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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.

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.

Here's a detailed breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for the Test Set and Data Provenance

• Sample Size: 560 NCCT scans
  • 174 SDH positive cases
  • 386 SDH negative cases
• Data Provenance: Retrospective study. Scans were obtained from various regions in the U.S.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Three.
• Qualifications: All truthers were US board-certified neuroradiologists.

4. Adjudication Method for the Test Set

• Adjudication Method: 2+1 truther scheme. Ground truth was determined by two neuroradiologists, with a third neuroradiologist intervening in cases of disagreement. (28 cases were sent to the third truther).

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? No, the text describes a standalone performance evaluation of the device's AI algorithm.

6. Standalone (Algorithm Only) Performance

• Was a standalone performance study done? Yes. The performance data section explicitly states, "JLK, Inc. performed a standalone performance in accordance with the §892.2080 special controls to demonstrate adequate clinical performance of the JLK-SDH module."

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus of US board-certified neuroradiologists.

8. Sample Size for the Training Set

• Sample Size: 29,524 non-contrast CT (NCCT) scans
  • 3,330 patients had SDH
  • 11,732 had different kinds of intracranial hemorrhage (IPH, IVH, SAH, or EDH)
  • 14,462 patients did not have any intracranial hemorrhage

9. How the Ground Truth for the Training Set Was Established

• The document does not explicitly detail the exact method for establishing ground truth for the training set. It only mentions that the images "had been obtained in patients with and without intracranial hemorrhage" and categorizes them by the type of hemorrhage. While it suggests clinical diagnoses, the specific process (e.g., expert review, clinical reports, pathology) used to label these training cases is not described.

Clarification on "Acceptance Criteria"
The document states that the "primary endpoints, sensitivity and specificity, both exceeded 80%." This implies that >80% for both sensitivity and specificity served as the acceptance criteria for the standalone performance study. For time-to-notification, the acceptance criterion was to 'meet the target' established by the predicate device.

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JLK-AILink is a software that receives digital images and data from various sources (i.e., CT scanners, MR scanners, ultrasound systems, computed & direct radiographic devices, secondary capture devices, scanners, imaging gateways, or other imaging sources). Images and analyzed data with optional modules can be stored, communicated, processed, and displayed within the system and or across computer networks at distributed locations. Typical users of this system are trained professionals including but not limited to physicians and medical technicians. The device is not intended for mammography.

JLK-AILink is a software designed to streamline the management, visualization, and communication of medical imaging data within clinical workflows. It facilitates bidirection with hospital PACS systems or imaging equipment such as MR, CT, or X-ray scanners, enabling the communication of DICOM images. This functionality ensures seamless integration with existing hospital IT infrastructure while adhering to DICOM communication standards.

The software includes a DICOM viewer that offers tools for image manipulation and annotation, such as zoom, magnify, contrast adjustment, cobb angle measurement, and annotation tools, making it an efficient and userfriendly interface for medical professionals. The modular architecture allows healthcare institutions to integrate optional analysis solutions with DICOM communication to expand its image analysis capabilities as needed.

JLK-AlLink also supports mobile access, enabling clinicians to view images, interact with annotations, and receive critical alerts remotely. With a focus on interoperability, the software ensures compatibility not only with PACS systems but also with containerized environments, allowing for secure and data management.

This combination of DICOM functionalities, modular integration, and mobile accessibility makes ILK-AILink a clinical tool for enhancing clinical workflows and improving the efficiency of medical imaging data usage in diverse healthcare settings.

The provided FDA 510(k) clearance letter for JLK-AILink (K250348) primarily focuses on establishing substantial equivalence to a predicate device (STARPACS™ K031013) for a general medical image management and processing system. The documentation explicitly states that "JLK-AILink is a software that receives digital images and data from various sources... Images and analyzed data with optional modules can be stored, communicated, processed, and displayed..."

However, the provided text does not contain the detailed information required to describe the acceptance criteria and the specific study proving the device meets those criteria for any optional analysis modules. The clearance is for the base platform, which is a medical image management and processing system, not an AI-powered diagnostic or assistive tool with specific performance metrics.

The "Non-Clinical and/or Clinical Tests Summary & Conclusions" section (Page 7-8) states:
"JLK-AILink was designed and developed according to the standards required for software development. For software evaluation, software functionality, risk management, cybersecurity, verification, validation, and requirements were addressed. The evaluation was tested according to the verification and validation processes and planning, and the test results support that all system requirements have met their acceptance criteria and are adequate for their intended use."

This statement confirms that the base system functionality (image reception, storage, communication, processing, display, and features like zoom, magnify, contrast adjustment, Cobb angle measurement, annotation tools) was verified and validated against its design requirements and acceptance criteria, but it does not specify what those criteria were in a quantitative sense, nor does it detail a performance study as one would expect for an AI/ML diagnostic or assistive device that provides analytical outputs (e.g., disease detection, segmentation, quantitative measurements relevant to a clinical endpoint).

Therefore, based solely on the provided text, the following information cannot be extracted:

• A table of acceptance criteria and reported device performance for an AI module: The document describes the system as a general image management and processing system. While it mentions "optional modules" that can expand its "image analysis capabilities," the clearance itself (K250348) pertains to the core platform, not specific AI analysis functionalities that would require detailed performance metrics.
• Sample size for the test set: Not mentioned.
• Data provenance for the test set: Not mentioned.
• Number of experts used to establish ground truth: Not mentioned, as ground truth for specific AI analysis outputs is not addressed.
• Qualifications of experts: Not mentioned.
• Adjudication method: Not mentioned.
• MRMC comparative effectiveness study: Not mentioned. The focus is on functionality and equivalence to a predicate PACS system, not comparative clinical performance for an AI-assisted workflow.
• Effect size of human reader improvement with AI assistance: Not applicable based on the scope of clearance described.
• Standalone (algorithm only) performance: Not mentioned, as no specific AI algorithm's performance is described.
• Type of ground truth used: Not mentioned.
• Sample size for the training set: Not mentioned.
• How ground truth for the training set was established: Not mentioned.

In summary: The provided 510(k) clearance letter for JLK-AILink pertains to a general medical image management and processing system (PACS-like functionality), falling under Regulation Number 892.2050 and Product Code LLZ. This product code typically covers systems that handle, display, and process medical images, but does not inherently imply specific AI diagnostic or analytical capabilities that would necessitate the detailed performance study information requested. The "optional modules" are noted, but their specific functionalities and performance (if they are AI-powered analytics) are not covered in this general clearance document.

To answer your questions for an AI-enabled device with specific analytical functions, one would typically need a 510(k) summary that details the performance of those specific AI modules, rather than just the general image management system.

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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.

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 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.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly list "acceptance criteria" for the standalone performance in a table, but it states that the primary endpoints, sensitivity and specificity, both exceeded 80%. This implies that 80% or greater for both sensitivity and specificity were the target acceptance criteria. It also sets a time-to-notification target.

Here's a table summarizing the implicit acceptance criteria and the reported performance:

2. Sample Size and Data Provenance for the Test Set

• Sample Size: 376 Non-Contrast CT (NCCT) scans. This included 188 ICH-positive and 188 ICH-negative cases.
• Data Provenance: Retrospective study. The scans were obtained from various regions in the U.S.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Two primary ground truthers, with a third used for adjudication in cases of disagreement.
• Qualifications of Experts: All truthers were U.S. board-certified neuroradiologists (specifically, American Board of Radiologists (ABR)-certified neuroradiologists).

4. Adjudication Method for the Test Set

The adjudication method used was a 2+1 scheme.

• Ground truth was initially determined by two ABR-certified neuroradiologists.
• In cases of disagreement between the first two truthers, a third neuroradiologist intervened to reach a consensus (act as a tie-breaker).
• The document notes that 30 cases were sent to the third truther due to disagreements.

5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted. The study focused on standalone performance. The document explicitly states: "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."

Therefore, there is no effect size reported for human readers improving with AI vs. without AI assistance.

6. Standalone Performance (Algorithm Only)

Yes, a standalone performance evaluation (algorithm only, without human-in-the-loop performance) was performed.

• "The algorithm's performance was validated through a standalone performance evaluation using an independent dataset, distinct from the one for algorithm training data."
• "JLK, Inc. performed a standalone performance in accordance with the §892.2080 special controls to demonstrate adequate clinical performance of the JLK-ICH module."

7. Type of Ground Truth Used

The ground truth used was expert consensus. It was established by U.S. board-certified neuroradiologists following a 2+1 adjudication scheme.

8. Sample Size for the Training Set

The training dataset was substantial and diverse:

• Total ICH cases: 14,462
• US-based datasets: 14,998 cases (7,499 ICH cases, 7,499 normal cases)
• Out-of-US datasets: 13,926 cases (6,963 normal cases, 6,963 ICH cases)
• Total Training Cases (approximate sum): 14,998 (US) + 13,926 (Out-of-US) = 28,924 cases total. (Note: The first line stating 14,462 ICH cases seems to be a subset or summary that might exclude normal cases, given the detailed breakdown follows. The calculated sum provides a better approximation of the total
  images used for training).

9. How Ground Truth for the Training Set Was Established

The document states: "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... All cases are carefully separated from the clinical performance datasets."

While it mentions the dataset composition, it does not explicitly detail how the ground truth for the training set was established. It only specifies the ground truth establishment method (2+1 neuroradiologist consensus) for the test set. It is common for training data to have ground truth established through similar expert review processes, but this document does not provide those specifics for the training set itself.

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JLK-PWI is an image processing software package to be used by trained professionals, including but not limited to physicians and medical technicians.

The software runs on a standard off-the-shelf computer or a virtual platform, such as VMware, and can be used to perform image viewing, processing, and analysis of Diffusion-weighted imaging (DWI) and Perfusion-weighted imaging (PWI) images for the brain from Magnetic Resonance Imaging (MRI) systems. Data and images are acquired through DICOM-compliant imaging devices. JLK-PWI provides both viewing and analysis capabilities for dynamic imaging datasets obtained through MRI protocols.

The DWI analysis capabilities are used to analyze local water diffusion properties from diffusion-weighted MRI data.

The PWI analysis capabilities are for analyzing dynamic imaging data, showing properties of changes in contrast over time. This functionality includes calculating parameters related to tissue flow (perfusion) and tissue blood volume.

JLK-PWI is image processing software designed to analyze Diffusion-weighted imaging (DWI) and Perfusionweighted imaging (PWI) images from Magnetic Resonance Imaging (MRI) scanners. The software calculates local water diffusion properties from DWI images and the perfusion area, which is a delayed perfusion area, using the analyzed perfusion map from PWI images.

JLK-PWI can be used to communicate with a DICOM-compliant device or a PACS server to receive DWI and PWI images. The software is designed to automatically receive and analyze DWI and PWI images with DICOM image data. The analyzed diffusion and perfusion parameters, which are related to the tissue blood flow and volume, are written to the source DICOM and stored in the data storage. Medical professionals can review the analysis results through the implemented user interface (UI) or a connected PACS server.

The FDA 510(k) summary for JLK-PWI presents the following information regarding its acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not explicitly state the sample size used for the test set.

The data provenance is not explicitly stated in terms of country of origin or explicit retrospective/prospective study design for the performance validation. However, the context implies the data would be real-world medical image data.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

The document does not explicitly state the number of experts used to establish the ground truth for the test set or their specific qualifications.

4. Adjudication Method for the Test Set

The document does not explicitly describe the adjudication method used for the test set. It mentions comparison to "results obtained using RAPID," suggesting the predicate device's output may have served as a reference in some capacity, but no expert adjudication method is detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

A multi-reader multi-case (MRMC) comparative effectiveness study was not discussed in the provided summary as a method for evaluating the device's performance. The study focused on the standalone performance of JLK-PWI and its equivalence to the predicate.

6. Standalone Performance Study

Yes, a standalone performance study was done. The summary states, "JLK-PWI demonstrated reliable and accurate performance in calculating local water diffusion properties from DWI images and the perfusion area, which is a delayed perfusion area, using the analyzed perfusion map from PWI images." This indicates that the algorithm's performance was evaluated independently.

7. Type of Ground Truth Used

The primary "ground truth" or reference standard used for evaluating JLK-PWI's performance appears to be the results generated by the legally marketed predicate device, iSchemaView RAPID (K121447). The summary states, "The software's results were substantially equivalent to those obtained using RAPID." This suggests a comparative approach where RAPID's output served as the benchmark.

8. Sample Size for the Training Set

The document does not explicitly state the sample size for the training set.

9. How Ground Truth for the Training Set Was Established

The document does not explicitly describe how the ground truth for the training set was established.

Summary of Study Information Provided:

The provided 510(k) summary focuses primarily on asserting that JLK-PWI's performance, particularly in calculating DWI and PWI parameters, is reliable, accurate, and "substantially equivalent" to that of the predicate device, iSchemaView RAPID. While it indicates that "extensive performance validation testing and software verification" were conducted, many specific details about the study design, such as sample sizes for test and training sets, data provenance, expert involvement for ground truth, and adjudication methods, are not explicitly provided in this summary. The primary claim of meeting acceptance criteria is through demonstrating "substantial equivalence" to the predicate device's results.

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JLK-CTP is an image processing software package to be used by trained professionals, including but not limited to physicians and medical technicians.

The software runs on a standard "off-the-shelf" computer or a virtual platform, such as VMware, and can be used to perform image viewing, processing, and analysis of CT perfusion images for the brain.

Data and images are acquired through DICOM compliant imaging devices.

JLK-CTP provides both viewing and analysis capabilities for dynamic imaging datasets obtained through CT Perfusion protocols.

The analysis is for visualization and examination of imaging data, revealing characteristics of contrast changes over time. This functionality includes the calculation of CT perfusion parameters associated with tissue flow (perfusion) and tissue blood volume.

JLK-CTP is image processing software designed to analyze CT perfusion (CTP) images. Using the analyzed perfusion map, the software calculates the volume of the reduced cerebral blood flow (CBF) area and the volume of the delayed Tmax area in the CTP images.

JLK-CTP can be used to communicate with a DICOM-compliant device or a PACS server to receive CTP images. The software is designed to automatically receive and analyze a head CTP image with DICOM image data. The analyzed perfusion parametric maps, which are related to the tissue blood flow and volume, are written to the source DICOM and stored in the data storage. Users can review the analysis results through the implemented user interface (UI) or a connected PACS server.

JLK-CTP Device Acceptance Criteria and Study Details

The provided FDA 510(k) summary for the JLK-CTP device describes its intended use as an image processing software for analyzing CT perfusion (CTP) images of the brain. The primary goal of the regulatory submission is to demonstrate substantial equivalence to the predicate device, iSchemaView RAPID (K121447).

Here's a breakdown of the acceptance criteria and study details based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document states that JLK-CTP "demonstrated reliable and accurate performance in calculating the reduced blood flow and delayed Tmax tissue volumes. The software's results were substantially equivalent to those obtained using RAPID." While explicit quantitative acceptance criteria are not detailed in this summary, the implicit acceptance criterion is demonstrated substantial equivalence to the predicate device, RAPID, in terms of its ability to calculate these perfusion parameters.

2. Sample Size Used for the Test Set and Data Provenance

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It simply mentions "extensive performance validation testing," but no specific numbers are provided in this summary.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

The document does not explicitly state the number of experts used or their qualifications for establishing ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method (e.g., 2+1, 3+1, none) for the test set. The comparison is made to "results obtained using RAPID," suggesting a comparative analysis to the predicate's output rather than an independent expert adjudication of each case.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study, nor does it quantify any effect size of human readers improving with AI vs. without AI assistance. The focus of this submission appears to be on the standalone performance of the algorithm in comparison to the predicate device.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone study was done. The summary explicitly states: "JLK-CTP demonstrated reliable and accurate performance in calculating the reduced blood flow and delayed Tmax tissue volumes. The software's results were substantially equivalent to those obtained using RAPID." This indicates that the performance of the JLK-CTP algorithm itself was evaluated and compared to the output of the predicate device.

7. Type of Ground Truth Used

The "ground truth" for the performance evaluation appears to be the results of the predicate device, iSchemaView RAPID. The summary repeatedly emphasizes that JLK-CTP's results were "substantially equivalent to those obtained using RAPID." This implies that RAPID's output was used as the reference standard for comparison.

8. Sample Size for the Training Set

The document does not explicitly state the sample size used for the training set.

9. How the Ground Truth for the Training Set Was Established

The document does not explicitly state how the ground truth for the training set was established. Given the comparison to the predicate device for evaluation, it is plausible that data processed by the predicate, or data with established clinical interpretation, would have been used for training, but this is not confirmed in the provided text.

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JBS-LVO is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of standard of care workflow.

JBS-LVO uses an artificial intelligence algorithm to analyze images for findings suggestive of a pre-specified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected positive findings is not for diagnostic use beyond notification. Specifically, the device analyzes CT angiogram images of the brain acquired in the acute setting and sends notifications to a neurovascular specialist that a suspected large vessel occlusion has been identified and recommends a review of those images. Images can be previewed through a mobile application. JBS-LVO is intended to analyze terminal ICA and MCA-M1 vessels for LVOs.

Images that are previewed through the mobile application are compressed and for informational purposes only. They are not intended for diagnostic use beyond notification. The JBS-LVO device does not alter the original medical image. 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. JBS-LVO 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 a diagnosis.

Limitations:
The device does not process scans containing metallic artifacts.

JBS-LVO is a radiological computer aided triage and notification (CAD) software package compliant with the DICOM standard. IBS-VV is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to analyze computed tomography angiography (CTA) images for findings suggestive of a suspected large vessel occlusion (LVO) and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Specifically, JBS-LVO is optimized to evaluate occlusions of the intracranial carotid artery (ICA) and proximal middle cerebral artery (MCA-M1 segment). It is important to clarify that this quantification is solely used within the device's Al module to facilitation process. The output provided to heathcare professionals is stiritly a flag indicating the presence (positive) of an LVO, in accordance with regulatory guidelines.

JBS-LVO is a combination of software modules that allow for detection and notification of patients with a suspected LVC. JBS-LVO consists of an algorithm and mobile application software module.

The JBS-LVO Image Analysis Algorithm (LVO Detection Algorithm) is a locked, artificial intelligence (Al) software algorithm utilizing convolutional neural network (CNN) that analyzes CTA images of the brain for a suspected LVO. The LVO Detection Algorithm is hosted on the ILK-server and analyzes applicable CTA images of the brain that are acquired on CT scanners and are automatically transmitted to the ILK-server. Upon detection of a suspected LVO, the LVO notification module sends a notification of the suspected finding.

The JBS-LVO notification functionality enable medical professionals and clinicians to preview compressed and informational images through via mobile application notification. Image viewing through the mobile application interface is for informational purposes only and is not for diagnostic use.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

Notes:

• The document implies that the sensitivity and specificity acceptance criteria were "exceeded," suggesting a >80% threshold was the minimum.
• The AUC is reported as a performance metric, indicating its importance, even if a specific numerical acceptance criterion wasn't explicitly listed alongside the other two.
• The CTA to notification time criterion is established by comparison to a reference predicate device (Rapid LVO, K221248).

Study Details

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: Not explicitly stated as a numerical count of cases or patients in the provided text. The document mentions "a retrospective study" was conducted and "each case output from the JBS-LVO device was compared with a ground truth."
• Data Provenance: Retrospective study. The origin of the data (e.g., country of origin) is not explicitly mentioned for the test set, but it states that "the images used to train the algorithm were sourced from datasets that included equipment from various manufacturers, such as Siemens, Philips, Toshiba, and GE", implying a diverse source for the overall dataset.

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

• Number of Experts: Three (two initial ground truthers, with a third intervening in cases of disagreement).
• Qualifications of Experts: All truthers were US board-certified neuroradiologists.

4. Adjudication Method for the Test Set

• Adjudication Method: 2+1 adjudication. The ground truth was determined by two ground truthers, with a third ground truther intervening in cases of disagreement.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• MRMC Study: No, an MRMC comparative effectiveness study was not conducted. The document describes a "standalone performance evaluation" of the algorithm only.

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

• Standalone Performance Study: Yes, a standalone performance evaluation of the algorithm without human-in-the-loop performance was conducted. The text states: "The performance of the device's AI algorithms was validated in a standalone performance evaluation, utilizing an independent dataset different from the one used for algorithm training." and "JLK, Inc. performed a standalone performance with the §892.2080 special controls to show acceptance of the clinical performance of the JBS-LVO module."

7. The Type of Ground Truth Used

• Ground Truth Type: Expert consensus. Specifically, ground truth was "established by US board-certified neuro-radiologists."

8. The Sample Size for the Training Set

• Training Set Sample Size: Not explicitly stated as a numerical count in the provided text. The document refers to it as "datasets."

9. How the Ground Truth for the Training Set Was Established

• Training Set Ground Truth: The training data was "labeled by trained radiologists to identify the presence of LVO." The specific number or adjudication method for these "trained radiologists" is not detailed, but it implies expert labeling.

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MEDIHUB PROSTATE is an image processing software package that performs outlining, processing, viewing, and editing of prostate MR images. The software can support study review and analysis of prostate MR data with computed modules. The analysis result can be presented in various formats, including images overlaid onto source MR images and a structured report.

MEDIHUB PROSTATE semi-automatically outlines the prostate based on MR images by contour, and it requires the user to edit with image manipulating tools and confirm the final result. The package provides additional functionalities including registered multiparametric-MRI viewing and combining MR sequences into a single image to support visualization. Edited PI-RADS report and semi annotated prostate region can be viewed in each single image in the final report.

MEDIHUB PROSTATE is intended to be used by trained radiologists and urologists. Patient management decisions should not be made solely based on the analysis performed by MEDIHUB PROSTATE.

Limitations:

• . MEDIHUB PROSTATE has been validated for use with Siemens 3T Vida and Skyra MRI machines.
• . MEDIHUB PROSTATE is also designed for use with the Siemens 3T T2 MRI series, supporting slice thicknesses ranging from 3.5 mm to 5 mm.
• . MEDIHUB PROSTATE has been tested on patients aged 55 years and above.

MEDIHUB PROSTATE is an image processing software package for multi-parametric prostate MR image analysis. The analysis may assist trained radiologists in clinical interpretation of prostate MR studies. It can be accessed through a web browser, and provides the following main features:

• . A semi-automatic processing module that outlines the prostate region and performs multiparametric MRI image registration.
• . A user-interaction module in which the user can edit and approve the computed prostate outline and determine PSA density using serum PSA level.
• . A user-interaction module in which the user can view multi-parametric MRI images, and outline and analyze ROIs. This extension will also apply a mathematical operation on the input images to combine information from another MRI sequences into a single combination image.
• A semi-automatic processing module that collects all results for exporting and transferring back to the user.
  All measurements are manual except for the prostate volume, which is semi-automatic and requires user review. The method for measuring prostate volume is straightforward and unaffected by patient demographics. Users have the option to outline the prostate either completely manually or with semi-automatic assistance. This device is not intended to be used for fully automatic prostate delineation. It does not involve any segmentation functions by itself. The Al functionality is limited to assessing the total prostate volume, without segmenting lesions.

In semi-automatic mode, our device employs an Al-based algorithm to initially outline the prostate volume, and then it requires the user to edit, review and approve. Additionally, the device calculates the total prostate volume. However, users are responsible for performing all other image annotations and measurements manually. This implies that the final decision should be confirmed by the user, and the user should not rely solely on the device's analysis.

Additional annotations and measurements: all calculated manually

• PI-RADS
• Location of seminal vesicles
• . Prostate zones
• . DWI and DCE graphs

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for MEDIHUB PROSTATE:

I. Acceptance Criteria and Reported Device Performance

The primary performance metric for the prostate region segmentation algorithm is the Dice coefficient, and the secondary metric is the Hausdorff Distance.

II. Sample Sizes and Data Provenance

• Training Dataset:

  • Korea: 748 cases
  • US (University of Missouri Health Care): 709 cases
  • Total Training: 1457 cases
  • Data Provenance: Retrospective, collected from Korea and the US (University of Missouri Health Care).

• Validation Dataset:

  • Korea: 80 cases
  • US (University of Missouri Health Care): 136 cases
  • Total Validation: 216 cases
  • Data Provenance: Retrospective, collected from Korea and the US (University of Missouri Health Care).

• Clinical Test Dataset (Standalone Performance):

  • Sample Size: 114 T2 MR images
  • Data Provenance: Retrospective, collected from the US (University of Missouri Health Care).

• Clinical Test Dataset (Reader Performance):

  • Sample Size: 73 cases (a subset of the 114 cases used for standalone performance)
  • Data Provenance: Retrospective, collected from the US (University of Missouri Health Care).

III. Number and Qualifications of Experts for Test Set Ground Truth

• Number of Experts: Three expert radiologists.
• Qualifications of Experts: The document states they were "expert-level radiologists" without further specifying their years of experience or board certifications.

IV. Adjudication Method for Test Set Ground Truth

• Adjudication Method: Majority rule approach. In cases of ties, annotations were consolidated through discussion and mutual agreement among the three radiologists. This is a 2+1 (if two agree, that becomes the truth; if all three disagree, they discuss to reach consensus) or potentially a 3-way consensus if agreement is required.

V. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? Yes, a "Clinical Testing (Reader Performance)" study was conducted, which is a type of MRMC study where human reader performance with and without AI assistance is compared.
• Effect Size of Improvement: The improvements in the Dice coefficient for prostate outlining performance for the three radiologists when using the prostate region segmentation algorithm of MEDIHUB PROSTATE were:
  • Radiologist 1: 0.156
  • Radiologist 2: 0.011
  • Radiologist 3: 0.008
    These values represent the change in their individual Dice coefficients when assisted by the AI.

VI. Standalone Performance (Algorithm Only)

• Was a standalone study done? Yes, a "Clinical Testing (Stand-alone Performance)" study was conducted.
• Performance Metrics: The mean Dice coefficient was 0.928 (95% CI: [0.925, 0.931]) and the Hausdorff distance was 2.171 (95% CI: [1.097, 3.245]).

VII. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. The ground truth for the test datasets was established by three expert radiologists through independent annotation followed by a majority rule approach with discussion for ties.

VIII. Sample Size for Training Set

• As detailed in Section II, the total training sample size was 1457 cases (748 from Korea, 709 from US).

IX. How the Ground Truth for the Training Set Was Established

The document states: "Study Population Dataset: The Study Population Dataset is the same as algorithm development dataset." It then describes how the "ground truth produced by three expert radiologists" was used for the "performance check test" on the segmentation algorithm, implying a similar ground truthing process for the development/training data as was used for the test set. Therefore, it is implied that the ground truth for the training set was also established by expert consensus of radiologists, similar to the test set, though the specifics of the number of radiologists and adjudication method for the training set itself are not separately detailed from the general "ground truth produced by three expert radiologists."

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