Viz SDH

K220439 · Viz. Ai, Inc. · QAS · Jul 25, 2022 · Radiology

Device Facts

• Record ID: K220439
• Device Name: Viz SDH
• Applicant: Viz. Ai, Inc.
• Product Code: QAS · Radiology
• Decision Date: Jul 25, 2022
• Decision: SESE
• Submission Type: Traditional
• Regulation: 21 CFR 892.2080
• Device Class: Class 2
• Attributes: AI/ML, Software as a Medical Device

Intended Use

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. 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 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.

Device Story

Viz SDH is a software-only, parallel workflow tool for hospital networks. It receives non-contrast CT (NCCT) head images from DICOM-compliant scanners. An AI/ML algorithm analyzes these images for subdural hemorrhage (SDH). Upon detection, the system sends a push notification to a neurovascular or neurosurgical specialist via a mobile application (Viz VIEW). The mobile app allows specialists to preview non-diagnostic, potentially compressed images and view patient lists. The device does not alter original images or provide diagnostic markings. It serves as a triage tool to alert specialists early, facilitating faster clinical review. Specialists must view non-compressed images on a diagnostic viewer and perform a full patient evaluation before making care decisions. The device benefits patients by potentially reducing time to specialist notification and intervention.

Clinical Evidence

Bench testing using 542 retrospective NCCT scans from three U.S. clinical sites. Sensitivity 94% (90-97% CI), specificity 92% (89-95% CI), and AUC 0.96. Performance goals of 80% met. Subgroup analysis performed for age, gender, scanner manufacturer/model, slice thickness, SDH volume, and location. Average time to notification was 1.15 ± 0.57 minutes, comparable to the predicate device.

Technological Characteristics

Software-only, cloud-hosted AI/ML algorithm. Inputs: DICOM NCCT head images. Outputs: Push notifications to mobile app. Connectivity: Networked via DICOM. Mobile app (Viz VIEW) provides non-diagnostic image preview. No image marking. Algorithm class: AI/ML.

Indications for Use

Indicated for hospital networks and trained clinicians (neurovascular or neurosurgical specialists) to identify and communicate suspected subdural hemorrhage (SDH) in non-contrast head CT images. Not for diagnostic use; intended for notification and triage parallel to standard of care.

Regulatory Classification

Identification

Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.

Special Controls

Radiological computer aided triage and notification software must comply with the following special controls: 1. Design verification and validation must include: i. A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations. ii. A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (e.g., improved time to review of prioritized images for pre-specified clinicians). iii. Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment. iv. Standalone performance testing protocols and results of the device. v. Appropriate software documentation (e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results). 2. Labeling must include the following: i. A detailed description of the patient population for which the device is indicated for use. ii. A detailed description of the intended user and user training that addresses appropriate use protocols for the device. iii. Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (e.g., poor image quality for certain subpopulations), as applicable. iv. A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images. v. Device operating instructions. vi. A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.

*Classification.* Class II (special controls). The special controls for this device are:(1) Design verification and validation must include: (i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations. (ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage ( *e.g.,* improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts ( *e.g.,* subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device. (v) Appropriate software documentation ( *e.g.,* device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following: (i) A detailed description of the patient population for which the device is indicated for use; (ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device; (iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level ( *e.g.,* poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images; (v) Device operating instructions; and (vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness ( *e.g.,* improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (*e.g.,* confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.

Predicate Devices

• Viz ICH (K210209)

Submission Summary (Full Text)

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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is in blue and includes the letters "FDA" followed by the words "U.S. Food & Drug Administration".

Viz.ai, Inc. Vi Ma Regulatory Affairs Specialist 201 Mission St, 12th Floor, SAN FRANCISCO CA 94105 USA

# Re: K220439

July 25, 2022

Trade/Device Name: Viz SDH Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QAS Dated: February 15, 2022 Received: February 16, 2022

Dear Vi Ma:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see

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https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email ([e-mail]) or phone [phone] or 301-796-7100).

Sincerely,

Jessica Lamb. Ph.D.

Assistant Director Imaging Software Team DHT 8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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510(k) Number (if known)

K220439

#### Device Name

#### Viz SDH

Indications for Use (Describe)

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 bevond 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. 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 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.

Type of Use (Select one or both, as applicable)

区 Prescription Use (Part 21 CFR 801 Subpart D)

□ Over-The-Counter Use (21 CFR 801 Subpart C)

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## 510(k) Summary Viz.ai, Inc.'s Viz SDH

| Applicant Name: | Viz.ai, Inc.<br>201 Mission St, 12th Floor<br>San Francisco, CA 94105 |
|-----------------|----------------------------------------------------------------------------------------------------------------------------|
| Contact Person: | Vi Ma<br>Regulatory Affairs Specialist<br>201 Mission Street, 12th Floor<br>San Francisco, CA 94105<br>Tel. [phone] |

[e-mail]

Date Prepared: June 23, 2022

Device Name and Classification

| Name of Device: | Viz SDH |
|-----------------------|-----------------------------------------------------------------|
| Common or Usual Name: | Radiological Computer-Assisted Triage and Notification Software |
| Classification Panel: | Radiology |
| Regulation No: | 21 C.F.R. § 892.2080 |
| Regulatory Class: | Class II |
| Product Code: | QAS |

### Predicate Device

| Manufacturer | Device Name | Application No. |
|--------------|-------------|-----------------|
| Viz.ai, Inc. | Viz ICH | K210209 |

#### Device Description

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 imaqe 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

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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 toqgle 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.

### Intended Use / Indications for Use

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.

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 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.

### Summary of Technological Characteristics

The subject device. Viz SDH, is substantially equivalent to the predicate device, the Viz ICH device (K210209). In comparing the technological characteristics, both the subject and predicate devices use an artificial intelligence algorithm and mobile notification software to identify and notify specialists of patients with a suspected hemorrhage. Where the subject and predicate differ is that the software algorithm for the subject device is designed to detect subdural hemorrhages, while the predicate device detects a variety of intracranial hemorrhages, including subdural hemorrhages.

Both the subject and the predicate devices include mobile application software that allows a user to receive push notifications for patients identified with a suspected hemorrhage by their respective software algorithms. Both devices interface with a non-diagnostic mobile DICOM

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image viewer to allow the specialist user to preview non-diagnostic images and view patient details associated with a series.

When used with the Viz VIEW mobile application software, the Viz SDH mobile notification software module is subject to the same non-diagnostic viewing limitations as the predicate and has the same non-diagnostic warning on the image viewing screen as the predicate.

| | Subject Device | Predicate Device |
|---------------------------------|-------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|
| | Viz SDH | Viz ICH |
| Application No. | K220439 | K210209 |
| Product Code | QAS | QAS |
| Regulation No. | 21 C.F.R. § 892.2080 | 21 C.F.R. § 892.2080 |
| Anatomical<br>Region | Head | Head |
| Diagnostic<br>Application | Notification-only | Notification-only |
| Notification/<br>Prioritization | Yes | Yes |
| Intended User | Neurovascular or<br>Neurosurgical Specialist | Neurovascular or Neurosurgical<br>Specialist |
| DICOM<br>Compatible | Yes | Yes |
| Data Acquisition | Acquires medical image data<br>from DICOM compliant<br>imaging devices and<br>modalities. | Acquires medical image data from<br>DICOM compliant imaging devices<br>and modalities. |
| Supported<br>Imaging Modality | Computed Tomography, non-<br>contrast (NCCT) | Computed Tomography, non-<br>contrast (NCCT) |
| Alteration of<br>Original Image | No | No |
| Results of Image<br>Analysis | Internal, no image marking | Internal, no image marking |
| Preview Images | Initial assessment; non-<br>diagnostic purposes | Initial assessment; non-diagnostic<br>purposes |
| View DICOM<br>Data | DICOM Information about the<br>patient, study and current<br>image. | DICOM Information about the<br>patient, study and current image. |
| Time to<br>Notification | $1.15±0.57$ minutes | $1.15±0.83$ minutes |

### Performance Data

542 Non-contrast Computed Tomography (NCCT) scans (studies) were obtained from three clinical sites in the U.S. There were approximately twice the number of negative than positive cases (66.1% images without SDH and 33.9.% with SDH, respectively) included in the analysis.

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Sensitivity and specificity were calculated in the image database, comparing the Viz SDH's output to ground truth as established by trained neuro-radiologists. Sensitivity and specificity were 94% (90% - 97%) and 92% (89% - 95%), respectively. Because the lower bound of each confidence interval exceeded 80%, the study met the pre-specified performance goals of 80% for sensitivity and specificity.

In addition, the area under the receiver operating characteristic curve (AUC) was 0.96, demonstrating the clinical utility and potential benefits of the classifier based on the imaging study results.

Image /page/6/Figure/2 description: The image is a Receiver Operating Characteristic (ROC) curve for Viz SDH. The x-axis represents the false positive rate, and the y-axis represents the true positive rate. The ROC curve has an area of 0.96, and the threshold is 0.0317 mL. A dashed line is shown as a reference for random chance.

In addition, the time to notification for SDH was compared to the time to notification for predicate device, Viz ICH. The Viz ICH time to notification was compared to the standard of care and was clinically meaningful from the perspective of effective triage (i.e., showing a reduction in the time to notification when compared to the standard of care).

Since SDH is a specific type of intracranial hemorrhage, a comparison of the Viz SDH time to notification with respect to the predicate device, Viz ICH (which was shown to support effective triage for hemorrhage) provides a comparable insight in the ability of Viz SDH to provide effective triage.

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In the study, the average time to alerting a specialist was 69.1±34.3 sec (1.15±0.57 minutes), which is comparable to the average time to notification seen in the Viz ICH of 1.15±0.83 minutes. This data generally demonstrated that specialists could become involved in the clinical workflow early with notifications from the Viz SDH software.

| Device Performance by Clinical Site | | |
|-------------------------------------|----------------------|----------------------|
| Clinical Site | Sensitivity [95% CI] | Specificity [95% CI] |
| Site 001 | 0.93 [0.83, 0.98] | 0.91 [0.86, 0.95] |
| Site 002 | 0.93 [0.81,0.99] | 0.95[0.87,0.99] |
| Site 003 | 0.95 [0.89, 0.99] | 0.92 [0.85, 0.96] |

Stratification of Device Performance

| Device Performance by Age | | |
|---------------------------|----------------------|----------------------|
| Age Range (Years) | Sensitivity [95% CI] | Specificity [95% CI] |
| <50 | 1.0 [0.54, 1.0] | 0.95 [0.84, 0.99] |
| 50-70 | 1.0 [0.88, 1.0] | 0.92 [0.82, 0.97] |
| 70< | 0.91 [0.82, 0.96] | 0.91 [0.81, 0.97] |

| Device Performance by Gender | | | |
|------------------------------|----------------------|----------------------|--|
| Gender (Years) | Sensitivity [95% CI] | Specificity [95% CI] | |
| Male | 0.97 [0.92, 0.99] | 0.9 [0.84, 0.94] | |
| Female | 0.9 [0.80, 0.96] | 0.94 [0.90, 0.97] | |

| Device Performance by Presence of Subdural Hemorrhage and Other Hemorrhage | | |
|----------------------------------------------------------------------------|-----------------------|----------------------|
| Hemorrhage Subtypes | Sensitivity [95% CI] | Specificity [95% CI] |
| Subdural hemorrhage only | 0.93 ['0.88', '0.97'] | - |
| Subdural hemorrhage and<br>other hemorrhage present | 0.97 ['0.86', '1.00'] | - |
| Other, non-subdural<br>hemorrhage present | - | 1.0 ['0.40', '1.00'] |

| Device Performance by Slice Thickness | | | | |
|---------------------------------------|----------------------|----------------------|--|--|
| Slice Thickness | Sensitivity [95% CI] | Specificity [95% CI] | | |
| 2.5mm ≤ Slice Thickness < 3.5mm | 0.95 [0.89, 0.98] | 0.93 [0.88, 0.96] | | |
| 3.5mm ≤ Slice Thickness ≤ 5.0mm | 0.93 [0.83, 0.98] | 0.91 [0.86, 0.95] | | |

| Device Performance by SDH Thickness | | |
|-------------------------------------|----------------------|--|
| Thickness | Sensitivity [95% Cl] | |
| 3mm ≤ Thickness < 6mm | 0.81 [0.58, 0.95] | |
| 6mm ≤ Thickness < 10mm | 0.91 [0.8, 0.98] | |
| Thickness > 10mm | 0.97 [0.93, 0.99] | |

| Device Performance by SDH Type | |
|--------------------------------|----------------------|
| SDH Type | Sensitivity [95% CI] |

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| Acute | 0.92 [0.85, 0.97] |
|--------------------------|-------------------|
| Non-Acute (Chronic) | 0.93 [0.82, 0.99] |
| Both (Acute and Chronic) | 0.98 [0.88, 1.00] |

| Device Performance by SDH Location | |
|------------------------------------|---------------------------|
| SDH Location | Sensitivity [95% CI] |
| Tentorial | 1.0 CI: [0.79, 1.00] |
| Falcine | 0.87 CI: [0.69, 0.96] |
| Posterior Fossa | 0.33 CI: ["0.01", "0.91"] |

| Device Performance by Scanner Manufacturer | | | |
|--------------------------------------------|----------------------|----------------------|--|
| Manufacturer | Sensitivity [95% CI] | Specificity [95% CI] | |
| General Electric | 0.92 [0.84, 0.97] | 0.92 [0.87, 0.95] | |
| Siemens | 0.94 [0.83, 0.99] | 0.94 [0.87, 0.98] | |
| Toshiba | 0.98 [0.89, 1.0] | 0.92 [0.81, 0.97] | |

| Device Performance by Scanner Manufacturer/Model | | | |
|--------------------------------------------------|-----------------------------|-------------------------|-----------------------|
| Manufacturer | Model | Sensitivity [95% CI] | Specificity [95% CI] |
| GE Medical<br>Systems | BRIGHTSPEED | 1.0 [0.63, 1.0] | 0.82 [0.57, 0.96] |
| | RIGHTSPEED S | 1.0 [0.29, 1.0] | 1.0 [0.29, 1.0] |
| | DISCOVERY 610 | 1.0 [0.69, 1] | 0.94 [0.70, 1] |
| | DISCOVERY CT750 HD | 0.85 [0.65, 0.96] | 0.93 [0.88, 0.97] |
| | LIGHTSPEED VCT | 1.0 [0.48, 1.0] | 0.5 [0.01, 0.99] |
| | LIGHTSPEED16 | 1.0 ['0.03', '1.00'] | 0.5 ['0.01', '0.99'] |
| | OPTIMA CT540 | 1.0 ['0.03', '1.00'] | N/A |
| | OPTIMA CT660 | 1.0 ['0.03', '1.00'] | 1.0 ['0.03', '1.00'] |
| | REVOLUTION CT | 1.0 ['0.74', '1.00'] | 0.67 ['0.09', '0.99'] |
| | REVOLUTION EVO | 0.86 ['0.65', '0.97'] | 0.94 ['0.81', '0.99'] |
| Siemens | EMOTION 16 | N/A | 1.0 [0.03, 1.0] |
| | PERSPECTIVE | 0.94['0.74',<br>'1.00'] | 0.93 ['0.77', '0.99'] |
| | SENSATION 64 | 1.0 [0.03, 1.0] | N/A |
| | SOMATOM DEFINITION<br>AS | 1.0 ['0.29', '1.00'] | 1.0 ['0.59', '1.00'] |
| | SOMATOM DEFINITION<br>AS+ | 0.89 ['0.67', '0.99'] | 0.95 ['0.83', '0.99'] |
| | SOMATOM DEFINITION<br>FLASH | 1.0 ['0.16', '1.00'] | N/A |
| | SOMATOM GO.ALL | 1.0 ['0.29', '1.00'] | 0.88 ['0.47', '1.00'] |
| | SOMATOM PERSPECTIVE | 1.0 [0.03, 1.0] | N/A |
| Toshiba | AQUILION | 1.0 ['0.84', '1.00'] | 0.95 ['0.76', '1.00'] |

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| Device Performance by Scanner Manufacturer/Model | | | |
|--------------------------------------------------|----------------|-----------------------|-----------------------|
| Manufacturer | Model | Sensitivity [95% CI] | Specificity [95% CI] |
| | AQUILION ONE | 1.0 ['0.54', '1.00'] | 1.0 ['0.63', '1.00'] |
| | AQUILION PRIME | 0.95 ['0.75', '1.00'] | 0.87 ['0.69', '0.96'] |

| Device Performance by Subdural Hemorrhage Volume | | |
|--------------------------------------------------|----------------------|--|
| Volume (mL) | Sensitivity [95% CI] | |
| Volume <1 | 0.33 [0.04, 0.78] | |
| 1 <= Volume < 5 | 0.85 [0.65, 0.96] | |
| 5 <= Volume < 10 | 1.0 [0.75, 1.0] | |
| Volume >= 10 | 0.98 [0.94, 1.0] | |

| Device Performance by Scanner Reconstruction Method | | | |
|-----------------------------------------------------|--------------------------|-----------------------|-----------------------|
| Manufacturer | Reconstruction<br>Method | Sensitivity [95% CI] | Specificity [95% CI] |
| GE Medical<br>Systems | SOFT | 0.79 ['0.49', '0.95'] | 1.0 ['0.79', '1.00'] |
| GE Medical<br>Systems | STANDARD | 0.94 ['0.85', '0.98'] | 0.91 ['0.86', '0.95'] |
| GE Medical<br>Systems | STND# | 1.0 ['0.66', '1.00'] | 0.67 ['0.09', '0.99'] |
| Siemens | H30s | 1.0 ['0.40', '1.00'] | 1.0 ['0.40', '1.00'] |
| Siemens | ['Hc40f', '2'] | 1.0 ['0.29', '1.00'] | 0.88 ['0.47', '1.00'] |
| Siemens | ['J30s', '2'] | 0.92 ['0.79', '0.98'] | 0.94 ['0.86', '0.98'] |
| Toshiba | FC26 | 0.96 ['0.81', '1.00'] | 0.9 ['0.76', '0.97'] |
| Toshiba | FC68 | 1.0 ['0.82', '1.00'] | 0.95 ['0.75', '1.00'] |

## Conclusions

Viz SDH is as safe and effective as the predicate device. Viz SDH has essentially the same intended use and similar indications, technological characteristics, and principles of operation as its predicate device. The differences in indications do not alter the intended diagnostic use of the device and do not affect its safety and effectiveness when used as labeled. In addition, the technological differences between Viz SDH and its predicate device raise no new issues of safety or effectiveness. Performance data demonstrate that Viz SDH is as safe and effective as the predicate device, the previously cleared Viz ICH software (K210209). Thus, Viz SDH is substantially equivalent.