not intended for diagnostic use beyond notification. Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests. Viz ICH is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

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

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

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

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

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

1. Table of Acceptance Criteria and Reported Device Performance

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

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

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

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

4. Adjudication method for the test set

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

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

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

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

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

7. The type of ground truth used

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

8. The sample size for the training set

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

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

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

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Viz CTP is an image processing software package to be used by trained professionals, including but not limited to physicians and medical technicians. The software runs on a standard "off-the-shelf" computer or a virtual platform, such as VMware, and can be used to perform image processing, analysis, and communication of computed tomography (CT) perfusion scans of the brain. Data and images are acquired through DICOM-compliant imaging devices.

Viz CTP provides both analysis and communication capabilities for dynamic imaging datasets that are acquired with CT Perfusion imaging protocols. Analysis includes calculation of parameters related to tissue flow (perfusion) and tissue blood volume. Results of image processing which include CT perfusion parameter maps generated from a raw CTP scan are exported in the standard DICOM format and may be viewed on existing radiological imaging viewers.

Viz CTP is a standalone software package that is comprised of several modules including DICOM receiving and sending modules, a study processor, image analysis algorithm, as well as software system components including a DICOM storage database and system health-monitoring. Viz CTP allows for bi-directional communication of data and may be implemented to allow a DICOM-compliant device to send files directly from the imaging modality, through a node on a local network, or from a PACS server. The device is designed to automatically receive, identify, extract, and analyze a CTP study of the head embedded in DICOM image data. The software outputs parametric maps related to tissue blood flow (perfusion) and tissue blood volume that are written back to the source DICOM. Following such analysis, the software automatically sends the results of analysis to a preconfigured destination point. The software allows for repeated use and continuous processing of data and can be deployed on a supportive infrastructure that meets the minimum system requirements.

Viz CTP image analysis includes calculation of the following perfusion related parameters:

• Cerebral Blood Flow (CBF)
• Cerebral Blood Volume (CBV)
• Mean Transit Time (MTT)
• Residue function time-to-peak (TMax)
• Arterial Input Function (AIF)

The primary users of Viz CTP are medical imaging professionals who analyze dynamic CT perfusion studies. The results of image analysis produced by Viz CTP should be viewed through appropriate diagnostic viewers when used in clinical decision making.

Here's a summary of the acceptance criteria and the study that proves the Viz CTP device meets those criteria, 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

The study used a "commercially available simulated dataset (digital phantom) generated by simulating tracer kinetic theory."

• Sample Size: Not explicitly stated as a number of cases or images.
• Data Provenance: This was a simulated dataset, not derived from real patient scans. It was designed to include a "wide range of clinically relevant values of perfusion parameters as ground truth."

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

• Number of Experts: Not applicable. The ground truth was established by the design of the simulated digital phantom itself, which was "generated by simulating tracer kinetic theory" and included "a wide range of clinically relevant values of perfusion parameters as ground truth."
• Qualifications of Experts: Not applicable.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. The ground truth was inherent in the simulated dataset, not determined by expert reviewers.

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

• MRMC Study: No, an MRMC comparative effectiveness study involving human readers was not mentioned. The performance study focused on the algorithm's standalone accuracy against a simulated ground truth.
• Effect Size of Human Readers Improve with AI vs without AI Assistance: Not applicable, as no MRMC study was performed.

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

• Standalone Study: Yes, a standalone performance study was done. The document states, "Viz.ai Inc. performed software verification and validation testing of the device and additional performance testing on a commercially available simulated dataset (digital phantom)..." and "Correlations between the output of the Viz CTP device and the ground truth values were calculated, and compared to published correlations between the ground truth and the outputs of 7 other commercially available and academic CTP post-processing software." This evaluates the algorithm's performance directly.

7. The Type of Ground Truth Used

• Type of Ground Truth: The ground truth was based on "commercially available simulated dataset (digital phantom) generated by simulating tracer kinetic theory," which includes "a wide range of clinically relevant values of perfusion parameters as ground truth." This is a simulated, theoretical ground truth.

8. The Sample Size for the Training Set

• Sample Size for Training Set: The document does not specify the sample size or details of any training set used for the algorithm development. It focuses solely on the performance testing.

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

• Ground Truth for Training Set: Not mentioned in the provided text.

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