(114 days)
Not Found
Yes
The device description explicitly states that it uses an "artificial intelligence algorithm" and a "deep learning algorithm".
No
This device is a diagnostic/triage tool, not a therapeutic one. It analyzes images to flag potential issues for review by trained professionals but does not provide any treatment or intervention.
No.
The text explicitly states, "the device does not alter the original medical image, does not remove cases from queue, and is not intended to be used as a diagnostic device."
Yes
The device is described as a "software device" and a "computer program" that runs on an operating system and can be deployed on an onsite server. It processes existing medical images and provides a worklist and notifications, without mentioning any proprietary hardware components being part of the device itself.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections. They are used to provide information for diagnosis, monitoring, or screening.
- InferRead CT Stroke.AI's Function: InferRead CT Stroke.AI analyzes medical images (Non-Enhanced Head CT images) to identify potential findings (intracranial hemorrhage). It does not analyze biological samples from the patient.
- Intended Use: The intended use clearly states it's for "radiological computer aided triage and notification software for use in the analysis of Non-Enhanced Head CT images." This is image analysis, not in vitro testing.
- Device Description: The description details software processing of DICOM images, not laboratory analysis of biological specimens.
Therefore, InferRead CT Stroke.AI falls under the category of medical imaging software or a medical device that processes medical images, not an In Vitro Diagnostic device.
No
The provided text does not contain any explicit statements indicating that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The "Control Plan Authorized (PCCP) and relevant text" section explicitly states "Not Found".
Intended Use / Indications for Use
InferRead CT Stroke.AI is a radiological computer aided triage and notification software 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 suspected positive findings of intracranial hemorrhage (ICH).
InferRead CT Stroke.AI uses an artificial 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 a worklist with marked cases of suspected ICH findings. The device does not alter the original medical image, does not remove cases from queue, and is not intended to be used as a diagnostic device. If the clinician does not view the case, or if a case is not flagged, cases remain to be processed per the standard of care.
The results of InferRead CT Stroke.AI 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.
Product codes
QAS
Device Description
InferRead CT Stroke.AI is a radiological computer-assisted triage and notification software device. The software device is a computer program with a deep learning algorithm running on Ubuntu operating system. The device can be deployed as an onsite server in the hospital and the user interacts with the software from a client workstation. The device can be broken down into 4 modules, the NeoViewer, Docking Toolbox, RePACS, and DLServer.
The Docking Toolbox module receives DICOM series and inspects the series against a list of requirements. Series that pass the requirements are sent into the system for prediction for intracranial hemorrhage. Series are processed in a first-out order. When hemorrhage is detected, the system marks the case in the work list prompting the user to conduct preemptive triage and prioritization.
When the user refreshes the page, cases with suspected findings will be marked with an indicator. Cases are identified, such as by Name and Patient ID. The user may filter and sort by suspected ICH and identify the case. A preview is available but is not intended for primary diagnosis and a radiologist must review the case per their standard process. The suspected cases assist in triaging intracranial hemorrhage cases sooner than standard of care practice alone.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Mentions artificial intelligence algorithm and deep learning algorithm.
Input Imaging Modality
Non-Enhanced Head CT images
Anatomical Site
Head
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Hospital networks and trained radiologists.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
A total of 369 non-contrast brain CT scans (studies) were obtained from three hospitals in the U.S. There were approximately equal numbers of positive and negative cases (51.5% of images with ICH and 48.5% without ICH, respectively) included in the analysis. The reference standard for ground truth was established by trained neuro-radiologists.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Infervision conducted a retrospective study to assess the clinical performance and notification functionality of the InferRead CT Stroke.AI software. The study evaluated the InferRead deep learning algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by trained neuro-radiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the InferRead time-to-notification and the Time-to-open-exam for the standard of care. The InferRead time-to-notification includes the time to receive the DICOM scan, analyze and the worklist application shows the results. The standard of care time-to-open-exam consisted of the time from the initial scan of the patient to when the radiologist first opens the exam for review.
A total of 369 non-contrast brain CT scans (studies) were obtained from three hospitals in the U.S. There were approximately equal numbers of positive and negative cases (51.5% of images with ICH and 48.5% without ICH, respectively) included in the analysis. Comparing the InferRead software output to the ground truth, the sensitivity and specificity of InferRead CT Stroke.AI are 0.916 (95% CI: 0.867-0.951) and 0.922 (95% CI: 0.872-0.957), which are significantly higher than the 80% null hypothesis (p values
§ 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.
0
Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
August 12, 2021
Infervision Medical Technology Co., Ltd. % Mr. Matt Deng Director Infervision US, Inc. 1900 Market Street PHILADELPHIA PA 19103
Re: K211179
Trade/Device Name: InferRead CT Stroke.AI Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QAS Dated: July 8, 2021 Received: July 12, 2021
Dear Mr. Deng:
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 and Part 809); medical device reporting (reporting of medical device-related adverse events) (21 CFR
1
- 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-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 mediation-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
Indications for Use
510(k) Number (if known) K211179
Device Name InferRead CT Stroke.AI
Indications for Use (Describe)
InferRead CT Stroke.AI is a radiological computer aided triage and notification software for use in the analysis of Non-Enhanced Head CT images. The device is intended to assist hospital networks and trained radiologists in workflow trage by flagging suspected positive findings of intracranial hemorrhage (ICH).
InferRead CT Stroke.Al uses an artificial 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 a worklist with marked cases of suspected ICH findings. The device does not alter the original medical image, does not remove cases from queue, and is not intended to be used as a diagnostic device. If the clinician does not view the case, or if a case is not flagged, cases remain to be processed per the standard of care.
The results of InferRead CT Stroke.AI 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.
| 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) |
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3
510(k) Summary
Infervision Medical Technology Co., Ltd. K211179
This 510(k) Summary is in conformance with 21 CFR 807.92
| Submitter: | Infervision Medical Technology Co., Ltd.
Room B401, 4th Floor, Building 1,
No. 12 Shangdi Information Road,
Haidian District, Beijing, 100085, China
Phone: +86 10-86462323 |
|--------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Primary Contact: | Matt Deng
Email: matt.deng@infervision.ai
Phone: 919-886-6082 |
| Second Primary
Contact: | Frank Wu
Email: frank.wu@infervision.ai
Phone: 857-988-1888 |
| Company Contact: | Xiaoyan Fan
Email: fxiaoyan@infervision.com
Phone: +86 13810508664 |
| Date Prepared: | April 8, 2021 |
| Device Name and Classification | |
| Trade Name: | InferRead CT Stroke.AI |
| Common Name: | Radiological computer aided triage and notification software |
| Classification: | Class II |
21 CFR 892.2080, Radiological computer aided triage and Regulation Number: notification software Classification Panel: Radiology Product Code:
QAS
Predicate Device:
| Primary Predicate | |
|---|---|
| Trade Name | BriefCase |
| 510(k) Submitter/Holder | Aidoc |
| Class | Class II |
| Regulation Number | 21 CFR 892.2080 |
| Classification Panel | Radiology |
| Product Code | QAS |
4
Device Description
InferRead CT Stroke.AI is a radiological computer-assisted triage and notification software device. The software device is a computer program with a deep learning algorithm running on Ubuntu operating system. The device can be deployed as an onsite server in the hospital and the user interacts with the software from a client workstation. The device can be broken down into 4 modules, the NeoViewer, Docking Toolbox, RePACS, and DLServer.
The Docking Toolbox module receives DICOM series and inspects the series against a list of requirements. Series that pass the requirements are sent into the system for prediction for intracranial hemorrhage. Series are processed in a first-out order. When hemorrhage is detected, the system marks the case in the work list prompting the user to conduct preemptive triage and prioritization.
When the user refreshes the page, cases with suspected findings will be marked with an indicator. Cases are identified, such as by Name and Patient ID. The user may filter and sort by suspected ICH and identify the case. A preview is available but is not intended for primary diagnosis and a radiologist must review the case per their standard process. The suspected cases assist in triaging intracranial hemorrhage cases sooner than standard of care practice alone.
Intended Use/Indications for Use
InferRead CT Stroke.AI is a radiological computer aided triage and notification software 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 suspected positive findings of intracranial hemorrhage (ICH).
InferRead CT Stroke.AI uses an artificial 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 a worklist with marked cases of suspected ICH findings. The device does not alter the original medical image, does not remove cases from queue, and is not intended to be used as a diagnostic device. If the clinician does not view the case, or if a case is not flagged, cases remain to be processed per the standard of care.
The results of InferRead CT Stroke.AI 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.
5
Comparison of Technological Characteristics
The subject and predicate devices are radiological computer-assisted triage and notification software. Both devices are artificial intelligence algorithms incorporated software packages for use with CT scanners, PACS, and workstations. Both devices process images intended to aid in prioritization and triage of non-enhanced head CT cases with intracranial hemorrhage. Both have the same intended use and indications for use for flagging suspected cases, and indicating to the clinician for review.
The predicate device sends pop up notifications and compressed previews to the workstations of radiologist. The subject device doesn't send pop up notifications as the predicate device. Instead, to fulfill the notification, the subject device visually marks the case in the worklist, indicating to a radiologist the need to review those images for ICH. For both devices, the user must be alert and receptive to the outputs of the device. Similarly to predicate device, the subject device also works in parallel to the standard of care. The indication prompts preemptive triage of the flagged case where the radiologist may decide to perform evaluation. Similarly, if the notification is rejected, the case remains in their standard queue to be handled per their standard of care.
The subject device provides a viewer on the workstation allowing the radiologist to preview the DICOM similarly to the compressed preview of the subject device. This viewer allows the user to Scroll through series. Similar to the predicate, the preview is for informational purposes only and not for diagnostic use. The notified clinicians are responsible for using the local imaging system for viewing the original images and engage the referring clinician for diagnosis and treatment decisions.
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The subject and predicate software utilizes a deep learning algorithm trained on medical images. The same type of safety and effectiveness questions as the predicate. That is, accurate detection of intracranial hemorrhage within the study on which a physician can base a clinically useful triage/prioritization assessment considering all available clinical information. Like the predicate, the subject device does not reading queue. Both devices operate in parallel with the standard of care, which remains the default option for all cases.
| Item | InferRead CT Stroke.AI
(Subject Device) | Aidoc Briefcase ICH (K180647)
(Predicate Device) | Comparison |
|--------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended Use /
Indications for Use | InferRead CT Stroke.AI is a
radiological computer aided triage
and notification software 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 suspected positive findings
of intracranial hemorrhage (ICH).
InferRead CT Stroke.AI uses an
artificial 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 a worklist with marked cases of
suspected ICH findings. The device
does not alter the original medical | 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, namely
Intracranial Hemorrhage (ICH).
BriefCase uses an artificial 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 | InferRead CT Stroke.AI
and the previously cleared
BriefCase (K180647)
have the same intended
use and\indications for use
in terms of finding
suspected intracranial
hemorrhage in non
contrast head CT, flagging
suspected cases, and
indicating the case to the
attention of the clinician. |
| | image, does not remove cases from queue, and is not intended to be used as a diagnostic device. If the clinician does not view the case, or if a case is not flagged, cases remain to be processed per the standard of care.
The results of InferRead CT Stroke.AI 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. | 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. | Both are designed to be used by the radiologist, prompt the radiologist to start preemptive triage of a flagged case |
| User Population | Radiologist | Radiologist | Both are indicated for use in analysis of non- enhanced head CT |
| Anatomical Region of Interest | Head | Head | |
| 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 | Both display DICOM information for informational purposes only |
| | | | |
| Segmentation of
region of interest | No; device does not mark, highlight,
or direct users' attention to a specific
location in the original image | No; device does not mark, highlight, or
direct users' attention to a specific
location in the original image | Neither marks, highlights
or directs attention to a
specific location in the
original image |
| Algorithm | Artificial intelligence algorithm with
database of images | Artificial intelligence algorithm with
database of images | Both use artificial
intelligence algorithm
with a database of images |
| Notification /
Prioritization | Yes, Case level indicator | Yes, pop-up notifications, case level
indicator | In both, the suspected
cases are indicated to the
user. The subject device
provides case level
indicator and allow the
user to sort suspected
cases to the top. |
| Preview Images | Presentation of a preview of the
study for initial assessment not
meant for diagnostic purposes
The device operates in parallel
with the standard of care, which
remains the default option for all
cases | Presentation of a preview of the
study for initial assessment not
meant for diagnostic purposes
The device operates in parallel
with the standard of care, which
remains the default option for all
cases | Both allow the user to
view the image. The
device is intended to work
in parallel with standard
of care. |
| Alteration of
original image | No | No | Neither alters the original
image. |
| Removal of cases
from worklist
queue | No | No | Neither removes cases
from the worklist queue. |
Detailed Comparison of the Subject and Predicate Devices
7
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9
Performance Data
Infervision conducted a retrospective study to assess the clinical performance and notification functionality of the InferRead CT Stroke.AI software. The study evaluated the InferRead deep learning algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by trained neuro-radiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the InferRead time-to-notification and the Time-to-open-exam for the standard of care. The InferRead time-to-notification includes the time to receive the DICOM scan, analyze and the worklist application shows the results. The standard of care time-to-open-exam consisted of the time from the initial scan of the patient to when the radiologist first opens the exam for review.
A total of 369 non-contrast brain CT scans (studies) were obtained from three hospitals in the U.S. There were approximately equal numbers of positive and negative cases (51.5% of images with ICH and 48.5% without ICH, respectively) included in the analysis. Comparing the InferRead software output to the ground truth, the sensitivity and specificity of InferRead CT Stroke.AI are 0.916 (95% CI: 0.867-0.951) and 0.922 (95% CI: 0.872-0.957), which are significantly higher than the 80% null hypothesis (p values