(66 days)
Not Found
Yes
The document explicitly states that the device "uses an artificial intelligence algorithm" and that the "Al/ML-based algorithm is designed to analyze NCCT of the head scans".
No.
The device is a computer-aided triage and notification software that assists in identifying and communicating images suggestive of specific clinical conditions (intracranial hemorrhage) to specialists, but it is not intended for diagnostic use beyond notification and does not provide direct therapy or treatment.
No
The device is explicitly stated to be a "notification-only, parallel workflow tool" and that "Identification of suspected findings is not for diagnostic use beyond notification." It also states, "JLK-ICH is limited to the analysis of imaging data and should not be used in lieu of full patient evaluation or relied upon to make or confirm the diagnosis." The mobile application's image viewing is also specified as "non-diagnostic purposes only."
Yes
The device is described as a "radiological computer-aided triage and notification software" and "software that adheres to the DICOM standard." It comprises an "image analysis algorithm hosted on JLK servers and a mobile application for notification management." While it interacts with CT scanners and provides a mobile application interface, the core medical device functionality is described as software processing and notification, without mention of proprietary hardware components being part of the regulated device itself.
Based on the provided information, JLK-ICH is not an In Vitro Diagnostic (IVD) device.
Here's why:
- Definition of IVD: 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 examine these samples outside of the body.
- JLK-ICH's Function: JLK-ICH analyzes radiological images (non-contrast CT scans of the head). It does not analyze biological samples taken from the patient.
- Intended Use: The intended use clearly states it's a "radiological computer-aided triage and notification software indicated for use in the analysis of non-contrast CT images." It's a tool to assist in the interpretation and prioritization of medical images.
- Nature of the Input: The input is imaging data (DICOM standard), not biological specimens.
Therefore, JLK-ICH falls under the category of medical imaging software or a radiological computer-assisted triage and notification (CADt) device, not an In Vitro Diagnostic device.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
JLK-ICH is a radiological computer-aided triage and notification software indicated for use in the analysis of non-contrast CT images. JLK-ICH is a notification-only, parallel workflow tool that is intended to assist hospital networks and trained clinicians to identify and communicate images of specific patients to specialists, independent of the standard of care workflow.
JLK-ICH uses an artificial intelligence algorithm to analyze images for findings suggestive of prespecified clinical conditions and promptly notifies the appropriate medical specialists of these findings in parallel with the standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the head to detect intracranial hemorrhage (ICH). The system sends a notification to a clinician that a suspected ICH has been identified and recommends a review of those images. Images can be previewed and compressed through a mobile application.
Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating clinician before making care-related decisions or requests.
JLK-ICH is limited to the analysis of imaging data and should not be used in lieu of full patient evaluation or relied upon to make or confirm the diagnosis.
Product codes (comma separated list FDA assigned to the subject device)
OAS
Device Description
JLK-ICH is a radiological computer-assisted triage and notification (CADt) software that adheres to the DICOM standard. The device functions as a Non-Contrast Computed Tomography (NCCT) processing module, providing triage and notification for suspected hemispheric intracranial hemorrhage (ICH). This software acts as a notificationonly, parallel workflow tool for hospital networks and trained clinicians, enabling the identification and communication of suspected patient images to relevant specialists, independent of the standard care workflow. JLK-ICH processes non-contrast computed tomography (NCCT) scans, prioritizing triage and notification for suspected hemispheric intracranial hemorrhaqe (ICH). The system utilizes advanced artificial intelligence to automatically analyze NCCT scans for indicators of ICH and promptly notify appropriate medical specialists of potential cases.
JLK-ICH comprises an image analysis algorithm hosted on JLK servers and a mobile application for notification management. The AI/ML-based algorithm is designed to analyze NCCT of the head scans forwarded from CT scanners to the JLK servers. The mobile software module enables users to receive and toggle notifications for suspected ICH cases identified by the JLK-ICH Image Analysis Algorithm. Users can view a patient list and non-diagnostic CT scans through the mobile application. Image viewing through the mobile application interface is for non-diagnostic purposes only.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Non-contrast CT (NCCT)
Anatomical Site
head
Indicated Patient Age Range
Not Found
Intended User / Care Setting
hospital networks and trained clinicians, specialists
Description of the training set, sample size, data source, and annotation protocol
The JLK-ICH Al model was trained using a dataset that includes 14,462 ICH cases from various institutions, divided between US-based and Out-of-US sources. In total, the US-based datasets contributed 14,998 cases, of which 7,499 were ICH cases and 7,499 were normal cases. These datasets were sourced from institutions including Stanford University and Thomas Jefferson University Hospital. The Out-of-US datasets provided 13,926 cases, evenly split between 6.963 normal cases and 6.963 ICH cases, collected from institutions such as Universidale Federal de Sao Paulo, Seoul St. Mary's Hospital, and five other institutions. All cases are carefully separated from the clinical performance datasets.
Description of the test set, sample size, data source, and annotation protocol
A retrospective study was conducted using 376 NCCT scans obtained from various regions in the U.S. The analysis included 188 ICH-positive and 188 ICH-negative cases. Ground truth was determined by two American Board of Radiologists (ABR)-certified neuroradiologists, with a consensus reached by a third in case of disagreement.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
A retrospective standalone performance study was conducted to assess the sensitivity and specificity of the image analysis algorithm and notification functionality in detecting ICH in the head. The study used 376 NCCT scans, comprising 188 ICH-positive and 188 ICH-negative cases. Ground truth was established by two US board-certified neuroradiologists, with a third resolving disagreements.
Key results:
- Sensitivity: 97.3% (95% CI: 94.8% to 99.5%)
- Specificity: 97.9% (95% CI: 95.5% to 99.5%)
- AUC: 0.978 (95% CI: 0.959 to 0.992)
- Mean NCCT-to-notification time for ICH-positive cases: 0.19 ± 0.04 minutes.
Performance metrics were also stratified by age range, gender, race-ethnicity, scanner manufacturer, slice thickness, ICH subtype, and ICH volume, consistently showing high sensitivity and specificity across most categories.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sensitivity: 97.3% (95% CI: 94.8% to 99.5%)
Specificity: 97.9% (95% CI: 95.5% to 99.5%)
AUC: 0.978 (95% CI: 0.959 to 0.992)
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.
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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JLK, Inc. % John Smith M.D, J.D. - Global Regulatory Partner Hogan Lovells US LLP Columbia Square 555 Thirteenth Street NW Washington, District of Columbia 20004
January 3, 2025
Re: K243363
Trade/Device Name: Jlk-ich Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological Computer Aided Triage And Notification Software Regulatory Class: Class II Product Code: OAS Dated: October 29, 2024 Received: October 29, 2024
Dear John 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 (the 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.
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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" (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 (QS) 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 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-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 Assistant Director DHT8B: Division of Radiologic 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
JLK-ICH
Indications for Use (Describe)
JLK-ICH is a radiological computer-aided triage and notification software indicated for use in the analysis of non-contrast CT images. JLK-ICH is a notification-only, parallel workflow tool that is intended to assist hospital networks and trained clinicians to identify and communicate images of specific patients to specialists, independent of the standard of care workflow.
JLK-ICH uses an artificial intelligence algorithm to analyze images for findings suggestive of prespecified clinical conditions and promptly notifies the appropriate medical specialists of these findings in parallel with the standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the head to detect intracranial hemorrhage (ICH). The system sends a notification to a clinician that a suspected ICH has been identified and recommends a review of those images. Images can be previewed and compressed through a mobile application.
Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating clinician before making care-related decisions or requests.
JLK-ICH is limited to the analysis of imaging data and should not be used in lieu of full patient evaluation or relied upon to make or confirm the diagnosis.
Type of Use (Select one or both, as applicable)> Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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Image /page/4/Picture/0 description: The image shows a logo with a stylized unicorn with wings. The unicorn is white, and its horn is red. The letters "JLK" are written in blue below the unicorn. The logo appears to be for a company or organization with the initials JLK.
K243363
510(k) SUMMARY JLK, Inc.'s JLK-ICH K243363
| Applicant Name: | JLK, Inc.
JLK Tower, 5, Teheran-ro 33-gil, Gangnam-gu,
Seoul, Republic of Korea |
|-----------------|------------------------------------------------------------------------------------------------------------------------------------------|
| Contact Person: | Dongmin Kim
CEO
JLK Tower, 5, Teheran-ro 33-gil Gangnam-gu
Seoul, South Korea 06141
(+82) 02 6925 6189
dmkim@jlkgroup.com |
Date Prepared: January 3, 2025
Device Name and Classification
| • | Name of Device: | JLK-ICH |
|---|---|---|
| • | Common or Usual Name: | Radiological Computer-Assisted Triage and Notification |
| Software | ||
| • | Classification Panel: | Radiology |
| • | Regulation No: | 21 C.F.R. § 892.2080 |
| • | Regulatory Class: | Class II |
| • | Product Code: | QAS |
Predicate Device
| Manufacturer | Predicate Trade Name | Application No. | Product Code |
|---|---|---|---|
| Viz.ai, Inc. | Viz ICH | K193658 | QAS |
Device Description
JLK-ICH is a radiological computer-assisted triage and notification (CADt) software that adheres to the DICOM standard. The device functions as a Non-Contrast Computed Tomography (NCCT) processing module, providing triage and notification for suspected hemispheric intracranial hemorrhage (ICH). This software acts as a notificationonly, parallel workflow tool for hospital networks and trained clinicians, enabling the identification and communication of suspected patient images to relevant specialists, independent of the standard care workflow. JLK-ICH processes non-contrast computed tomography (NCCT) scans, prioritizing triage and notification for suspected hemispheric intracranial hemorrhaqe (ICH). The system utilizes advanced artificial intelligence to automatically analyze NCCT scans for indicators of ICH and promptly notify appropriate medical specialists of potential cases.
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Image /page/5/Picture/0 description: The image shows a logo with a stylized unicorn with wings. The unicorn is white with a blue outline, and its horn is red. The letters "JLK" are written in bold, dark blue font below the unicorn. The logo appears to be for a company or organization with the initials JLK.
JLK-ICH comprises an image analysis algorithm hosted on JLK servers and a mobile application for notification management. The Al/ML-based algorithm is designed to analyze NCCT of the head scans forwarded from CT scanners to the JLK servers. The mobile software module enables users to receive and toggle notifications for suspected ICH cases identified by the JLK-ICH Image Analysis Algorithm. Users can view a patient list and non-diagnostic CT scans through the mobile application. Image viewing through the mobile application interface is for non-diagnostic purposes only.
The JLK-ICH Al model was trained using a dataset that includes 14,462 ICH cases from various institutions, divided between US-based and Out-of-US sources. In total, the US-based datasets contributed 14,998 cases, of which 7,499 were ICH cases and 7,499 were normal cases. These datasets were sourced from institutions including Stanford University and Thomas Jefferson University Hospital. The Out-of-US datasets provided 13,926 cases, evenly split between 6.963 normal cases and 6.963 ICH cases, collected from institutions such as Universidale Federal de Sao Paulo, Seoul St. Mary's Hospital, and five other institutions. This broad collection of both US and Out-of-US data ensures that the Al model is trained on a diverse set of cases, enhancing its applicability across different populations and clinical environments. All cases are carefully separated from the clinical performance datasets.
The algorithm's performance was validated through a standalone performance evaluation using an independent dataset, distinct from the one for algorithm training data. Each case output from the JLK-ICH device was compared with a ground truth standard determined by two ground truthers, with a third ground truther intervening in cases of disagreement (i.e., 2+1 truther scheme). All truthers were US board-certified neuroradiologists.
Intended Use/Indications for Use
JLK-ICH is a radiological computer-aided triage and notification software indicated for use in the analysis of non-contrast CT images. JLK-ICH is a notification-only. parallel workflow tool that is intended to assist hospital networks and trained clinicians to identify and communicate images of specific patients to specialists, independent of the standard of care workflow.
JLK-ICH uses an artificial intelligence algorithm to analyze images for findings suggestive of pre-specified clinical conditions and promptly notifies the appropriate medical specialists of these findings in parallel with the standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the head to detect intracranial hemorrhage (ICH). The system sends a notification to a clinician that a suspected ICH has been identified and recommends a review of those images . Images can be previewed and compressed through a mobile application.
Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating clinician before making care-related decisions or requests.
JLK-ICH is limited to the analysis of imaging data and should not be used in lieu of full patient evaluation or relied upon to make or confirm the diagnosis.
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Image /page/6/Picture/0 description: The image shows a logo with a winged unicorn above the letters "JLK". The unicorn is drawn with blue lines, except for its horn, which is red. The letters "JLK" are in a bold, dark blue font and are positioned below the unicorn graphic. The overall design is simple and clean, with a focus on the unicorn as the central element.
Summary of Technological Characteristics
Both the subject and predicate devices utilize artificial intelligence and machine learning (AI/ML) algorithms and mobile notification software to identify and notify specialists, respectively, of patients with the presence of suspected intracranial hemorrhage (ICH) on noncontrast CT imaging of the head. JLK-ICH operates in parallel to standard of care without removing the cases from normal clinical workflow. The subject and predicate devices both include a mobile application with the same mobile software functions and outputs.
The software algorithms used in the subject device and predicate device are hosted on similar architectures that automatically receive imaging in the same DICOM format and use similar mechanisms to identify applicable imaging for analysis. Both the subject and the predicate devices include mobile application software that allows users to receive push notifications for patients identified with a suspected ICH. Users can view a unique list of patients with suspected ICH and examine the non-contrast CT scan of the patient through the mobile application. The imaging viewing of NCCT scans analyzed by both the subject and predicate devices are intended solely for informational and prioritization review purposes only (i.e., triage and notification) and are not intended for diagnostic use. Both devices interface with a nondiagnostic mobile DICOM image viewer, which allows the specialist to preview non-diagnostic images and view patient details associated with a series. The outputs of the subject and predicate devices are the same; both devices identify suspected ICH and send notifications of suspected ICH findings from the same server.
The average total NCCT-to-notification time for the JLK-ICH system is 0.19 ± 0.04 minutes. This performance is comparable to the predicate device, Viz ICH (K193658), which reported a mean time of 0.49 ± 0.15 minutes. JLK-ICH successfully meets the target time-to-notification of ≤ 0.49 ± 0.15 minutes set by the predicate device.
The JLK-ICH is as safe and effective as the predicate device Viz ICH (K193658). JLK-ICH device has the same intended use and similar indications, technological characteristics, principles of operation, and performance characteristics as the legally marketed predicate device. Performance data demonstrates that JLK-ICH is as safe and effective as the predicate device, the previously cleared Viz ICH. Thus, JLK-ICH is substantially equivalent.
| Viz ICH | JLK-ICH | |
|---|---|---|
| Image Analysis | ||
| Supported Imaging | ||
| Modality | Non-contrast CT (NCCT) | Non-contrast CT (NCCT) |
| Alteration of Original | ||
| Image Database | No | No |
| Results of Image | ||
| Analysis | Internal, no image marking | Internal, no image marking |
| Image Viewing Functionality | ||
| Preview Images | Initial assessment, non- | |
| diagnostic purposes | Initial assessment, non- | |
| diagnostic purposes |
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Image /page/7/Picture/0 description: The image shows a logo with the letters "JLK" in a bold, dark blue font. Above the letters is a stylized drawing of a unicorn with wings. The unicorn is white with blue outlines, and its horn is red. The overall design is clean and professional.
| DICOM Information about the patient, study and current image. | DICOM Information about the patient, study and current image. | |
|---|---|---|
| View DICOM Data | ||
| Image viewing and any manipulation (window, pan, level, zoom) | Yes | Yes |
| 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. |
| Technological Characteristics | ||
| Compatibility with the environment and other devices | DICOM Compatible | DICOM Compatible |
| Transfer, store, and process DICOM images | Yes | Yes |
| Technical Implementation | Artificial intelligence algorithm with database of images | Artificial intelligence algorithm with database of images |
| Interference with standard workflow | No. Cases are not removed from worklist or deprioritized. | No. Cases are not removed from worklist or deprioritized. |
| Data acquisition | Acquires medical image data from DICOM-compliant imaging devices and modalities | Acquires medical image data from DICOM-compliant imaging devices and modalities |
| Time to Notification | $0.49\pm0.15$ minutes | $0.19 +0.04$ minutes |
Performance Data
JLK, Inc. conducted extensive performance validation testing and software verification of the JLK-ICH system. This performance validation testing demonstrated that the JLK-ICH system accurately represents key processing parameters under a range of clinically relevant parameters and perturbations associated with the software's intended use. The documentation was provided as recommended by FDA's Guidance for Industry and FDA staff, "Content of Premarket Submissions for Device Software Functions," June 14, 2023.
In addition to the software verification and validation testing described in the sections above, JLK, Inc. performed a standalone performance in accordance with the §892.2080 special controls to demonstrate adequate clinical performance of the JLK-ICH module. The test dataset used during the standalone performance evaluation was newly acquired, and appropriate steps were taken to ensure it was independent of the training dataset used in model development.
A retrospective study was conducted to assess the sensitivity and the standalone performance of the image analysis algorithm and notification functionality of Triage intracranial hemorrhage (ICH). Specifically, the study evaluated the Triage ICH image analysis in terms of sensitivity and specificity with respect to ground truth (as established by US board-certified neuroradiologists), in detecting ICH in the head.
376 NCCT scans were obtained from various regions in the U.S. The analysis included 188 ICH-positive and 188 ICH-negative cases. Note that there were 30 cases sent to the third truther (i.e., tie-breaker interpreter) due to disagreements between the first two truthers. The
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Image /page/8/Picture/0 description: The image shows a logo with a stylized unicorn with wings. The unicorn is primarily white with blue outlines, except for its horn, which is red. The letters "JLK" are displayed in bold, dark blue font below the unicorn image. The logo appears to be for a company or organization with the initials JLK.
patient cohort was enriched to promote the generalizability of clinical and demographic variables (e.g., age, gender, and race-ethnicity) to the target patient population. Ground truth was determined by two American Board of Radiologists (ABR)-certified neuroradiologists, with a consensus reached by a third in case of disagreement.
The primary endpoints, sensitivity and specificity, both exceeded 80%, Specifically, the sensitivity was 97.3% with a 95% confidence interval (CI) of 94.8% to 99.5%. The specificity was 97.9% with a 95% Cl of 95.5% to 99.5%. The area under the curve (AUC) was 0.978 with a 95% Cl of 0.959 to 0.992.
Image /page/8/Figure/3 description: This image is a Receiver Operating Characteristic (ROC) curve for JLK-ICH. The ROC curve plots the true positive rate against the false positive rate. The area under the ROC curve is 0.978, which indicates a high level of accuracy. A blue dot is shown on the curve at threshold = 0.5.
As part of a secondary analysis, the company stratified the performance of the device by various confounding variables:
| Performance Metrics Overview for Patients Categorized by Age | |||
|---|---|---|---|
| Age Range | |||
| (Years) | N | Sensitivity [95% CI] | Specificity [95% CI] |