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JLK-NCCT is a radiological computer-aided triage and notification software designed for analyzing non-contrast head CT (NCCT) images. The software assists hospital networks and trained clinicians by flagging and communicating them of findings suggestive of (1) Intracranial Hemorrhage (ICH) and (2) large vessel occlusion (LVO) involving the internal carotid artery (ICA), middle cerebral artery M1 (MCA-M1) and middle cerebral artery M2 (MCA-M2) on NCCT images.

JLK-NCCT employs an artificial intelligence (AI) algorithm to analyze images and highlight cases with detected (1) ICH or (2) LVO on an on-premises or cloud-based JLK server. This occurs in parallel with the ongoing standard of care image interpretation. Users receive notifications for cases with suspected ICH and LVO findings via mobile devices. Notifications include compressed preview images for informational purposes only and not intended for diagnostic use beyond notification.

The device does not modify the original medical image, and is not intended to be used as a primary diagnostic device. The results of JLK-NCCT are intended to be used in conjunction with other patient information and professional judgment to assist with triage and prioritization of medical images. Clinicians who receive notifications are responsible for reviewing full images per the standard of care. JLK-NCCT is intended for adults use only.

JLK-NCCT is a radiological computer-assisted triage and notification (CADt) software that complies with the DICOM standard. It functions as a Non-Contrast Computed Tomography (NCCT) processing module, prioritizing triage, and notification for suspected intracranial hemorrhage (ICH) and large vessel occlusion (LVO). Operating as a notification-only tool, it assists hospital networks and clinicians by flagging critical cases and alerting specialists independent of standard workflows. JLK-NCCT's AI algorithm analyzes NCCT scans for ICH and LVO indicators and provides automated notifications to streamline clinical decision-making.

JLK-NCCT consists of an AI-based image analysis algorithm hosted on JLK servers either on-premises or cloud-based and a mobile software module for notification management. The AI processes NCCT head scans, detecting suspected ICH and LVO, and transmits mobile notifications with compressed preview images for triage. PACS integration is optional and supported when available. The system does not modify original medical images and is not intended for diagnostic use. JLK-NCCT integrates the JLK-ICH Model (ICH detection), LVO score Model (ischemic assessment), and HAS Model (Hyperdense Artery Sign detection), supporting real-time alerts and prioritization within hospital workflows (the ICH algorithm is the same as the device cleared under K243363).

JLK-NCCT was trained using clinical datasets from the U.S. and South Korea, totaling 3,067 cases, sourced from multiple institutions to ensure diversity and robustness. The dataset included NCCT scans acquired using imaging equipment from various manufacturers, such as GE, Siemens, Philips, and Toshiba, covering a range of scanning parameters to enhance model generalizability. Data were collected from institutions across different geographic locations, including hospitals in North Carolina and Texas in the U.S., as well as Seoul St. Mary's Hospital and other South Korean medical centers. All imaging studies were labeled by board-certified neuroradiologists. This diverse dataset strengthens the AI model's applicability across various clinical environments, supporting its role as a triage and notification tool for assisting clinicians in early detection and prioritization of suspected ICH and LVO cases.

The performance of the device's AI algorithms 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-NCCT 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.

Here's a breakdown of the acceptance criteria and study details for the JLK-NCCT device, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Note: The document primarily focuses on LVO detection performance for the standalone study, as the ICH algorithm was previously cleared.

Study Details for JLK-NCCT Performance Evaluation

Here's a detailed breakdown of the study that proves the device meets the acceptance criteria:

1. Sample Size Used for the Test Set and Data Provenance:

   • Sample Size: 288 cases for the standalone evaluation (144 LVO Positive, 144 LVO Negative).
   • Data Provenance: Retrospective study. The data were "newly acquired" and "independent of the training dataset." No specific countries of origin for the test set are mentioned, but the training data was from the U.S. and South Korea.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

   • Number of Experts: Two ground truthers, with a third intervening in cases of disagreement.
   • Qualifications of Experts: All truthers were U.S. board-certified neuroradiologists.

3. Adjudication Method for the Test Set:

   • Method: 2+1 truther scheme. Two ground truthers independently assessed each case, and a third ground truther intervened to resolve any disagreements.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size:

   • Was it done?: Yes, a reader performance study was conducted.
   • Effect Size (Improvement with AI vs. without AI assistance):
     • Sensitivity: JLK-NCCT demonstrated a sensitivity of 0.792.
       • Improvement over all readers (Neuroradiologist and General Radiologist average): 0.224 (0.792 - 0.568), (95% CI: 0.144–0.306).
       • Improvement over general radiologists: 0.159 (0.792 - 0.633), (95% CI: 0.083–0.237).
       • Improvement over neuroradiologists: 0.267 (0.792 - 0.525), (95% CI: 0.174–0.356).
     • Specificity: JLK-NCCT demonstrated a specificity of 0.933.
       • Improvement over all readers (Neuroradiologist and General Radiologist average): 0.093 (0.933 - 0.840), (95% CI: 0.038–0.150).
       • Improvement over general radiologists: 0.137 (0.933 - 0.796), (95% CI: 0.070–0.205).
       • Improvement over neuroradiologists: 0.064 (0.933 - 0.869), (95% CI: 0.005–0.126).
     • The device "satisfied both criteria show General Radiologist superiority and Neuroradiologist non-inferiority." This indicates that the AI significantly improved performance for general radiologists and did not degrade (or potentially improved) performance for neuroradiologists.

5. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

   • Was it done?: Yes. The document states, "A standalone performance evaluation using an independent dataset different from the one used for algorithm training." Performance metrics (Sensitivity, Specificity, AUC for LVO detection, and Time-to-Notification) are reported for this standalone performance.

6. The Type of Ground Truth Used:

   • Type: Expert consensus, established by U.S. board-certified neuroradiologists using a 2+1 truther scheme.

7. The Sample Size for the Training Set:

   • Sample Size: 3,067 cases.

8. How the Ground Truth for the Training Set Was Established:

   • Method: "All imaging studies were labeled by board-certified neuroradiologists." The exact number of neuroradiologists per case or the adjudication method for the training set is not specified, but it was expert-labeled.

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