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Rapid DeltaFuse 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 images.

Data and images are acquired through DICOM compliant imaging devices.

Rapid DeltaFuse provides both viewing and analysis capabilities for imaging datasets acquired with Non-Contrast CT (NCCT) images.

The CT analysis includes NCCT maps showing areas of hypodense and hyperdense tissue including overlays of time differentiated scans of the same patient.

Rapid DeltaFuse is intended for use for adults.

Rapid DeltaFuse (DF) is a Software as a Medical Device (SaMD) image processing module and is part of the Rapid Platform. It provides visualization of time differentiated neuro hyperdense and hypodense tissue from Non-Contrast CT (NCCT) images.

Rapid DF is integrated into the Rapid Platform which provides common functions and services to support image processing modules such as DICOM filtering and job and interface management along with external facing cyber security controls. The Integrated Module and Platform can be installed on-premises within customer's infrastructure behind their firewall or in a hybrid on-premises/cloud configuration. The Rapid Platform accepts DICOM images and, upon processing, returns the processed DICOM images to the source imaging modality or PACS.

The provided FDA 510(k) clearance letter for Rapid DeltaFuse describes the acceptance criteria and the study that proves the device meets those criteria, though some details are absent.

Here's a breakdown of the information found in the document, structured according to your request:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated in a quantified manner as a target. Instead, the document describes the type of performance evaluated and the result obtained.

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

• Sample Size for Test Set: 14 cases were used for the co-registration analysis. The sample size for other verification and validation testing is not specified.
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective).

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

• This information is not provided in the document. The document refers to "performance validation testing" and "software verification and validation testing" but does not detail the involvement of human experts or their qualifications for establishing ground truth.

4. Adjudication Method for the Test Set

• This information is not provided in the document.

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

• No MRMC comparative effectiveness study was reported. The document focuses on the software's performance (e.g., DICE coefficient for co-registration) rather than its impact on human reader performance.

6. Standalone (Algorithm Only) Performance Study

• Yes, a standalone performance study was done. The reported DICE coefficients (0.94 and 0.95) are measures of the algorithm's performance in co-registration and overlay addition, independent of human interaction.

7. Type of Ground Truth Used

• The document implies that the ground truth for co-registration and overlay performance was likely established through a reference standard based on accurate image alignment and feature identification, against which the algorithm's output (DICOM images with overlays) was compared. The exact method of establishing this reference standard (e.g., manual expert annotation, a different validated algorithm output) is not explicitly stated.

8. Sample Size for the Training Set

• The document does not specify the sample size used for training the Rapid DeltaFuse algorithm.

9. How Ground Truth for the Training Set Was Established

• The document does not specify how the ground truth for the training set was established.

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The Rapid MLS software device is designed to measure the midline shift of the brain from a NCCT acquisition and report the measurements. Rapid MLS analyzes adult cases using machine learning algorithms to identify locations and measurements of the expected brain midline and any shift which may have occurred. The Rapid MLS device provides the user with annotated images showing measurements. Its results are not intended to be used on a stand-alone basis for clinical decision-making or otherwise preclude clinical assessment of NCCT cases.

Rapid MLS software device is a radiological computer-assisted image processing software device using AI/ML. The Rapid MLS device is a non-contrast CT (NCCT) processing module which operates within the integrated Rapid Platform to provide a measurement of the brain midline. The Rapid MLS software analyzes input NCCT images that are provided in DICOM format and provides both a visual output containing a color overlay image displaying the difference between the expected and indicated brain midline at the Foramen of Monro; and a text file output (json format) containing the quantitative measurement.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) Clearance Letter for Rapid MLS (K243378):

Acceptance Criteria and Device Performance

The core of the acceptance criteria for Rapid MLS appears to be its ability to measure midline shift with an accuracy comparable to or better than human experts.

Study Details

Here's a detailed summary of the study proving the device meets the acceptance criteria:

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

   • Sample Size: 153 NCCT cases
   • Data Provenance:
     • Country of Origin: Not explicitly stated for all cases, but sourced from 13 sites (2 OUS [Outside US], 11 US). This indicates a mix of international and domestic data.
     • Retrospective or Prospective: Not explicitly stated, but the description of "validation data was sourced and blinded independent of the development cases" and "demographic split for age and gender... used to test for broad demographic representation and avoidance of overlap bias with development" suggests these were pre-existing, retrospectively collected cases (i.e., not prospectively collected for this trial).
     • Scanner Manufacturers: Mixed from GE, Philips, Toshiba, and Siemens scanners.
     • Demographics: Male: 44%, Female: 56%, Age Range: 26-93 years.

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

   • Number of Experts: 3 experts
   • Qualifications of Experts: Not explicitly stated, but the context implies they are medical professionals who use midline shift as a clinical metric, likely radiologists or neurologists.

3. Adjudication Method for the Test Set:

   • Method: Expert consensus was used to establish ground truth. The document states "ground truth established by 3 experts." This implies a consensus approach, but the specific method (e.g., majority vote, discussion to consensus) is not detailed. The "experts average pairwise MAE" suggests individual expert measurements were consolidated. It is not explicitly stated whether a 2+1 or 3+1 method was used, but given there were 3 experts, it's likely they reached a consensus view.

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

   • The study does compare the device's performance to human experts, but it's not explicitly described as a traditional MRMC comparative effectiveness study where human readers use the AI and then are compared to human readers without AI.
   • Effect Size of Human Readers Improvement with AI vs. Without AI Assistance: This specific comparison (human with AI vs. human without AI) was not the primary focus of the reported performance study. The study primarily evaluated the standalone performance of the AI in comparison to expert measurements (i.e., the AI as a "reader" vs. expert "readers"). The "Indications for Use" state that the results "are not intended to be used on a stand-alone basis for clinical decision-making or otherwise preclude clinical assessment of NCCT cases," implying it's an assistive tool, but the study described measures the AI's accuracy against experts, not the improvement of experts with the AI.

5. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:

   • Yes. The document states, "Final device validation included standalone performance validation." The reported MAE of the Rapid MLS and its comparison to the experts' pairwise MAE directly reflect its standalone performance.

6. The Type of Ground Truth Used:

   • Ground Truth Type: Expert Consensus from the 3 experts.

7. The Sample Size for the Training Set:

   • Training Set Sample Size: 138 cases

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

   • The document implies that the "Algorithm development was performed using 162 cases from multiple sites; training included 24 cases for validation and 138 for training." While it doesn't explicitly state how ground truth was established for the training set, it is highly probable that a similar (if not identical) process involving human expert annotation was used, given the reliance on expert consensus for the validation/test set. The development cases were chosen to cover 0-18.6 mm offsets from expected midline, indicating a process of identifying and labeling the midline shift in these cases.

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Rapid Neuro3D (RN3D) is an image analysis software for imaging datasets acquired with conventional CT Angiography (CTA) from the aortic arch to the vertex of the head. The module removes bone, tissue, and venous vessels, providing a 3D and 2D visualization of the neurovasculature supplying arterial blood to the brain.

Outputs of the device include 3D rotational maximum intensity projections (MIPS), volume renders (VR), along with the curved planar reformation (CPR) of the isolated left and right internal carotid and vertebral arteries.

Rapid Neuro3D is designed to support the physician in confirming the presence or absence of physician-identified lesions and evaluation, documentation, and follow-up of any such lesion and treatment planning.

Its results are not intended to be used on a stand-alone basis for clinical decision-making or otherwise preclude clinical assessment.

RN3D is indicated for adults.

Precautions/Exclusions:

o Series containing excessive patient motion or metal implants may impact module output quality.

o The RN3D module will not process series that meet the following module exclusion criteria:

• Series containing inadequate contrast agent ( 400mm.
- 2) Z FOV (cranio-caudal transverse anatomical coverage) 1.0 mm.
- 4) Z slice spacing of 1.25 mm.
- 5) slice thickness > 1.5mm.
- 6) data acquired at x-ray tube voltage 150kVp.

Rapid Neuro 3D (RN3D) is a Software as a Medical Device (SaMD) image processing module and is part of the Rapid Platform. It allows for visualization of arterial vessels of the head and neck and identifies and segments arteries of interest in patient CTA exams.

The Rapid Platform provides common functions and services to support image processing modules such as DICOM filtering and job and interface management. The Rapid Platform can be installed on-premises within customer's infrastructure behind their firewall or in a hybrid on-premises/cloud configuration. The software can be installed on dedicated hardware or a virtual machine. The Rapid Platform accepts DICOM images and, upon processing, returns the processed DICOM images to the source imaging modality or PACS.

The RN3D image processing module is based on pre-trained artificial intelligence / machinelearning models and facilitates a 3D visualization of the neurovasculature supplying arterial blood to the brain. The module analyzes input CTA images in DICOM format and provides a corresponding DICOM series output that can be used by a DICOM viewer, clinical workstations. and PACS systems.

Here's a breakdown of the acceptance criteria and study details for the Rapid Neuro3D device, extracted from the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the primary endpoints of the studies.

2. Sample Sizes and Data Provenance for the Test Set

• Segmentation Quality Study:

  • Sample Size: 120 CTA cases from 115 patients (65 female; 50 male; aged from 27 to 90+).
  • Data Provenance: 104 US, 16 OUS (Outside US).
  • Retrospective/Prospective: Not explicitly stated, but the mention of a "test dataset was independent from the data used during model training" suggests a retrospective nature.

• Segmentation Accuracy Study:

  • Sample Size: 50 CTA cases from 48 patients (24 female; 24 male; aged from 27 to 90+).
  • Data Provenance: 43 US, 7 OUS.
  • Retrospective/Prospective: Not explicitly stated, but the mention of a "test dataset was independent from the data used during model training" suggests a retrospective nature.

3. Number and Qualifications of Experts for Ground Truth

• Number of Experts: Up to three clinical experts (for the segmentation quality study). The document does not specify if the same number of experts were used for the segmentation accuracy study's ground truth.
• Qualifications: "Clinical experts." No further specific qualifications (e.g., years of experience, subspecialty) are provided in the text.

4. Adjudication Method for the Test Set

• Method: "Consensus of up to three clinical experts" was used to determine clinical accuracy in the segmentation quality study. For the segmentation accuracy study, "ground truth" was established, and for reproducibility it mentions "reproducibility (of ground truths)" implying a process, but a specific adjudication method like 2+1 or 3+1 isn't explicitly detailed for the accuracy study.

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

• Was it done? No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not explicitly described or reported. The studies described focus on the standalone performance of the AI device against expert consensus or defined ground truth.

6. Standalone (Algorithm Only) Performance Study

• Was it done? Yes. Both the "Segmentation Quality Study" and the "Segmentation Accuracy Study" evaluated the standalone performance of the Rapid Neuro3D algorithm. The outputs were compared against source DICOM images and established ground truth, respectively, without mentioning human-in-the-loop performance improvement.

7. Type of Ground Truth Used

• Segmentation Quality Study: Expert consensus against source DICOM images.
• Segmentation Accuracy Study: For the extracranial region and CPR, it was compared against "ground truth" (presumably expert annotated regions). For the intracranial region, it was compared to the "predicate device" performance, implying the predicate served as a reference for substantial equivalence in that specific context. The document also mentions "reproducibility (of ground truths)," indicating expert delineations.

8. Sample Size for the Training Set

• The document states, "The test dataset was independent from the data used during model training," but it does not provide the specific sample size for the training set.

9. How Ground Truth for the Training Set Was Established

• The document does not provide details on how ground truth was established for the training set. It only mentions that the test set data was independent from the training data.

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Rapid ASPECTS is a computer-aided diagnosis (CADx) software device used to assist the clinician in the assessment and characterization of brain tissue abnormalities using CT image data. The Software automatically registers images and segments and analyzes ASPECTS Regions of Interest (ROIs). Rapid ASPECTS extracts image data for the ROI(s) to provide analysis and computer analytics based on morphological characteristics. The imaging features are then synthesized by an artificial intelligence algorithm into a single ASPECT (Alberta Stroke Program Early CT) Score. Rapid ASPECTS is indicated for evaluation of adult patients presenting for diagnostic imaging workup, for evaluation of extent of disease. Extent of disease refers to the number of ASPECTS regions affected which is reflected in the total score. This device provides information that may be useful in the characterization of early ischemic brain tissue injury for ischemic stroke patient (typically

The Rapid platform is Software as a Medical Device (SaMD), which provides for the visualization and study of changes in tissue and vasculature using digital images captured by diagnostic imaging systems including CT (Computed Tomography), CTA (CT Angiography), MRI (Magnetic Resonance Imaging) and MRA (MR Angiography) as an aid to physician diagnosis. Rapid can be installed on a customer's Server or it can be accessed online as a virtual system. It provides viewing, quantification, analysis, and reporting capabilities. The Rapid platform has multiple modules a clinician may elect to run and provide analysis for decision making.

Rapid ASPECTS provides an automatic ASPECT Score based on the case input file for the physician. The score includes which ASPECT regions are identified based on regional imaging features derived from Non-Contrast Computed Tomography (NCCT) brain image data. The results are generated based on the Alberta Stroke Program Early CT Score (ASPECTS) guidelines and provided to the clinician for review and verification. At the discretion of the clinician, the scores may be adjusted based on other clinical factors the clinician may integrate though the Rapid Platform Interface.

The ASPECTS software module processing pipeline performs four major tasks:

• Orientation and spatial normalization of the input imaging data (rigid registration/alignment with anatomical template).
• Delineation of pre-defined regions of interest on the normalized input data and computing numerical values characterizing underlying voxel values within those regions.
• Identification and highlighting previous/old stroke areas along with areas of early ischemic change; and
• Labeling of these delineated regions and providing a summary score reflecting the number of regions with early ischemic change as per ASPECTS guidelines.

Subsequently. the system notifies the physician of the availability of the ASPECT Score with an overlayed atlas. The ASPECTS information is then available for the physician to review and edit prior to sending the data to a PACS or Workstation. The final summary score together with the regions selected and underlying voxel values are then sent to the Picture Archiving and Communication System (PACS) to become a part of the permanent patient medical record.

The provided text describes the acceptance criteria and the study that proves the device meets those criteria for iSchemaView, Inc.'s Rapid ASPECTS (v3) CADx software.

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria for Rapid ASPECTS (v3)

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

• Standalone Performance Test Set Sample Size: 88 scans (from the "Suspected Stroke" category)
• Reader Improvement Test Set Sample Size: 102 scans (including 88 "Suspected Stroke" and 14 "Stroke Mimic" cases)
• Supplemental Confounder/Mimic Sensitivity Assessment Sample Size: This involved a separate set of supplemental data. While the number of scans directly used for this specific assessment is not explicitly stated as a single total, the types and counts of cases are listed: Abscess (3), Dural AVF (4), Hydrocephalus (4), Hypertensive Encephalopathy (2), Isodense SDH (4), Multiple Sclerosis (3), and Traumatic Brain Injury (3). These cases were reviewed for 115 reads.
• Data Provenance: The data included both US (79.41% for the reader improvement study test set) and OUS (20.59%) cases. It's a combination of different scanner manufacturers: GE (23), Siemens (28), Cannon/Toshiba (22), and Philips (29). The description suggests it is retrospective data, as it describes a collection of existing scans.

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

The ground truth for both the standalone performance and the reader improvement study was established using:

• Three experts to establish the reference standard for the standalone performance study.
• The clinical reader study involved one expert neuroradiologist and five non-expert typical readers. While the specific qualifications for "typical readers" aren't detailed, the text implies they represent general clinicians who evaluate CT scans in community hospitals and primary stroke centers. The neuroradiologist is explicitly identified as an expert.

4. Adjudication Method for the Test Set

The document explicitly states: "The primary reader improvement endpoint is to demonstrate that reader scoring of the 10 ASPECT regions is more closely aligned with the reference standard when read in conjunction with Rapid ASPECTS than without Rapid ASPECTS." And for standalone performance: "The percent agreement of Rapid ASPECTS to the reference at the ASPECTS region level and at the scan level is 82.8%. Both are comparable (overlapping CI) to the pairwise agreement between any two of the three experts."

This indicates that a reference standard was established by experts. While the specific method of reaching this reference standard (e.g., 2+1, consensus) is not explicitly detailed, the mention of "pairwise agreement between any two of the three experts" for the standalone performance suggests that the ground truth was derived from a consensus or adjudicated process involving these three experts.

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

Yes, a multi-reader multi-case (MRMC) comparative effectiveness study was done. This is referred to as the "Clinical Validation Reader Improvement" study.

• Effect Size of Human Readers Improve with AI vs. without AI assistance: The fixed effect of the Rapid assist increases the percent agreement on average by about 0.02. Specifically, agreement increases from 82% without assistance to 84% with assistance (excluding the expert). When including the non-expert readers, the average agreement increases from 80.4% without assistance to 83.3% with assistance.
  • The benefit was most substantial among the non-neuroradiologist expert readers.
  • The system allowed non-expert physicians to perform at an "expert-like level."
  • There was no significant impact (positive or negative) on the score of the expert neuroradiologist.

6. Standalone Performance (i.e., algorithm only without human-in-the-loop performance)

Yes, a standalone performance study was done.

• Results: The percent agreement of Rapid ASPECTS to the reference at both the ASPECTS region level and at the scan level was reported as 82.8%. This was found to be comparable (with overlapping confidence intervals) to the pairwise agreement between any two of the three experts who established the ground truth.

7. The Type of Ground Truth Used

The ground truth used was expert consensus / expert reading. It was established by a panel of experts. The text refers to "the reference" established by "three experts" for the standalone performance and a "reference standard" for the reader improvement study.

8. The Sample Size for the Training Set

The document does not explicitly state the sample size for the training set. It only describes the test sets used for validation.

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

As the training set sample size is not provided, the method for establishing its ground truth is also not specified in the provided text.

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Rapid is an image processing software package to be used by trained professionals,including but not limited to physicians and medical technicians. The software runs ona standard off-the-shelf computer or a virtual platform, such as VMware, and can be used to perform image viewing, processing and analysis of images. Data and images are acquired through DICOM compliant imaging devices.

Rapid provides both viewing and analysis capabilities for functional and dynamic imaging datasets acquired with CT Perfusion (CTP). CT Angiography (CTA), and MRI including a Diffusion Weighted MRI (DWI) Module and a Dynamic Analysis Module (dynamic contrast-enhanced imaging data for MRI and CT).

The CT analysis includes NCCT maps showing areas of hypodense and hyperdense tissue.

The DWI Module is used to visualize local water diffusion properties from the analysis of diffusion weighted MRI data.

The Dynamic Analysis Module is used for visualization and analysis of dynamic imaging data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to tissue flow (perfusion) and tissue blood volume.

Rapid CT-Perfusion and Rapid MR-Perfusion can be used by physicians to aid in the selection of acute stroke patients (with known occlusion of the intracranial internal carotid artery or proximal middle cerebral artery)Instructions for the use of contrast agents for this indication can be found in Appendix A of the User's Manual. Additional information for safe and effective drug use is available inthe product-specific iodinated CT and gadolinium-based MR contrast drug labeling.

In addition to the Rapid imaging criteria, patients must meet the clinical requirements for thrombectomy, as assessed by the physician, and have none of the following contraindications or exclusions:

• · Bolus Quality: absent or inadequate bolus.
• · Patient Motion: excessive motion leading to artifacts that make the scan technically inadequate
• Presence of hemorrhage

Rapid is a software package that provides for the visualization and study of changes in tissue using digital images captured by diagnostic imaging systems including CT (Computed Tomography) and MRI (Magnetic Image Resonance), as an aid to physician diagnosis. Rapid can be installed on a customer's Server or it can be accessed online as a virtual system. It provides viewing, quantification, analysis and reporting capabilities.

Rapid is a Software as a Medical Device (SaMD) consisting of one or more Rapid Servers (dedicated or virtual) in on-premises or hybrid (on-premises/cloud) configurations. The Rapid Server is an image processing engine that connects to a hospital LAN, or inside the Hospital Firewall in the on-premises configuration or in conjunction with a secure link to the cloud in the hybrid configuration. It can be a dedicated Rapid Server or a VM Rapid appliance, which is a virtualized Rapid Server that runs on a dedicated server.

Rapid is designed to streamline medical image processing tasks that are time consuming and fatiguing in routine patient workup. Once Rapid is installed it operates with minimal user interaction. Once the CT (NCCT, CT, CTA) or MR (MR, MRA) data are acquired, the CT or MRI console operator selects Rapid as the target for the DICOM images, and then the operator selects which study/series data to be sent to Rapid. Based on the type of incoming DICOM data. Rapid will identify the data set scanning modality and determine the suitable processing module. The Rapid platform is a central control unit which coordinates the execution image processing modules which support various analysis methods used in clinical practice today.

Here's an analysis of the provided text to fulfill your request, noting that the document is an FDA 510(k) clearance letter and summary, which typically focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed de novo device performance study. Therefore, some of the requested information (like specific effect sizes from MRMC studies or detailed ground truth establishment for a training set) might not be explicitly present if the submission didn't require entirely new clinical performance data for clearance.

Key Observation from the Document:
The document (K233512) is a 510(k) summary for iSchemaView Rapid (6.0), claiming substantial equivalence to a previously cleared predicate device, Rapid (K213165). The primary change appears to be an "extension of installation in a hybrid configuration (on-premises and hybrid)." This implies that extensive new clinical performance studies for the core functionality may not have been required, as the device is deemed "as safe and effective as the previously cleared Rapid (K213165) with an extension of installation in a hybrid configuration."

Given this, the "acceptance criteria" and "study that proves the device meets the acceptance criteria" are largely framed around demonstrating equivalence to the predicate and ensuring the new configuration doesn't introduce new safety or effectiveness concerns.

Acceptance Criteria and Device Performance (Based on the provided document)

Since this is a 510(k) submission for substantial equivalence based on a predicate, the "acceptance criteria" are implied to be that the device performs equivalently to the predicate and any new features (like hybrid configuration) do not negatively impact safety or effectiveness. The document highlights software verification and validation as the primary means of demonstrating compliance.

1. Table of Acceptance Criteria and Reported Device Performance

• Image viewing, processing, and analysis of CT/MRI images for functional and dynamic imaging datasets.
• Specific modules: CT-Perfusion, MR-Perfusion, DWI, Dynamic Analysis, NCCT maps (hypodense/hyperdense tissue), CTA.
• Aid in selection of acute stroke patients (with known occlusion of intracranial ICA or proximal MCA).
• Calculation of parameters related to tissue flow (perfusion) and tissue blood volume. | "Rapid has the same intended use and similar indications, technological characteristics and principles of operation as its predicate devices."
  "Rapid is as safe and effective as the previously cleared Rapid (K213165) with an extension of installation in a hybrid configuration..." |
  | Technical Compliance:
• DICOM compliance.
• Operates on standard off-the-shelf computers or virtual platforms.
• Handles DICOM medical image data (CT, MRI) from various sources.
• Secure communication protocols (SMTP with security extensions, VPN, SSH). | "Rapid complies with DICOM (Digital Imaging and Communications in Medicine) - Developed by the American College of Radiology and the National Electrical Manufacturers Association. NEMA PS 3.1 - 3.20."
  "Rapid is a DICOM-compliant PACS software..."
  "Rapid runs on standard 'off-the-shelf' computer and networking hardware."
  "Rapid generally connects to the infrastructure of the medical partner... Rapid uses a SMTP protocol with security extensions to provide secure communications."
  "Available types of connection include VPN (Virtual Private Network - RFC2401 and RFC4301 Standards) Tunnel and SSH (Secure Shell)." |
  | Performance Accuracy & Reliability:
• Accurate representation of key processing parameters.
• Handles clinically relevant parameters and perturbations.
• Meets all design requirements and specifications. | "iSchemaView conducted extensive performance validation testing and software verification and validation testing of the Rapid system."
  "This performance validation testing demonstrated that the Rapid system provides accurate representation of key processing parameters under a range of clinically relevant parameters and perturbations associated with the intended use of the software."
  "Software performance, validation and verification testing demonstrated that the Rapid system met all design requirements and specifications."
  "The Rapid System performance has been validated with phantom and case data." |
  | Safety & Effectiveness (no new issues compared to predicate):
• Compliance with QSR (21 CFR Part 820.30).
• Risk management (EN ISO 14971:2019).
• Software lifecycle processes (IEC 62304:2016).
• Usability engineering (IEC 62366:2015). | "Rapid has been designed, verified and validated in compliance with 21 CFR, Part 820.30 requirements. The device has been designed to meet the requirements associated with EN ISO 14971:2019 (risk management)."
  "Rapid raises no new issues of safety or effectiveness compared to Rapid (K2131650), as demonstrated by the testing conducted with Rapid." |

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

The document mentions "The Rapid System performance has been validated with phantom and case data." However, it does not specify the sample size for the test set of "case data" or "phantom data", nor does it specify the country of origin or whether the data was retrospective or prospective. For a 510(k), particularly one proving substantial equivalence to a predicate, new large-scale clinical studies are not always required if software verification and validation suffice, as implied here.

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

The document states, "The primary users of Rapid software are medical imaging professionals who analyze tissue using CT or MRI images." However, it does not specify the number of experts used to establish ground truth for the test set, nor does it provide their specific qualifications (e.g., number of years of experience, specific board certifications). It only generically refers to "trained professionals, including but not limited to physicians and medical technicians."

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1) used for the test set.

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

The document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done comparing human readers with AI vs. without AI assistance, nor does it state an effect size for such an improvement. The focus is on the device's standalone performance and its equivalence to the predicate.

6. If a Standalone Performance Study (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, the document implies that a standalone performance evaluation of the algorithm's core processing capabilities was conducted. It states: "iSchemaView conducted extensive performance validation testing and software verification and validation testing of the Rapid system. This performance validation testing demonstrated that the Rapid system provides accurate representation of key processing parameters under a range of clinically relevant parameters and perturbations associated with the intended use of the software." This refers to the algorithm's performance in processing images and generating analyses.

7. The Type of Ground Truth Used

The document states, "The Rapid System performance has been validated with phantom and case data." This suggests that the ground truth for "phantom data" would be known physical or simulated values. For "case data," the document does not explicitly state the type of ground truth, such as expert consensus, pathology, or outcomes data. However, given the context of stroke patient selection, clinical outcomes or expert consensus on imaging findings would typically be relevant for such applications.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set. As this is a 510(k) for an updated version of an existing device, it's possible that the training data for the core AI components was part of earlier development and was not re-evaluated for this specific submission, or that detailed training data was not a required element for this type of substantial equivalence claim.

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

The document does not provide any information on how the ground truth for the training set was established.

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Rapid SDH is a radiological computer aided triage and notification software indicated for use in the triage and notification of hemispheric SDH in non-enhanced head images. The device is intended to assist trained radiologists in workflow triage by providing notification of suspected findings of hemispheric Subdural Hemorrhage (SDH) in head CT images. Rapid SDH uses an artificial intelligence algorithm to analyze images and highlight cases with suspected hemispheric SDH on a server or standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected hemispheric SDH findings include compressed preview images, that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device.

The results of Rapid SDH are intended to be used in conjunction with other patient information and based on professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

Rapid SDH is a radiological computer-assisted triage and notification software device. The Rapid SDH module is a Non-Contrast Computed Tomography (NCCT) processing module which operates within the integrated Rapid Platform to provide triage and notification of suspected hemispheric sub-dural hemorrhage (SDH). The Rapid SDH module is an Al/ML module. The output of the module is a priority notification to clinicians indicating the suspicion of SDH based on positive findings. The Rapid SDH module uses the basic services supplied by the Rapid Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) summary for iSchemaView, Inc.'s Rapid SDH:

Executive Summary of Device Purpose:
Rapid SDH is a radiological computer-aided triage and notification software that uses an AI algorithm to identify suspected hemispheric Subdural Hemorrhage (SDH) in non-enhanced head CT images. Its primary function is to assist radiologists in workflow triage by providing rapid notifications, not for diagnostic purposes.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: 310 samples (147 positive cases, 163 negative cases).
• Data Provenance: Retrospective, multinational study. Specific countries are not listed, but various sites are named (e.g., Gradient, Riverside Regional Medical Center, Image Core Lab, Augusta University Medical Center, Ascension, D3, Segmed, Baptist, Hospital de Clinicas de POA, Stanford CA, Ospedale Regionale di Lugano, NYU, Flagler Hospital, MUSC).

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: Three (3)
• Qualifications of Experts: Neuro-radiologists. No further details on years of experience or other specific qualifications are provided in this document.

4. Adjudication Method for the Test Set

The adjudication method used to establish ground truth is not explicitly stated in the provided document beyond "Truth was established using three (3) expert neuro-radiologists." Common methods like 2+1 or 3+1 (where dissenting opinions require a tie-breaker or consensus review) are not detailed. It implies a consensus approach among the three experts.

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

• Was an MRMC study done? The document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to evaluate how human readers improve with AI vs. without AI assistance. The study described focuses on the standalone performance of the AI algorithm.
• Effect size of human reader improvement: Not applicable, as no MRMC study comparing human readers with and without AI assistance was reported.

6. Standalone Performance Study (Algorithm Only)

• Was a standalone study done? Yes, the performance data presented is for the standalone (algorithm-only) performance of the Rapid SDH software in identifying SDH in CT scans. The primary endpoints (sensitivity, specificity, AUC) and secondary endpoint (processing time) are all metrics of the algorithm's performance without human intervention in the loop for the performance evaluation itself.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. The document states: "Truth was established using three (3) expert neuro-radiologists." This indicates that the ground truth labels for the presence or absence of SDH were determined by the agreement of these medical professionals.

8. Sample Size for the Training Set

• The document does not specify the sample size used for the training set. It only describes the test set used for performance validation.

9. How Ground Truth for the Training Set Was Established

• The document does not detail how the ground truth for the training set was established. It only describes the ground truth establishment for the test set. Given it's an AI/ML module, it's highly likely that a similar expert review process would have been used for training data, but it's not explicitly stated.

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Rapid Aneurysm Triage and Notification (ANRTN) is a radiological computer-assisted triage and notification software device for analysis of CT images of the head. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and prioritizing studies with suspected saccular aneurysms during routine patient care. Rapid ANRTN uses an artificial intelligence algorithm to analyze images and highlight studies with suspected saccular aneurysms in a standalone application for study list prioritization or triage in parallel to ongoing standard of care. The device generates compressed preview images that are meant for informational purposes only and not intended for diagnostic use. The device does not alter the original medical image and is not intended to be used as a diagnostic device. Analyzed images are available for review through the PACS, email and mobile application. When viewed the images are for informational purposes only and not for diagnostic use. The results of Rapid ANRTN, in conjunction with other clinical information and professional judgment, are to be used to assist with triage/prioritization of saccular aneurysm cases. Radiologists who read the original medical images are responsible for the diagnostic decision. Rapid ANRTN is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

Rapid ANRT is limited to detecting saccular aneurysms at least 4mm in diameter in adults.

Rapid ANRTN software device is a radiological computer-assisted image processing software device. The Rapid ANRTN device is a CTA processing module which operates within the integrated Rapid Platform to determine the suspicion of head saccular aneurysm(s). The ANRTN software analyzes input CTA images that are provided in DICOM format and provides notification of suspected saccular aneurysm(s) and a non-diagnostic, compressed image for preview. Rapid ANRTN is an AI/ML image processing module which integrates within the Rapid Platform.

The provided text describes the acceptance criteria and the study that proves the device (Rapid Aneurysm Triage and Notification - Rapid ANRTN) meets these criteria.

Here's the breakdown of the requested information:

1. A table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance

• Test Set Sample Size: 266 CTA cases (151 positive for aneurysm, 115 negative).
• Data Provenance:
  • Country of Origin: Not explicitly stated in the provided text.
  • Retrospective or Prospective: Not explicitly stated, but the mention of cases "obtained from Siemens, GE, Toshiba, and Philips scanners" and "698 (633 training, 65 validation) CTA cases from multiple sites" suggests a retrospective collection of existing imaging data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of Experts: 3 experts.
• Qualifications of Experts: Not explicitly stated beyond "experts." It is typically assumed these are trained medical professionals (e.g., radiologists) with relevant experience, but specific qualifications are not detailed in the provided text.

4. Adjudication method for the test set

• Adjudication Method: "Ground truth established by 3 experts." This implies a consensus-based approach, but the specific adjudication method (e.g., majority vote, specific tie-breaking rules, or if all 3 had to agree) is not explicitly detailed (e.g., 2+1, 3+1). It likely refers to a consensus reading among the three experts.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly stated or described. The study focused on the standalone performance of the algorithm. The device's intended use is to "assist hospital networks and trained radiologists in workflow triage," implying an assistive role to humans, but the provided data only shows the algorithm's performance, not human performance with and without assistance.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

• Standalone Performance: Yes, a standalone performance validation was done. The text explicitly states: "Final device validation included standalone performance validation." and "This performance validation testing demonstrated the Rapid ANRTN device provides accurate representation of key processing parameters under a range of clinically relevant perturbations associated with the intended use of the software."

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Type of Ground Truth: Expert consensus. The text states, "ground truth established by 3 experts."

8. The sample size for the training set

• Training Set Sample Size: 633 CTA cases. (The broader algorithm development dataset included 698 total, split into 633 training and 65 validation cases, with the 266 cases being the final performance validation set).

9. How the ground truth for the training set was established

• Ground Truth Establishment for Training Set: The text states, "Algorithm development was performed using 698 (633training, 65 validation) CTA cases from multiple sites." While it mentions the cases were "selected [to] covered a wide range of suspected saccular aneurysms," the specific method for establishing ground truth for the training set (e.g., expert review, clinical reports, or a combination) is not explicitly detailed in the provided document. It is implied, but not stated, that a similar expert review process would have been used as for the test set.

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Rapid NCCT Stroke is a radiological computer aided triage and notification software indicated for use in the analysis of (1) nonenhanced head CT (NCCT) images. The device is intended to assist hospital networks and trained clinicians in workflow triage by flagging and communicating suspected positive findings of (1) head CT images for Intracranial Hemorrhage (ICH) and (2) NCCT large vessel occlusion (LVO) of the ICA and MCA-M1.

Rapid NCCT Stroke uses an artificial intelligence algorithm to analyze images and highlight cases with detected (1) ICH or (2) NCCT LVO on the Rapid server on premise or in the cloud in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected ICH or LVO findings via PACS, email or mobile device. Notifications include compressed preview images that are meant for informational purposes only, and are not intended for diagnostic use beyond notification.

The device does not alter the original medical image, and it is not intended to be used as a primary diagnostic device. The results of Rapid NCCT Stroke are intended to be used in conjunction with other patient information and based on professional judgment to assist with triage/prioritization of medical images. Notified clinicians are ultimately responsible for reviewing full images per the standard of care. Rapid NCCT Stroke is for Adults only.

Rapid NCCT Stroke (RNS) is a radiological computer-assisted triage and notification software device. RNS is a non-enhanced CT (NCCT) processing module which operates within the integrated Rapid Platform to provide triage and notification of suspected intracranial hemorrhage (ICH) and NCCT Large Vessel Occlusion (LVO) of the ICA and MCA-M1. The RNS is an AI/ML SaMD. The output of the module is a priority notification to clinicians indicating the suspicion of ICH or NCCT LVO. ICH analysis uses the ICH Algorithm to identify findings within the ICH algorithm; and the NCCT LVO suspicion uses the combined analysis of the ASPECTS and Hyperdense Vessel Sign (HVS) algorithms. The RNS module uses the basic services supplied by the Rapid Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.

The Rapid NCCT Stroke device is a radiological computer-aided triage and notification software for detecting intracranial hemorrhage (ICH) and large vessel occlusion (LVO) on non-enhanced head CT (NCCT) images.

Here's an analysis of its acceptance criteria and the study that proves it:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Sizes and Data Provenance

• Test Set Sample Size: 254 cases. These cases included:
  • ICH Positive: 26
  • LVO Positive: 115
  • Negative for ICH and LVO: 103
  • Excluded: 10 (due to age and technical inadequacy)
• Data Provenance: The study was a "retrospective, blinded, multicenter, multinational study." This indicates that the data was collected from multiple centers in various countries and that the analysis was performed on existing, pre-collected data. Specific countries are not mentioned.

3. Number of Experts and Qualifications for Ground Truth

• Ground Truth Establishment: The document mentions "expert reader truthing of the data." The specific number of experts is not explicitly stated for the ground truth establishment, but it is implied that multiple experts were involved given "expert reader truthing."
• Qualifications of Experts: The document refers to "human readers" including "neuroradiologists and general radiologists" in the context of the secondary clinical endpoints. This suggests that the experts involved in establishing ground truth would likely possess similar qualifications in radiology, with expertise in neurological imaging, to accurately identify ICH and LVO.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method like 2+1 or 3+1. It states that the ground truth was established by "expert reader truthing." This implies that a consensus or a well-defined process was used by the experts to determine the definitive diagnoses, but the specific mechanics of that process (e.g., number of readers, tie-breaking rules) are not detailed.

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

Yes, a form of MRMC comparative effectiveness study was done for LVO.

• Effect Size of Human Readers' Improvement with AI vs. without AI Assistance: The study did not directly assess how much human readers improve with AI assistance. Instead, it compared the standalone performance of the Rapid NCCT Stroke device to the performance of human readers (both general radiologists and a broader group of "all readers," which included experts) in identifying LVO.
  • Expert Non-inferiority: The device demonstrated non-inferiority to "overall readers" (experts and non-experts). The device's sensitivity was 0.635, while the sensitivity for "all readers" was 0.436. The difference in sensitivity (device vs. all readers) was 0.199 (95% CI: 0.055-0.34), indicating the device performed better than the overall human readers.
  • Non-expert Superiority: The device demonstrated superiority to "general radiologists". The device's sensitivity was 0.635, while the sensitivity for general radiologists was 0.409. The difference in sensitivity (device vs. general radiologists) was 0.226 (95% CI: 0.071-0.381), indicating the device performed better than general radiologists.
  • These results show that the standalone device performed better than human readers in terms of sensitivity for LVO detection. The study design doesn't provide an effect size for human reader improvement with AI assistance (i.e., a human-in-the-loop scenario).

6. Standalone (Algorithm Only) Performance Study

Yes, an algorithm-only standalone performance study was done.

• The reported sensitivities and specificities for ICH (Se: 0.962, Sp: 0.974) and LVO (Se: 0.635, Sp: 0.951) refer to the standalone performance of the Rapid NCCT Stroke device.
• The ICH algorithm's performance was noted to be "consistent with the ICH standalone module performance (K221456)," further confirming standalone evaluation.
• The comparison against human readers (secondary clinical endpoints) also used the device's standalone output for comparison.

7. Type of Ground Truth Used

The ground truth used was expert consensus (referred to as "expert reader truthing of the data").

8. Sample Size for the Training Set

The document does not provide the sample size for the training set. It only describes the test set used for performance validation.

9. How Ground Truth for the Training Set Was Established

The document does not provide information on how the ground truth for the training set was established. It focuses solely on the validation study and the ground truth for its test set.

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Rapid ICH is a radiological computer aided triage and notification software in the analysis of non-enhanced head CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive findings of pathologies in head CT images, for IPH, IVH, SAH, and SDH Intracranial Hemorrhages (CH).

Rapid ICH uses an artificial intelligence algorithm to analyze images and highlight cases with detected ICH on a server or standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected ICH findings. Notifications include compressed preview images, which are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not is a a diagnostic device.

The results of Rapid ICH are intended to be used in conjunction and based on professional judgment, to assist with trage /prioritization of medical images. Notified radiologists are responsible for viewing full images per the standard of care.

Rapid ICH is a radiological computer-assisted triage and notification software device. The Rapid ICH module is a non-enhanced CT (NCCT) processing module which operates within the integrated Rapid Platform to provide triage and notification of suspected intracranial hemorrhage. The Rapid ICH module is an AI/ML module. The output of the module is a priority notification to clinicians indicating the suspicion of ICH based on positive findings. The Rapid ICH module uses the basic services supplied by the Rapid Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.

Here's a breakdown of the acceptance criteria and study details for the Rapid ICH device, based on the provided text:

Acceptance Criteria and Device Performance

The primary performance goals for Rapid ICH were defined by sensitivity and specificity thresholds.
Acceptance Criteria Table and Reported Device Performance:

Study Details

2. Sample Size and Data Provenance:

• Test Set Sample Size: 314 cases (148 ICH positive, 166 ICH negative).
• Data Provenance: Retrospective, multicenter, multinational study. Specific countries are not detailed, but "multinational" implies diverse geographical origins.

3. Number of Experts and Qualifications for Ground Truth:

• Number of Experts: Not explicitly stated how many individual experts established the ground truth. The document mentions "expert reader truthing of the data," suggesting one or more experts.
• Qualifications of Experts: The document states "trained radiologists" are intended users and mentions "expert reader truthing." However, specific qualifications such as years of experience, board certification, or subspecialty are not provided for the ground truth experts.

4. Adjudication Method for the Test Set:

• The document implies ground truth was established by "expert reader truthing of the data," but does not specify an adjudication method (e.g., 2+1, 3+1, consensus review process if multiple readers were involved).

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

• No, an MRMC comparative effectiveness study was NOT mentioned for evaluating human readers' improvement with AI assistance. The study focused on the standalone performance of the AI algorithm (accuracy) and the time-to-notification benefit.

6. Standalone Performance (Algorithm Only):

• Yes, a standalone performance study was done. The reported sensitivity, specificity, and AUC values directly reflect the algorithm's performance in identifying ICH presence. The study evaluated the software's performance in identifying abnormalities, and the "time to notification" indicates the speed of the algorithm's output.

7. Type of Ground Truth Used:

• Expert Consensus: The ground truth for the test set was established through "expert reader truthing of the data." This implies a clinical expert (radiologist) determined the presence or absence of ICH.

8. Sample Size for the Training Set:

• The document states that the "minor change causing this filing, is the use of additional data for training and validation," implying the training set for this iteration of the device included more data than the predicate. However, the specific sample size of the training set is not provided in the summary.

9. How the Ground Truth for the Training Set was Established:

• Similar to the test set, the document indicates that the device was trained and validated using "retrospective case data based on expert reader truthing of the data." This suggests the ground truth for the training set was also established by expert review/diagnosis by clinical experts.

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