(140 days)
Aidoc Medical's BriefCase (K180647)
Not Found
Yes
The document explicitly states that the device uses "artificial intelligence algorithms" and "machine learning algorithms" for image analysis and detection of findings.
No
This device is a triage tool that identifies potential emergent findings in CT images for radiologists, aiding in workflow prioritization. It is not intended for diagnosis and does not provide direct therapy to patients.
No
The "Intended Use / Indications for Use" section explicitly states, "Identification of suspected findings is not for diagnostic use beyond notification" and "NinesAl is intended to be used as a triage tool...and should not be used in-lieu of full patient evaluation or relied upon to make or confirm a diagnosis."
Yes
The device description explicitly states "NinesAl is a software device and does not come into contact with patients." While it includes a "workstation module," this is described as being used to alert the radiologist, implying it's part of the software system's output mechanism rather than a separate hardware component that performs a medical function. The core medical function (image analysis) is performed by the software algorithms.
Based on the provided information, NinesAl is not an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens derived from the human body (like blood, urine, tissue) to provide information for diagnosis, monitoring, or screening.
- NinesAl's Function: NinesAl analyzes medical images (head CT scans) that are already acquired from the patient. It does not analyze biological specimens.
- Intended Use: The intended use clearly states it's a "parallel workflow tool indicated for use by hospital networks and trained clinicians to identify images of specific patients to a radiologist... to aid in prioritizing and performing the radiological review." It explicitly states that the "Identification of suspected findings is not for diagnostic use beyond notification" and "should not be used in-lieu of full patient evaluation or relied upon to make or confirm a diagnosis." This aligns with a medical image analysis tool, not an IVD.
- Device Description: The description reinforces that it's a "software device" that "does not come into contact with patients" and analyzes "radiological studies."
Therefore, NinesAl falls under the category of medical image analysis software, not an In Vitro Diagnostic device.
No
The provided text does not contain any explicit statement that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
NinesAl is a parallel workflow tool indicated for use by hospital networks and trained clinicians to identify images of specific patients to a radiologist, independent of standard of care workflow, to aid in prioritizing and performing the radiological review. NinesAl uses artificial intelligence algorithms to analyze head CT images for findings suggestive of a pre-specified emergent clinical condition.
The software automatically analyzes Digital Imaging and Communications in Medicine (DICOM) images as they arrive in the Picture Archive and Communication System (PACS) using machine learning algorithms. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the software analyzes head CT images of the brain to assess the suspected presence of intracranial hemorrhage and/or mass effect and identifies images with potential emergent findings in a radiologist's worklist.
NinesAl is intended to be used as a triage tool limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm a diagnosis. Additionally, preview images displayed to the radiologist outside of the DICOM viewer are non-diagnostic quality and should only be used for informational purposes.
Product codes (comma separated list FDA assigned to the subject device)
QAS
Device Description
NinesAl notifies a radiologist of the presence of a suspected critical abnormality in a radiological image. The software system is a complete package comprised of image analysis software and a workstation module that is used to alert the radiologist. The image analysis can also be configured to send HL7 messages and DICOM secondary series.
The image analysis uses Artificial Intelligence (AI) technology to analyze non contrast CT Head scans for the presence of Intracranial Hemorrhage and/or Mass Effect. More specifically, the device utilizes two machine learning (ML) algorithms to detect each finding respectively.
NinesAl is a software device and does not come into contact with patients. All radiological studies are still reviewed by trained radiologists. NinesAl is meant to be used as an aid for case prioritization.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Non contrast CT scan of the head
Anatomical Site
Head, brain
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Hospital networks and trained clinicians, Radiologists
Description of the training set, sample size, data source, and annotation protocol
Artificial intelligence algorithms are the technological principle for both the subject and predicate devices. The algorithms are trained using a database of radiological images.
Description of the test set, sample size, data source, and annotation protocol
Head CT studies included in each of the test datasets were obtained from over 20 clinical sites and included a minimum of 3 scanner manufacturers and over 20 scanner models, and also reflected broad patient demographics. Agreement rate between labelers who determined ground truth for the test dataset studies.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Nines performed software verification and validation testing that covers the performance of the algorithms, as well as the performance of the software and its components.
The NinesAl device underwent performance testing to verify the efficacy and safety of the machine learning algorithms. Both of the algorithms used in NinesAl were evaluated independently from each other to allow for an individual understanding of each algorithm's respective performance. Each algorithm was tested in a retrospective performance trial, with the primary endpoints of each trial being the respective sensitivity and specificity of the algorithm in question. Other endpoints included: Positive Predictive Value, Negative Predictive Value, ROC AUC, time savings relative to standard of care, and agreement rate between labelers who determined ground truth for the test dataset studies.
Key results:
Intracranial Hemorrhage: Sensitivity 0.899 [0.837, 0.940], Specificity 0.974 [0.974, 0.992]
Mass Effect: Sensitivity 0.964 [0.916, 0.987], Specificity 0.911 [0.856, 0.948]
Time-savings data for Intracranial Hemorrhage:
Time-to-open-dictation standard of care: Mean 159.4 min, Median 6.0 min
Time-notification of NinesAl: Mean 0.23 min, Median 0.24 min
Time-savings data for Mass Effect:
Time-to-open-dictation standard of care: Mean 28.5 min, Median 7.5 min
Time-notification of NinesAl: Mean 0.23 min, Median 0.24 min
The Intracranial Hemorrhage and Mass Effect algorithms met the performance goal outlined by the predicate device of .80 for both sensitivity and specificity. Additionally, the performance for each algorithm is comparable to the observed performance reported for the predicate device: 93.6% (95% CI: 86.6%-97.6%) sensitivity and 92.3% (95% CI: 85.4%-96.6%) specificity.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Intracranial Hemorrhage:
Sensitivity: 0.899 [0.837, 0.940]
Specificity: 0.974 [0.974, 0.992]
Positive Predictive Value (PPV): Not Directly Stated, but "Other endpoints included: Positive Predictive Value"
Negative Predictive Value (NPV): Not Directly Stated, but "Other endpoints included: Negative Predictive Value"
ROC AUC: Not Directly Stated, but "Other endpoints included: ROC AUC"
Mass Effect:
Sensitivity: 0.964 [0.916, 0.987]
Specificity: 0.911 [0.856, 0.948]
Positive Predictive Value (PPV): Not Directly Stated, but "Other endpoints included: Positive Predictive Value"
Negative Predictive Value (NPV): Not Directly Stated, but "Other endpoints included: Negative Predictive Value"
ROC AUC: Not Directly Stated, but "Other endpoints included: ROC AUC"
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Aidoc Medical's BriefCase (K180647)
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 892.2080 Radiological computer aided triage and notification software.
(a)
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.(b)
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.
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April 21, 2020
Image /page/0/Picture/1 description: The image contains the logos of the Department of Health & Human Services and the Food and Drug Administration (FDA). The Department of Health & Human Services logo is on the left, featuring a stylized depiction of a human figure. To the right is the FDA logo, with the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG" and "ADMINISTRATION" in blue text.
Nines, Inc. % John J. Smith, M.D., J.D. Regulatory Counsel Hogan Lovells US LLP 555 13th Street. NW WASHINGTON DC 20004
Re: K193351
Trade/Device Name: NinesAI Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QAS Dated: March 30, 2020 Received: March 30, 2020
Dear Dr. Smith:
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 https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting
1
combination-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 (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
510(k) Number
Device Name
NinesAl
Indications for Use (Describe)
NinesAl is a parallel workflow tool indicated for use by hospital networks and trained clinicians to identify images of specific patients to a radiologist, independent of care workflow, to aid in prioritizing and performing the radiological review. Nines Al uses artificial intelligence algorithms to analyze head CT images for findings suggestive of a pre-specified emergent clinical condition.
The software automatically analyzes Digital Imaging and Communications in Medicine (DICOM) images as they arive in the Picture Archive and Communication System (PACS) using machine learning algorithms. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the software analyzes head CT images of the brain to assess the suspected presence of intracranial hemorrhage and/or mass effect and identifies images with potential emergent findings in a radiologist's worklist.
NinesAl is intended to be used as a triage tool limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm a diagnosis. Additionally, preview images displayed to the radiologist outside of the DICOM viewer are non-diagnostic quality and should only be used for informational purposes.
| Type of Use (Select one or both, as applicable) | |||
|---|---|---|---|
| X Prescription Use (Part 21 CFR 801 Subpart D) | Over-The-Counter Use (21 CFR 801 Subpart C) | ||
| CONTINUE ON A SEPARATE PAGE IF NEEDED. |
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510(k) SUMMARY Nines, Inc.'s NinesAl K193351
Submitter:
Nines, Inc. 329 Alma Street Palo Alto, CA 94301
Contact Person:
Dr. Russell Stewart Phone: 650 924 6159 russell@ninesai.com
Date Prepared: April 17, 2020
Name of Device: NinesAl
Classification Name: Radiological Computer-Assisted Triage and Notification Software
Regulatory Class: Class II
Product Code: QAS
Predicate Device: Aidoc Medical's BriefCase (K180647)
Device Description
NinesAl notifies a radiologist of the presence of a suspected critical abnormality in a radiological image. The software system is a complete package comprised of image analysis software and a workstation module that is used to alert the radiologist. The image analysis can also be configured to send HL7 messages and DICOM secondary series.
The image analysis uses Artificial Intelligence (AI) technology to analyze non contrast CT Head scans for the presence of Intracranial Hemorrhage and/or Mass Effect. More specifically, the device utilizes two machine learning (ML) algorithms to detect each finding respectively.
NinesAl is a software device and does not come into contact with patients. All radiological studies are still reviewed by trained radiologists. NinesAl is meant to be used as an aid for case prioritization.
4
Intended Use / Indications for Use
NinesAl is a parallel workflow tool indicated for use by hospital networks and trained clinicians to identify images of specific patients to a radiologist, independent of standard of care workflow, to aid in prioritizing and performing the radiological review. NinesAl uses artificial intelligence algorithms to analyze head CT images for findings suggestive of a pre-specified emergent clinical condition.
The software automatically analyzes Digital Imaging and Communications in Medicine (DICOM) images as they arrive in the Picture Archive and Communication System (PACS) using machine learning algorithms. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the software analyzes head CT images of the brain to assess the suspected presence of intracranial hemorrhage and/or mass effect and identifies images with potential emergent findings in a radiologist's worklist.
NinesAl is intended to be used as a triage tool limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm a diagnosis. Additionally, preview images displayed to the radiologist outside of the DICOM viewer are non-diagnostic quality and should only be used for informational purposes.
Summary of Technological Characteristics
The NinesAl indications for use differ slightly from the predicate, but the minor differences do not neqatively impact the safety and effectiveness of the subject device. In sum, the predicate is indicated for use to analyze images for the presence of ICH. NinesAl is indicated for use to analyze ICH, as well as Mass Effect. The additional potential finding of Mass Effect can be seen concomitantly with ICH cases and the detection of such potential pathology is supported by performance testing. All potential findings covered by the predicate and NinesAl are emergent findings in the head.
Artificial intelligence algorithms are the technological principle for both the subject and predicate devices. The algorithms are trained using a database of radiological images. At a high level, the subject and predicate devices are based on the following same technological elements:
- · Artificial Intelligence Algorithm(s);
- · Notification Technology.
A table comparing the key features of the subject and predicate devices is provided below.
| NinesAI (K193351) | AIDoc's BriefCase (K180647) | |
|---|---|---|
| Indications for Use | NinesAl is a parallel workflow tool | |
| indicated for use by hospital networks | ||
| and trained clinicians to identify | ||
| images of specific patients to a | ||
| radiologist, independent of standard | ||
| of care workflow, to aid in prioritizing | ||
| and performing the radiological | ||
| review. NinesAl uses artificial | ||
| intelligence algorithms to analyze | ||
| head CT images for findings | BriefCase is a radiological computer | |
| aided triage and notification software | ||
| indicated for use in the analysis of | ||
| non-enhanced head CT images. | ||
| The device is intended to assist | ||
| hospital networks and trained | ||
| radiologists in workflow triage by | ||
| flagging and communication of | ||
| suspected positive findings of | ||
| pathologies in head CT images. | ||
| NinesAI (K193351) | AIDoc's BriefCase (K180647) | |
| suggestive of a pre-specified | ||
| emergent clinical condition. | namely Intracranial Hemorrhage | |
| (ICH). | ||
| BriefCase uses an artificial | ||
| The software automatically analyzes | ||
| Digital Imaging and Communications | ||
| in Medicine (DICOM) images as they | ||
| arrive in the Picture Archive and | ||
| Communication System (PACS) | ||
| using machine learning algorithms. | ||
| Identification of suspected findings is | ||
| not for diagnostic use beyond | ||
| notification. Specifically, the software | ||
| analyzes head CT images of the | ||
| brain to assess the suspected | ||
| presence of intracranial hemorrhage | ||
| and/or mass effect and identifies | ||
| images with potential emergent | ||
| findings in a radiologist's worklist. | ||
| NinesAl is intended to be used as a | ||
| triage tool limited to analysis of | ||
| imaging data and should not be used | ||
| in-lieu of full patient evaluation or | ||
| relied upon to make or confirm a | ||
| diagnosis. Additionally, preview | ||
| images displayed to the radiologist | ||
| outside of the DICOM viewer are non- | ||
| diagnostic quality and should only be | ||
| used for informational purposes. | intelligence algorithm to analyze | |
| images and highlight cases with | ||
| detected ICH on a standalone | ||
| desktop application in parallel to the | ||
| ongoing standard of care image | ||
| interpretation. The user is presented | ||
| with notifications for cases with | ||
| suspected ICH findings. Notifications | ||
| include compressed preview images | ||
| that are meant for informational | ||
| purposes only and not intended for | ||
| diagnostic use beyond notification. | ||
| The device does not alter the original | ||
| medical image and is not intended to | ||
| be used as a diagnostic device. | ||
| The results of BriefCase are intended | ||
| to be used in conjunction with other | ||
| patient information and based on | ||
| professional judgment, to assist with | ||
| triage/prioritization of medical images. | ||
| Notified clinicians are responsible for | ||
| viewing full images per the standard | ||
| of care. | ||
| User Population | used for informational purposes. | |
| Radiologists | Radiologists | |
| Technological | ||
| Characteristics | Artificial Intelligence algorithms | |
| detecting emergent findings sending | ||
| notifications to the workstation. | Artificial Intelligence algorithms | |
| detecting emergent findings sending | ||
| notifications to the workstation. | ||
| Components | - Artificial Intelligence | |
| Algorithm |
- Notification Technology | - Artificial Intelligence
Algorithm - Notification Technology |
| Anatomical Region
of Interest | Head | Head |
| Findings Covered | Intracranial Hemorrhage and Mass
Effect | Intracranial Hemorrhage |
| Data Acquisition
Protocol | Non contrast CT scan of the head | Non contrast CT scan of the head or
neck |
| View DICOM Data | DICOM information about the patient,
study and current image | DICOM information about the patient,
study and current image |
| Preview Images | Presentation of notification and
preview of the study for initial
assessment not meant for diagnostic
purposes. This is done via desktop
notification or via DICOM series.
The device operates in parallel with | Presentation of notification and
preview of the study for initial
assessment not meant for diagnostic
purposes. This is done via desktop
notification.
The device operates in parallel with |
| | NinesAI (K193351) | AIDoc's BriefCase (K180647) |
| | the default option for all cases | the default option for all cases |
| Alteration of
Original Image | No | No |
| Removal of Cases
from Worklist
Queue | No | No |
| Triage Notification
Types | Workstation Application Notification,
HL7 | Workstation Application Notification |
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Performance Testing
Nines performed software verification and validation testing that covers the performance of the algorithms, as well as the performance of the software and its components. In all instances, NinesAl functioned as intended and expected.
The NinesAl device underwent performance testing to verify the efficacy and safety of the machine learning algorithms. Both of the algorithms used in NinesAl were evaluated independently from each other to allow for an individual understanding of each algorithm's respective performance. Each algorithm was tested in a retrospective performance trial, with the primary endpoints of each trial being the respective sensitivity and specificity of the algorithm in question. Other endpoints included: Positive Predictive Value, Negative Predictive Value, ROC AUC, time savings relative to standard of care, and agreement rate between labelers who determined ground truth for the test dataset studies. Head CT studies included in each of the test datasets were obtained from over 20 clinical sites and included a minimum of 3 scanner manufacturers and over 20 scanner models, and also reflected broad patient demographics.
The primary endpoints for each algorithm are listed below:
| Finding | Sensitivity
[95% confidence
intervals] | Specificity [95% confidence
intervals] |
|-------------------------|----------------------------------------------|-------------------------------------------|
| Intracranial Hemorrhage | 0.899
[0.837, 0.940] | 0.974
[0.974, 0.992] |
| Mass Effect | 0.964
[0.916, 0.987] | 0.911
[0.856, 0.948] |
A time benefit analysis was performed for each algorithm and showed a time-to-notification that is faster than the similar standard of care metric of time-to-open-dictation. These results are summarized below for each algorithm.
| The time-savings data for Intracranial Hemorrhage is listed below: | |||
|---|---|---|---|
| -------------------------------------------------------------------- | -- | -- | -- |
| Metric | Mean (min) | Median (min) |
|---|---|---|
| Time-to-open-dictation | ||
| standard of care | 159.4 [67.07, 251.7] | 6.0 |
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| Metric | Mean (min) | Median (min) |
|---|---|---|
| Time-notification of | ||
| NinesAl | 0.23 [0.23, 0.24] | 0.24 |
The time-savings data for Mass Effect is listed below:
| Metric | Mean (min) | Median (min) |
|---|---|---|
| Time-to-open-dictation | ||
| standard of care | 28.5 [14.1, 42.8] | 7.5 |
| Time-notification of | ||
| NinesAl | 0.23 [0.23, 0.24] | 0.24 |
The Intracranial Hemorrhage and Mass Effect algorithms met the performance goal outlined by the predicate device of .80 for both sensitivity and specificity. Additionally, the performance for each algorithm is comparable to the observed performance reported for the predicate device: 93.6% (95% CI: 86.6%-97.6%) sensitivity and 92.3% (95% CI: 85.4%-96.6%) specificity.
Based on the clinical performance as documented in the pivotal clinical study, the subject software has a safety and effectiveness profile that is similar to the predicate device.
Conclusions
NinesAl has the same intended uses and similar indications, technological characteristics, and principles of operation as its predicate device. The minor differences in indication do not alter the intended use of the device and do not affect its safety and effectiveness when used as labeled. In addition, the minor technological differences between the NinesAl and its predicate devices raise no new issues of safety or effectiveness. Performance data demonstrate that NinesAl performs as intended. Thus, NinesAl is substantially equivalent.