(158 days)
Not Found
Yes
The device description explicitly states that it uses "deep learning algorithms" and "deep learning techniques," which are forms of machine learning and artificial intelligence. The "Mentions AI, DNN, or ML" section also confirms the use of "deep learning algorithms" and "deep learning techniques."
No.
Explanation: The device is a software workflow tool designed to aid in prioritizing the clinical assessment of non-contrast head CT cases with features suggestive of midline shift. It is a radiological computer-assisted triage and notification software system and is not intended to provide a direct therapeutic effect.
Yes
Explanation: The device is designed to identify "features suggestive of midline shift (MLS)" from non-contrast head CT cases to aid in prioritizing clinical assessment. This identification of a potential medical condition is a diagnostic function, even though the device is a workflow tool and its results are not for standalone clinical decision-making.
Yes
The device is described as a "software workflow tool" and a "radiological computer-assisted triage and notification software system" that analyzes images and provides notifications to a PACS/workstation. There is no mention of any accompanying 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.
- Device Function: The EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM analyzes medical images (non-contrast head CT scans). It does not analyze biological samples from the patient.
- Intended Use: The intended use is to aid in prioritizing the clinical assessment of CT cases with features suggestive of midline shift. This is a workflow tool for radiologists reviewing images, not a diagnostic test performed on a biological sample.
Therefore, the EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM falls under the category of a medical device, specifically a radiological computer-assisted triage and notification software system, rather than an In Vitro Diagnostic.
No
The document does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (EFAI MLSCT) is a software workflow tool designed to aid in prioritizing the clinical assessment of non-contrast head CT cases with features suggestive of midline shift (MLS) in individuals aged 18 years and above. EFAI MLSCT analyzes cases using deep learning algorithms to identify suspected MLS findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
EFAI MLSCT is not intended to direct attention to specific portions of an image or to anomalies other than MLS. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out MLS or otherwise preclude clinical assessment of CT studies.
Product codes
QAS
Device Description
EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (EFAI MLSCT) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze non-contrast head CTs and alerts the PACS/RIS workstation once images with features suggestive of MLS are identified.
Through the use of EFAI MLSCT, a radiologist is able to review studies with features suggestive of MLS earlier than in standard of care workflow.
The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original non-contrast head CT. The device aims to aid in prioritization and triage of radiological medical images only.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
EFAI MLSCT analyzes cases using deep learning algorithms to identify suspected MLS findings.
The software uses deep learning techniques to automatically analyze non-contrast head CTs.
Artificial intelligence algorithm with database of images.
Both the subject and predicate devices use an artificial intelligence algorithm to analyze images and highlight cases with detected pathologies, alongside the ongoing standard of care image interpretation.
Input Imaging Modality
non-contrast head CT
Anatomical Site
Head
Indicated Patient Age Range
18 years and above
Intended User / Care Setting
radiologist / PACS/workstation
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 retrospective, blinded, multisite clinical validation study was conducted with a dataset of 300 cases (102 positives and 198 negatives) consecutively collected from multiple clinical sites across the United States (U.S.). Each case included only one CT study. None of the cases was used as part of the EFAI MLSCT model development or analytical validation testing. The study population contained 51.67% females and 48.33% males, and the mean age of cases was 59.46 years. The CT scanner manufacturers of images were acquired from Siemens, Toshiba, GE Medical Systems, Philips, and Canon Medical Systems. Potential confounding cases in the dataset include artifact, poor patient position issue, intracranial hemorrhage, ischemic-stroke and related features, space-occupying lesions, and others.
The presence of MLS in each case was determined independently by three U.S. board-certified radiologists, and the reference standard (ground truth) was generated by the majority agreement between the three experts.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
A retrospective, blinded, multisite clinical validation study was conducted with a validation dataset of 300 cases. Each case included only one CT study. The performance acceptance criteria were set such that the lower bounds of 95% confidence intervals (Cls) of both sensitivity and specificity should exceed 0.8.
The observed results of the standalone performance validation study demonstrated that EFAI MLSCT by itself, in the absence of any interaction with a clinician, can provide case-level notifications with features suggestive of MLS with satisfactory results. The EFAI MLSCT was able to demonstrate sensitivity and specificity of 0.961 (95% CI=0.903-0.985) and 0.955 (95% CI=0.916-0.973) respectively, along with an AUROC of 0.983 (95% CI=0.967-0.996). The secondary endpoint of the observed system processing time per study is 62.04 seconds (95% Cl=60.65-63.44) on average and was significantly less than the pre-specified performance goal.
In addition, the results of the subgroup analysis, which included different genders, age groups, race or ethnicity groups, CT manufacturer groups, and CT slice thickness groups, demonstrated that EFAI MLSCT consistently performed high performance, underscoring its reliability and effectiveness across diverse subgroups. The device's performance in cases with image quality issues (including artifact and poor patient position issue) and radiologic findings (including intracranial hemorrhage, ischemic-stroke and related features, space-occupying lesions, and others) was also evaluated. The device consistently performed reliably across these circumstances. Furthermore, analysis of the device's ability to identify mild to severe levels of MLS, revealed that EFAI MLSCT maintains stable performance.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sensitivity: 0.961 (95% CI=0.903-0.985)
Specificity: 0.955 (95% CI=0.916-0.973)
AUROC: 0.983 (95% CI=0.967-0.996)
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
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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Image /page/0/Picture/0 description: The image contains the logos of the Department of Health and Human Services and the Food and Drug Administration (FDA). The Department of Health and Human Services logo is on the left, and the FDA logo is on the right. The FDA logo includes the letters "FDA" in a blue square, followed by the words "U.S. Food & Drug Administration" in blue text.
Ever Fortune.AI. Co., Ltd. Dr. Ti-Hao Wang Chief Technology Officer 8F. No. 360, Sec.1 Jingmao Rd., Beitun Dist. Taichung City, 406040 Taiwan
December 6, 2024
Re: K241923
Trade/Device Name: EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100) Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological Computer Aided Triage And Notification Software Regulatory Class: Class II Product Code: QAS Dated: June 21, 2024 Received: November 18, 2024
Dear Dr. Ti-Hao Wang:
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 (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 available 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.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device"
1
(https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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-regulatory
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assistance/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,
Jessica Lamb
Jessica Lamb, Ph.D Assistant Director Imaging Software Team DHT8B: 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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Indications for Use
Submission Number (if known)
Device Name
EFAINEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (MLS-CT-100)
Indications for Use (Describe)
EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (EFAI MLSCT) is a software workflow tool designed to aid in prioritizing the clinical assessment of non-contrast head CT cases with features suggestive of midline shift (MLS) in individuals aged 18 years and above. EFAI MLSCT analyzes cases using deep learning algorithms to identify suspected MLS findings. It makes caselevel output available to a PACS/workstation for worklist prioritization or triage.
EFAI MLSCT is not intended to direct attention to specific portions of an image or to anomalies other than MLS. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out MLS or otherwise preclude clinical assessment of CT studies.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
0ver-The-Counter Use (21 CFR 801 Subpart C)
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510(k) Summary
General Information 1.
| 510(k) Sponsor | Ever Fortune.AI Co., Ltd. |
|---|---|
| Address | 8F., No.360, Sec. 1, Jingmao Rd., |
| Beitun Dist., | |
| Taichung City 406040, | |
| Taiwan | |
| Applicant | Joseph Chang |
| Contact Information | 886-04-23213838 #216 |
| joseph.chang@everfortune.ai | |
| Correspondence Person | Ti-Hao Wang |
| Contact Information | 886-04-23213838 #168 |
| thothwang@gmail.com | |
| tihao.wang@everfortune.ai | |
| Date Prepared | November, 2024 |
Proposed Device 2.
| Proprietary Name | EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (MLS-CT-100) |
|---|---|
| Common Name | EFAI MLSCT |
| Classification Name | Radiological computer-assisted triage and notification software |
| Regulation Number | 21 CFR 892.2080 |
| Product Code | QAS |
| Regulatory Class | II |
3. Predicate Device
| Proprietary Name | qER |
|---|---|
| Premarket Notification | K200921 |
| Classification Name | Radiological computer-assisted triage and notification software |
| Regulation Number | 21 CFR 892.2080 |
| Product Code | QAS |
| Regulatory Class | II |
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Image /page/5/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a stylized figure of a person with a head made of a network of interconnected dots. The figure is teal in color. To the right of the figure is the text "EVER FORTUNE.AI", also in teal.
Device Description 4.
EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (EFAI MLSCT) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze non-contrast head CTs and alerts the PACS/RIS workstation once images with features suggestive of MLS are identified.
Through the use of EFAI MLSCT, a radiologist is able to review studies with features suggestive of MLS earlier than in standard of care workflow.
The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original non-contrast head CT. The device aims to aid in prioritization and triage of radiological medical images only.
ર. Intended Use / Indications for Use
EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (EFAI MLSCT) is a software workflow tool designed to aid in prioritizing the clinical assessment of non-contrast head CT cases with features suggestive of midline shift (MLS) in individuals aged 18 years and above. EFAI MLSCT analyzes cases using deep learning algorithms to identify suspected MLS findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
EFAI MLSCT is not intended to direct attention to specific portions of an image or to anomalies other than MLS. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out MLS or otherwise preclude clinical assessment of CT studies.
Comparison of Technological Characteristics with Predicate Device 6.
| Feature/
Function | Proposed Device:
EFAI MLSCT | Predicate Device:
qER
(K200921) |
|--------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended
Use/Indication
for Use | EFAI NEUROSUITE CT MIDLINE
SHIFT ASSESSMENT SYSTEM
(EFAI MLSCT) is a software
workflow tool designed to aid in | qER is a radiological computer aided
triage and notification software
indicated for use in the analysis of
non-contrast head CT images. |
| | prioritizing the clinical assessment of
non-contrast head CT cases with
features suggestive of midline shift
(MLS) in individuals aged 18 years
and above. EFAI MLSCT analyzes
cases using deep learning algorithms
to identify suspected MLS findings. It
makes case-level output available to a
PACS/workstation for worklist
prioritization or triage.
EFAI MLSCT is not intended to direct
attention to specific portions of an
image or to anomalies other than
MLS. Its results are not intended to be
used on a stand-alone basis for clinical
decision-making nor is it intended to
rule out MLS or otherwise preclude
clinical assessment of CT studies. | The device is intended to assist
hospital networks and trained medical
specialists in workflow triage by
flagging the following suspected
positive findings of pathologies in
head CT images: intracranial
hemorrhage, mass effect, midline
shift and cranial fracture.
qER uses an artificial intelligence
algorithm to analyze images on a
standalone cloud-based application in
parallel to the ongoing standard of care
image interpretation. The user is
presented with notifications for cases
with suspected findings.
Notifications include non-diagnostic
preview images that are meant for
informational purposes only. The
device does not alter the original
medical image and is not intended to
be used as a diagnostic device.
The results of the device 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. |
| Classification/
Product Code | 21 CFR 892.2080/QAS | 21 CFR 892.2080/QAS |
| Anatomical
region of interest | Head | Head |
| Data acquisition
protocol | Non contrast CT scan of the head | Non contrast CT scan of the head |
| 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. |
| Algorithm | Artificial intelligence algorithm with
database of images. | Artificial intelligence algorithm with
database of images. |
| Notification/
Prioritization | Yes, with midline shift as the only
output, there are no controls for the
user to select the finding or
combination of findings for triage | Yes, with controls for the user to select
the finding or combination of findings
for triage |
| Preview images | No | 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. |
| Alteration of
original image | No | No |
| Removal of cases
from worklist
queue | No | No |
| Abnormalities
triaged | finding: midline shift | Four findings: Intracranial
haemorrhage, Mass effect, midline
shift, cranial fracture and ICH |
| User control over
triage | Triages non-contrast CT scan only
when midline shift is identified. No
user control for configuring the triage
finding. | Triages non-contrast CT scan when
any of the 4 abnormalities are
identified.
Notification/ prioritization with user
provided control for configuring the
triage finding or combination of
finding(s) |
| Device
Deployment | deployed only on-premise | Performs de-identification of studies
on-premise and pushes them to the
cloud module for analysis. |
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Image /page/6/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a stylized human figure in teal, with a green circle containing a network of white dots at the head. To the right of the figure, the words "EVER" and "FORTUNE.AI" are written in teal. The word "FORTUNE.AI" is written in a smaller font than the word "EVER", and the dot in the "O" of "FORTUNE" is replaced with a green circle containing a network of white dots.
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Image /page/7/Picture/0 description: The image shows a logo for a company called "EVER FORTUNE.AI". The logo consists of a stylized figure of a person with a green head and a turquoise body. The head is a green sphere with a network of white lines and dots on it. The text "EVER" is in large, turquoise letters, and the text "FORTUNE.AI" is in smaller, turquoise letters below it. The logo is simple and modern, and it conveys a sense of innovation and technology.
7. Performance Data
Performance of the EFAI MLSCT has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/A1:2016 - Medical device software - Software life cycle processes, in addition to the FDA Guidance documents, "Content of Premarket Submissions for Device Software Functions" and "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions."
Ever Fortune.AI conducted a retrospective, blinded, multisite clinical validation study with the proposed device EFAI MLSCT with a pre-determined primary and secondary endpoint and
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performance goals to evaluate the performance of the EFAI MLSCT in identifying midline shift (MLS) from non-contrast head computed tomography (CT) scans on a validation dataset of 300 cases (102 positives and 198 negatively consecutively collected from multiple clinical sites across the United States (U.S.). Each case included only one CT study. None of the cases was used as part of the EFAI MLSCT model development or analytical validation testing, as the U.S. cases were solely collected for this study, while the model development and validation utilized cases from Taiwan.
The study population contained 51.67% females and 48.33% males, and the mean age of cases was 59.46 years. The CT scanner manufacturers of images were acquired from Siemens, Toshiba, GE Medical Systems, Philips, and Canon Medical Systems. Potential confounding cases in the dataset include artifact, poor patient position issue, intracranial hemorrhage, ischemic-stroke and related features, space-occupying lesions, and others.
The presence of MLS in each case was determined independently by three U.S. board-certified radiologists, and the reference standard (ground truth) was generated by the majority agreement between the three experts. The performance acceptance criteria were set such that the lower bounds of 95% confidence intervals (Cls) of both sensitivity and specificity should exceed 0.8.
The observed results of the standalone performance validation study demonstrated that EFAI MLSCT by itself, in the absence of any interaction with a clinician, can provide case-level notifications with features suggestive of MLS with satisfactory results. The EFAI MLSCT was able to demonstrate sensitivity and specificity of 0.961 (95% CI=0.903-0.985) and 0.955 (95% CI=0.916-0.973) respectively, along with an AUROC of 0.983 (95% CI=0.967-0.996), which is substantially equivalent to the predicate device (qER, K200921). The secondary endpoint of the observed system processing time per study is 62.04 seconds (95% Cl=60.65-63.44) on average and was significantly less than the pre-specified performance goal.
In addition, the results of the subgroup analysis, which included different genders, age groups, race or ethnicity groups, CT manufacturer groups, and CT slice thickness groups, demonstrated that EFAI MLSCT consistently performed high performance, underscoring its reliability and effectiveness across diverse subgroups. We also evaluated the device's performance in cases with image quality issues (including artifact and poor patient position issue) and radiologic findings (including intracranial hemorrhage, ischemic-stroke and related features, space-occupying lesions, and others) to assess the impact of these potential confounders. The device consistently performed reliably across these circumstances. Furthermore, our analysis of the device's ability to identify mild to severe levels of MLS, revealed that EFAI MLSCT maintains stable performance. In conclusion, the results demonstrate that the EFAI MLSCT device is determined to be substantially equivalent in safety and effectiveness to the predicate device.
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Safety & Effectiveness 8.
EFAI MLSCT has been designed, verified and validated in compliance with 21 CFR, Part 820.30 requirements. The device has been designed to meet the requirements associated with ISO 14971:2019 Medical devices - Application of risk management to medical devices. The EFAI MLSCT performance has been validated using retrospective data from case data and through the use of Reader comparison analysis.
9. Substantial Equivalence
The EFAI MLSCT has minor differences compared to the predicate device. These differences are related to the Intended Use, user control for triage configuration and inclusion of preview images in notifications.
The indications for use statement for the subject device is substantially similar to the cleared indications for Qure Ai's qER device. The overall effect of both the subject and predicate devices-namely, the workflow triaging of CT images by flagging suspected findings of pathologies in head CT images-remains the same. Although the subject device is intended to flag midline shift only without user control for configuring the triage finding, whereas the predicate flags mass effect, midline shift, cranial fractures, and ICH with user control for configuring the triage finding or combination of findings, this difference does not raise new questions of safety or effectiveness.
Both the subject and predicate devices use an artificial intelligence algorithm to analyze images and highlight cases with detected pathologies, alongside the ongoing standard of care image interpretation. The subject device provides notifications for cases with suspected findings without preview images, whereas the predicate provides notifications that include preview images for informational purposes only, not intended for diagnostic use beyond notification. Since clinicians can and should view the original CT before making a final determination on a case, with or without the sent preview images, the differences in notification outputs do not introduce new risks. Thus, the proposed device does not raise new questions of safety or efficacy, as the risks and technology used by both devices are the same and are mitigated similarly.
The predicate devices can be deployed either on client premises (on-premise) or on cloud servers (on-cloud). For on-cloud mode, an on-premise gateway interfaces with HIPAA-compliant cloud servers. The subject device, however, is exclusively deployed on-premise and does not interface with cloud servers, thereby no new risks or safety issues arise.
10. Conclusion
Based on the information submitted in this premarket notification, and based on the indications for use, technological characteristics, and performance testing, the EFAI MLSCT raises no new questions of safety and effectiveness and demonstrates substantial equivalence to the predicate device.