(101 days)
Not Found
Yes
The document explicitly states that the device "uses an artificial intelligence algorithm" and that this algorithm is a "convolutional network (CNN)".
No
The device is described as a "notification-only, parallel workflow tool" that assists in identifying and communicating images for review by trained radiologists. It explicitly states, "Identification of suspected findings is not for diagnostic use beyond notification" and "should not be used in lieu of full patient evaluation or relied upon to make or confirm the diagnosis." This indicates it's a tool to alert users to potential findings, not to directly treat or diagnose a condition.
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 emphasizes that "JLK-SDH 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." Its purpose is to triage and notify, not to provide a diagnosis itself.
Yes
The device is described as a "software package" and a "processing module" that is "hosted on JLK servers." While it interacts with imaging data and provides notifications via a mobile application, the core functionality and the device itself are defined as software. There is no mention of proprietary hardware being part of the device submission.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens derived from the human body. The description clearly states that JLK-SDH analyzes images (non-contrast CT scans of the head). It does not process or analyze biological samples like blood, urine, or tissue.
- The intended use is for image analysis and notification. The device is described as a "parallel workflow tool" to assist radiologists in identifying and communicating images. Its function is to analyze images for findings suggestive of a condition and send notifications.
- The output is not a diagnostic result. The intended use explicitly states, "Identification of suspected findings is not for diagnostic use beyond notification." The device recommends a review of the images by a clinician, who is responsible for making care-related decisions.
Therefore, while JLK-SDH is a medical device that uses AI and image processing, its function and intended use fall outside the scope of an In Vitro Diagnostic. It is a radiological computer-assisted triage and notification (CADt) software.
No
The letter does not 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-SDH is a notification-only, parallel workflow tool that is intended to assist trained radiologists to identify and communicate images of specific patients to a specialist, independent of the standard of care workflow.
JLK-SDH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate user of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the head for subdural hemorrhage (SDH) and sends notifications to a clinician that a suspected SDH has been identified and recommends a review of those images. Images can be previewed and compressed through PACS and mobile applications.
Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests.
JLK-SDH 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
QAS
Device Description
JLK-SDH is a radiological computer-assisted triage and notification (CADt) software package compliant with the DICOM standard. The device functions as a Non-Contrast Computed Tomography (NCCT) processing module, providing triage and notification for suspected hemispheric subdural hemorrhage (SDH). It serves as a notification-only, parallel workflow tool for hospital networks and trained clinicians. The device helps to identify and communicate specific patient images to trained radiologists, independent of the standard of care workflow. Utilizing an artificial intelligence algorithm, the system automatically receives and analyzes NCCT studies for image features indicating the presence of SDH and sends a notification to alert a radiologist of the case.
This algorithm, hosted on JLK servers, is designed to analyze non-contrast CT images of the head acquired on CT scanners and forwarded to JLK servers. The mobile software module that enables user to receive and toggle notifications for suspected subdural hemorrhages identified by the JLK-SDH Image Analysis Algorithm. Users can view a patient list, and nondiagnostic 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
Trained radiologists / Hospital networks
Description of the training set, sample size, data source, and annotation protocol
The images used to train the algorithm were sourced from datasets that included equipment from various manufacturers, such as GE. Siemens, Philips, and Toshiba. The training dataset utilized to develop the convolutional network (CNN) included 29,524 non-contrast CT (NCCT) scans that had been obtained in patients with and without intracranial hemorrhage. In particular, 3,330 patients had SDH, 11,732 had different kinds of intracranial hemorrhage (Intraparenchymal Hematoma (IPH), Intraventricular Hemorrhage (IVH), Subarachnoid Hemorrhage (SAH) or Epidural Hematoma (EDH)), and 14,462 patients did not have any intracranial hemorrhage. The datasets were comprised from a variety of institutions across separate geographic locations (the USA. Brazil, and South Korea), including Stanford University, Universidale Federal de Sao Paulo, Thomas Jefferson University Hospital, Seoul St. Mary's Hospital, and six other institutions to ensure its robustness and applicability. This broad collection of both US and Out-of-US data ensures that the AI model is trained on a diverse set of cases, enhancing its applicability across different populations and clinical environments.
Description of the test set, sample size, data source, and annotation protocol
The performance of the device's AI alqorithms was validated in a standalone performance evaluation using an independent dataset different from the one used for algorithm training. Each case output from the JLK-SDH 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.
560 NCCT scans were obtained from various regions in the U.S. The analysis included 174 SDH positive and 386 SDH negative cases. Note that there were 28 cases sent to the third truther (i.e., tie-breaker interpreter) due to disagreements between the first two truthers. The 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. We also considered variability among CT scanners, including those from Siemens, GE Medical Systems, Philips, Toshiba/Canon. 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 study was conducted to assess the sensitivity and the standalone performance of the image analysis algorithm and notification functionality of Triage subdural hemorrhage (SDH).
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.
Sample size: 560 NCCT scans (174 SDH positive and 386 SDH negative cases).
AUC: 0.974 with a 95% Cl of 0.958 to 0.989.
Standalone performance: The primary endpoints, sensitivity and specificity, both exceeded 80%. Specifically, the sensitivity was 97.1%, with a 95% confidence interval (CI) of 94.4% to 99.4%. The specificity was 97.4%, with a 95% Cl of 95.8% to 99.0%.
MRMC: Not Found
Key results: The JLK-SDH system for SDH-positive cases demonstrated efficient triage with a total NCCT-to-notification time ranging from an average of 11.49±3.04 seconds (0.19± 0.05 minutes), which successfully meets the target of 69.1±34.3 seconds (1.15±0.57 minutes) established by the predicate device, Viz SDH (K220439).
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sensitivity: 97.1% (95% CI: 94.4% to 99.4%)
Specificity: 97.4% (95% CI: 95.8% to 99.0%)
PPV: Not Found
NPV: Not Found
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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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
March 3, 2025
Re: K243611
Trade/Device Name: Jlk-SDH Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QAS Dated: February 3, 2025 Received: February 3, 2025
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 (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.
1
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 (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-devices/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
2
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,
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 JLK-SDH
Indications for Use (Describe)
JLK-SDH is a notification-only, parallel workflow tool that is intended to assist trained radiologists to identify and communicate images of specific patients to a specialist. independent of the standard of care workflow.
JLK-SDH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate user of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the head for subdural hemorrhage (SDH) and sends notifications to a clinician that a suspected SDH has been identified and recommends a review of those images. Images can be previewed and compressed through PACS and mobile applications.
Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests.
JLK-SDH 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)
ver-The-Counter Use (21 CFR 801 Subpart C)
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Image /page/4/Picture/0 description: The image shows a logo with the letters "JLK" in blue, with a stylized unicorn above the letters. The unicorn has white wings and a red horn. The logo appears to be for a company or organization with the initials JLK.
K243611
510(k) SUMMARY JLK, Inc.'s JLK-SDH K243611
| 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 31, 2025
Device Name and Classification
| Name of Device: | JLK-SDH |
|---|---|
| Common or Usual Name: | Radiological Computer-Assisted Triage and Notification |
| Software | |
| Classification Panel: | Radiology |
| Regulation No: | 21 C.F.R. § 892.2080 |
| Regulatory Class: | Class II |
| Product Code: | QAS |
Predicate Device
| Manufacturer | Predicate Trade Name | Application No. | Product Code |
|---|---|---|---|
| Viz.ai. Inc. | Viz SDH | K220439 | QAS |
Device Description
JLK-SDH is a radiological computer-assisted triage and notification (CADt) software package compliant with the DICOM standard. The device functions as a Non-Contrast Computed Tomography (NCCT) processing module, providing triage and notification for suspected hemispheric subdural hemorrhage (SDH). It serves as a notification-only, parallel workflow tool for hospital networks and trained clinicians. The device helps to identify and communicate specific patient images to trained radiologists, independent of the standard of care workflow. Utilizing an artificial intelligence algorithm, the system automatically receives and analyzes NCCT studies for image features indicating the presence of SDH and sends a notification to alert a radiologist of the case.
This algorithm, hosted on JLK servers, is designed to analyze non-contrast CT images of the head acquired on CT scanners and forwarded to JLK servers. The mobile software module
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Image /page/5/Picture/0 description: The image shows a logo with the letters "JLK" in bold, blue font. Above the letters is a white unicorn with blue wings. The unicorn's horn is red. The logo appears to be for a company or organization with the initials JLK.
that enables user to receive and toggle notifications for suspected subdural hemorrhages identified by the JLK-SDH Image Analysis Algorithm. Users can view a patient list, and nondiagnostic CT scans through the mobile application. Image viewing through the mobile application interface is for non-diagnostic purposes only.
The images used to train the algorithm were sourced from datasets that included equipment from various manufacturers, such as GE. Siemens, Philips, and Toshiba. The training dataset utilized to develop the convolutional network (CNN) included 29,524 non-contrast CT (NCCT) scans that had been obtained in patients with and without intracranial hemorrhage. In particular, 3,330 patients had SDH, 11,732 had different kinds of intracranial hemorrhage (Intraparenchymal Hematoma (IPH), Intraventricular Hemorrhage (IVH), Subarachnoid Hemorrhage (SAH) or Epidural Hematoma (EDH)), and 14,462 patients did not have any intracranial hemorrhage. The datasets were comprised from a variety of institutions across separate geographic locations (the USA. Brazil, and South Korea), including Stanford University, Universidale Federal de Sao Paulo, Thomas Jefferson University Hospital, Seoul St. Mary's Hospital, and six other institutions to ensure its robustness and applicability. This broad collection of both US and Out-of-US data ensures that the AI model is trained on a diverse set of cases, enhancing its applicability across different populations and clinical environments.
The performance of the device's AI alqorithms was validated in a standalone performance evaluation using an independent dataset different from the one used for algorithm training. Each case output from the JLK-SDH 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-SDH is a notification-only, parallel workflow tool that is intended to assist trained radiologists to identify and communicate images of specific patients to a specialist, independent of the standard of care workflow.
JLK-SDH uses an artificial intelligence algorithm to analyze images for findings suggestive of a prespecified clinical condition and to notify an appropriate user of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the head for subdural hemorrhage (SDH) and sends notifications to a clinician that a suspected SDH has been identified and recommends a review of those images that are previewed through mobile applications may be compressed only for notification.
Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating physician before making care-related decisions or requests.
JLK-SDH 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 stylized unicorn with wings. The unicorn is white with blue outlines for the body and wings, and a red horn. Below the unicorn is the text "JLK" in large, bold, dark blue letters. The logo appears to be for a company or organization with the initials JLK.
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 subdural hemorrhage (SDH) on noncontrast CT imaging of the head. JLK-SDH 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 SDH. Users can view a unique list of patients with suspected SDH 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 radiologist 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 SDH and send notifications of suspected SDH findings from the same server.
The subject and predicate device differ in the recipients of the notifications they send. While the predicate device, Viz SDH, sends notifications to neurovascular or neurosurgical specialists for time-sensitive cases with suspected findings, the subject device sends notifications to the radiologists. The radiologists then contact the appropriate specialist for further consultation per US standard-of-care workflow (e.g., neurovascular or neurosurgical specialist, or any other clinician capable of treating SDH patients). Yet, there is a clinical benefit in the subject workflow, similar to alerting a neurovascular/neurosurgical specialist of an SDH. In the United States, there is typically a designated "duty radiologist" and an "on-call neurosurgeon" who remains on standby at home and is ready to report to the hospital for emergency surgical procedures as required. Accordingly, this difference in notified user does not impact nor alter the US hospital workflow, as notifying a radiologist of suspected SDH functionally serves the same clinical utility as notifying a neurovascular or neurosurgery specialist within a given healthcare facility without raising any new or different questions of safety or effectiveness.
The JLK-SDH is as safe and effective as the predicate device Viz SDH (K220439). JLK-SDH has the same intended use and similar indications, technological characteristics, principles of operation, and performance characteristics as the legally marketed predicate device. Any differences in indications do not alter the intended diagnostic use of the device and do not affect its safety and effectiveness when used as labeled as discussed above. In addition, the technological differences between JLK-SDH and its predicate device do not raise any new questions of safety or effectiveness. Performance data demonstrates that JLK-SDH is as safe and effective as the predicate device, the previously cleared Viz SDH. Thus, JLK-SDH is substantially equivalent.
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Image /page/7/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. Below the unicorn is the text "JLK" in bold, dark blue letters. The logo appears to be for a company or organization with the initials JLK.
| Subject Device | Predicate Device | |
|---|---|---|
| Device Name | JLK-SDH | Viz SDH |
| 510(k) Number | K243611 | K220439 |
| Regulation Number | 21 C.F.R. § 892.2080 | 21 C.F.R. § 892.2080 |
| Product Code | QAS | QAS |
| Intended User | Radiologist | Neurovascular Specialist |
| Anatomical Region | Head | Head |
| Region of Interest | Hemispheric Subdural | |
| Hemorrhage | Hemispheric Subdural | |
| Hemorrhage | ||
| Indicated Imaging | ||
| Modality | Non-contrast CT (NCCT) | Non-contrast CT (NCCT) |
| Alteration of Original | ||
| Image Database | No | No |
| Diagnostic Application | Notification-only | Notification-only |
| Algorithm | ||
| Implementation | Artificial Intelligence / Machine | |
| Learning | Artificial Intelligence / Machine | |
| Learning. | ||
| Results of Image | ||
| Analysis | Internal, no image marking | Internal, no image marking |
| 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. | ||
| Relationship to standard | ||
| of care workflow | In Parallel / Concurrently | In Parallel / Concurrently |
| Compatibility with the | ||
| environment and other | ||
| devices | DICOM Compatible | DICOM Compatible |
| Technical | ||
| Implementation | Artificial intelligence algorithm | |
| with database of images | Artificial intelligence algorithm | |
| with database of images | ||
| Preview Images | Initial assessment; non- | |
| diagnostic purposes | ||
| Presentation of preview of the | ||
| study for initial informational | ||
| purposes | Initial assessment; non- | |
| diagnostic purposes | ||
| Presentation of preview of the | ||
| study for initial informational | ||
| purposes | ||
| 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 | 0.19 ±0.05 minutes | 1.15±0.57 minutes |
Performance Data
JLK, Inc. conducted extensive performance validation testing and software verification of the JLK-SDH system. This performance validation testing demonstrated that the JLK-SDH 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,
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"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-SDH 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 subdural hemorrhage (SDH). Specifically, the study evaluated the Triage SDH image analysis in terms of sensitivity and specificity with respect to ground truth (as established by US board-certified neuroradiologists) in detecting SDH in the head.
560 NCCT scans were obtained from various regions in the U.S. The analysis included 174 SDH positive and 386 SDH negative cases. Note that there were 28 cases sent to the third truther (i.e., tie-breaker interpreter) due to disagreements between the first two truthers. The 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. We also considered variability among CT scanners, including those from Siemens, GE Medical Systems, Philips, Toshiba/Canon. 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 97.1%, with a 95% confidence interval (CI) of 94.4% to 99.4%. The specificity was 97.4%, with a 95% Cl of 95.8% to 99.0%. The area under the curve (AUC) was 0.974 with a 95% Cl of 0.958 to 0.989.
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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 States (i.e. Clinical Site) | |||
|---|---|---|---|
| Clinical Site | Z | Sensitivity [95% CI] | Specificity [95% CI] |
| Region 001 | 422 | 0.97 [0.93, 0.99] | 0.99 [0.98, 1.00] |
| Region 002 | 138 | 0.98 [0.94, 1.00] | 0.9 [0.83, 0.96] |
Region 001: South – North Carolina, Texas, Florida, Georgia
Region 002: Northeast - New York / Midwest - Missouri, Iowa, Wisconsin, Illinois, Ohio
| Performance Metrics Overview for Patients Categorized by Age | |||
|---|---|---|---|
| Age Range | |||
| (Years) | N | Sensitivity [95% Cl] | Specificity [95% Cl] |
| 5mm | 1 | 1.0 [1.00, 1.00] |
| Performance Metrics Overview for Patients Categorized by SDH Thickness | ||
|---|---|---|
| SDH Thickness | Z | Sensitivity [95% Cl] |
| Thickness |