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uOmnispace.CT is a software for viewing, manipulating, evaluating and analyzing medical images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:

• The uOmnispace.CT Colon Analysis application is intended to provide the user a tool to enable easy visualization and efficient evaluation of CT volume data sets of the colon.
• The uOmnispace.CT Dental Application is intended to provide the user a tool to reconstruct panoramic and paraxial views of jaw.
• The uOmnispace.CT Lung Density Analysis application is intended to segment pulmonary, lobes, and airway, providing the user quantitative parameters, structure information to evaluate the lung and airway.
• The uOmnispace.CT Lung Nodule application is intended to provide the user a tool for the review and analysis of thoracic CT images, providing quantitative and characterizing information about nodules in the lung in a single study, or over the time course of several thoracic studies.
• The uOmnispace.CT Vessel Analysis application is intended to provide a tool for viewing, manipulating, and evaluating CT vascular images.
• The uOmnispace.CT Brain Perfusion application is intended to calculate the parameters such as: CBV, CBF, etc. in order to analyze functional blood flow information about a region of interest (ROI) in brain.
• The uOmnispace.CT Heart application is intended to segment heart and extract coronary artery. It also provides analysis of vascular stenosis, plaque and heart function.
• The uOmnispace.CT Calcium Scoring application is intended to identify calcifications and calculate the calcium score.
• The uOmnispace.CT Dynamic Analysis application is intended to support visualization of the CT datasets over time with the 3D/4D display modes.
• The uOmnispace.CT Bone Structure Analysis application is intended to provide visualization and labels for the ribs and spine, and support batch function for intervertebral disk.
• The uOmnispace.CT Liver Evaluation application is intended to provide processing and visualization for liver segmentation and vessel extraction. It also provides a tool for the user to perform liver separation and residual liver segments evaluation.
• The uOmnispace.CT Dual Energy is a post-processing software package that accepts UIH CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The Dual Energy application is intended to provide information on the chemical composition of the scanned body materials and/or contrast agents. Additionally, it enables images to be generated at multiple energies within the available spectrum.
• The uOmnispace.CT Cardiovascular Combined Analysis is an image analysis software package for evaluating contrast enhanced CT images. The CT Cardiovascular Combined Analysis is intended to analyze vascular and cardiac structures.It can be used in the qualitative and quantitative for the analysis of head-neck, abdomen, multi-body part combined, TAVR (Transcatheter Aortic Valve Replacement) CT data as input for the planning of cardiovascular procedures.
• The uOmnispace.CT Body Perfusion is intended to analyze blood flow information of dynamic CT images, by providing various perfusion-related parameters of the body parts.

The uOmnispace.CT is a post-processing software based on the uOmnispace platform for viewing, manipulating, evaluating and analyzing medical images, can run alone or with other advanced commercially cleared applications.

uOmnispace.CT contains the following applications:

• uOmnispace.CT Calcium Scoring
• uOmnispace.CT Lung Nodule
• uOmnispace.CT Colon Analysis
• uOmnispace.CT Lung Density Analysis
• uOmnispace.CT Dental Application
• uOmnispace.CT Bone Structure Analysis
• uOmnispace.CT Dual Energy
• uOmnispace.CT Vessel Analysis
• uOmnispace.CT Heart
• uOmnispace.CT Brain Perfusion
• uOmnispace.CT Dynamic Analysis
• uOmnispace.CT Liver Evaluation
• uOmnispace.CT Cardiovascular Combined Analysis
• uOmnispace.CT Body Perfusion

The modifications performed on the uOmnispace.CT (K233209) in this submission is due to the following changes that include:

• Add new application of Body Perfusion.
• Extend intended patient population for some applications
• Introduce deep-learning algorithm in applications of Lung Density Analysis, Vessel Analysis, Heart, Liver Evaluation and Cardiovascular Combined Analysis.

These modifications do not affect the intended use or alter the fundamental scientific technology of the device

This document describes the acceptance criteria and performance of the Medical Image Post-processing Software (uOmnispace.CT) for several AI-based segmentation algorithms, based on the provided FDA 510(k) clearance letter.

Acceptance Criteria and Reported Device Performance

Study Details for AI-Based Algorithms

The software features described in the submission are based on deep learning algorithms. The performance evaluation includes the following details for each application:

1. Lung Density Analysis (Lung Segmentation & Airway Segmentation)

• Sample size used for the test set and data provenance:

  • Sample Size: 100 subjects.
  • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It notes the test dataset comprises 100 cases of Chest CT scans covering different gender, age, and anatomical variants.

• Number of experts used to establish the ground truth for the test set and their qualifications:

  • Number of Experts: Not explicitly stated as a specific number of individual experts. The process mentions "well-trained annotators" and "a senior clinical specialist" for review and modification.
  • Qualifications: "well-trained annotators" and "a senior clinical specialist" (no further details on experience provided).

• Adjudication method for the test set:

  • Ground truth annotations are initially done by "well-trained annotators." A "senior clinical specialist" then checks and modifies these annotations to ensure correctness. This implies a form of expert review and potential consensus or single expert finalization.

• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No, an MRMC comparative effectiveness study was not done. The evaluation focuses on standalone algorithm performance against ground truth.

• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Yes, the performance testing explicitly evaluates the algorithm's output (Dice coefficient) against a reference standard (ground truth), indicating a standalone performance evaluation.

• The type of ground truth used: Expert consensus, through a process of initial annotation by trained individuals and subsequent review/modification by a senior clinical specialist.

• The sample size for the training set: Not specified in the provided document. It only states that the training data is "independent of the data used to test the algorithm."

• How the ground truth for the training set was established: Not specified in the provided document. It only mentions the training data is independent from the test data.

2. Vessel Analysis (Automatic Bone Removal - Abdomen & Limbs, Head & Neck)

• Sample size used for the test set and data provenance:

  • Sample Size: 156 subjects.
  • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It notes the test dataset comprises 156 cases of CTA scans covering different gender, age, and anatomical variants.

• Number of experts used to establish the ground truth for the test set and their qualifications:

  • Number of Experts: Not explicitly stated. The process mentions "well-trained annotators" and "a senior clinical specialist" for review and modification.
  • Qualifications: "well-trained annotators" and "a senior clinical specialist."

• Adjudication method for the test set: Similar to Lung Density Analysis, ground truth annotations are done by "well-trained annotators," with a "senior clinical specialist" checking and modifying them.

• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.

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

• The type of ground truth used: Expert consensus.

• The sample size for the training set: Not specified.

• How the ground truth for the training set was established: Not specified.

3. Heart (Coronary Artery Extraction & Heart Chamber Segmentation)

• Sample size used for the test set and data provenance:

  • Sample Size: 72 subjects.
  • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It notes the test dataset comprises 72 cases of CCTA scans covering different gender, age, and anatomical variants.

• Number of experts used to establish the ground truth for the test set and their qualifications:

  • Number of Experts: Not explicitly stated. The process mentions "well-trained annotators" and "a senior clinical specialist" for review and modification.
  • Qualifications: "well-trained annotators" and "a senior clinical specialist."

• Adjudication method for the test set: Similar to previous sections, ground truth annotations are done by "well-trained annotators," with a "senior clinical specialist" checking and modifying them.

• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.

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

• The type of ground truth used: Expert consensus.

• The sample size for the training set: Not specified.

• How the ground truth for the training set was established: Not specified.

4. Liver Evaluation (Liver, Hepatic Artery, Hepatic Portal Vein, and Hepatic Vein Segmentation)

• Sample size used for the test set and data provenance:

  • Sample Size: 74 subjects for liver and hepatic artery segmentation; 80 subjects for hepatic portal vein and hepatic vein segmentation.
  • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It notes the test datasets comprise Chest CT scans covering different gender, age, and anatomical variants.

• Number of experts used to establish the ground truth for the test set and their qualifications:

  • Number of Experts: Not explicitly stated. The process mentions "well-trained annotators" and "a senior clinical specialist" for review and modification.
  • Qualifications: "well-trained annotators" and "a senior clinical specialist."

• Adjudication method for the test set: Similar to previous sections, ground truth annotations are done by "well-trained annotators," with a "senior clinical specialist" checking and modifying them.

• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.

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

• The type of ground truth used: Expert consensus.

• The sample size for the training set: Not specified.

• How the ground truth for the training set was established: Not specified.

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The TeraRecon Neuro Algorithm is an algorithm for use by trained professionals, including but not limited to physicians, surgeons and medical clinicians.

The TeraRecon Neuro Algorithm is a standalone image processing software device that can be deployed as a Microsoft Windows executable on off-the-shelf hardware or as a containerized application (e.g., a Docker container) that runs on off-the-shelf hardware or on a cloud platform. Data and images are acquired via DICOM compliant imaging devices. DICOM results may be exported, combined with, or utilized by other DICOM-compliant systems and results.

The TeraRecon Neuro Algorithm provides analysis capabilities for functional, dynamic, and derived imaging datasets acquired with CT or MRI. It can be used for the analysis of dynamic brain perfusion image data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to brain tissue perfusion, vascular assessment, tissue blood volume, and other parametric maps with or without the ventricles included in the calculation. The algorithm also include volume reformat in various orientation, rotational MIP 3D batch while removing the skull. This "tumble view" allows qualitative review of vascular structure in direct correlation to the perfusion maps for comprehensive review.

The results of the TeraRecon Neuro Algorithm can be delivered to the end-user through image viewers such as TeraRecon's Aquarius Intuition system, TeraRecon's Eureka AI Results Explorer, TeraRecon's Eureka Clinical AI Platform, or other image viewing systems like PACS that can support DICOM results generated by the TeraRecon Neuro Algorithm.

The TeraRecon Neuro Algorithm results are designed for use by trained healthcare professionals and are intended to assist the physician in diagnosis, who is responsible for making all final patient management decisions.

The TeraRecon Neuro algorithm version 2.0.0 is a modification of the predicate device Neuro.AI Algorithm (K200750), which was a modification of the predicate device, Intuition-TDA, TVA, Parametric Mapping (which was cleared under K131447). The predicate device Intuition -TDA, TVA, Parametric Mapping is an optional module/workflow for the Intuition system (K121916). The TeraRecon Neuro algorithm is an image processing software device that can be deployed as a Microsoft Windows executable on off-the-shelf hardware or as a containerized application (e.g., Docker container) that runs on off-the-shelf hardware or on a cloud platform. The device has limited network connectivity or external medical support.

TeraRecon Neuro allows motion correction and processes, calculates and outputs brain perfusion analysis results for functional, dynamic, and derived imaging datasets acquired with CT or MRI. TeraRecon Neuro results are used for the analysis of dynamic brain perfusion image data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to brain tissue perfusion, vascular assessment and tissue blood volume.

Outputs include parametric map of measurements including time to peak (TTP), take off time (TOT), recirculation time (RT), mean transit time (MTT), blood volume (BV/CBV), blood flow (BF/CBF), time to maximum (Tmax) and penumbra/umbra maps that are derived from combinations of measurement parameters, such as mismatch maps and hypoperfusion maps with volumes and ratios, as well as 2D and 3D visualization of brain tissues and brain blood vessels (Note: Tmax, mismatch and hypoperfusion maps are only available for images of CT modality).

When TeraRecon Neuro results are used in external viewer devices such as TeraRecon's Intuition or Eureka medical devices, all the standard features offered by Intuition or Eureka are employed such as image manipulation tools like drawing the region of interest, manual or automatic segmentation of structures, tools that support creation of a report, transmitting and storing this report in digital form, and tracking historical information about the studies analyzed by the software.

The TeraRecon Neuro algorithm outputs can be used by physicians to aid in the diagnosis and for clinical decision support including treatment planning and post treatment evaluation. The software is not intended to replace the skill and judgment of a qualified medical practitioner and should only be used by individuals that have been trained in the software's function, capabilities and limitations. The device is intended to provide supporting analytical tools to a physician, to speed decision-making and to improve communication, but the physician's judgment is paramount, and it is normal practice for physicians to validate theories and treatment decisions multiple ways before proceeding with a risky course of patient management.

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

Study Details

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

The document does not explicitly state the numerical sample size for the test set used in the qualitative clinical user evaluation or the quantitative Tmax measurement accuracy study. It refers to "comparison maps generated by the subject device, the predicate device and two additional reference devices." Without specific numbers, it's impossible to determine the precise size of the test set cases.

Regarding data provenance, the document does not provide details on the country of origin or whether the data was retrospective or prospective.

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

• Number of Experts: One expert was used.
• Qualifications: Dr. Robert Falk, MD. No additional details about his specific experience or sub-specialty (e.g., radiologist with X years of experience) are provided in the text.

4. Adjudication Method for the Test Set

The adjudication method used for the clinical user evaluation was not explicitly specified as 2+1, 3+1, or any other formal method. The study involved a single evaluator (Dr. Robert Falk, MD) who was "asked to confirm through qualitative assessment." This suggests a single-expert review, rather than a multi-expert adjudication process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of Improvement with AI vs. Without AI Assistance

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly described. The evaluation involved a single expert providing a qualitative assessment. The study was focused on demonstrating substantial equivalence to predicate and reference devices, not on measuring the improvement of human readers with AI assistance. Therefore, there is no reported effect size of how much human readers improve with AI vs. without AI assistance.

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

Yes, a standalone performance evaluation was conducted for the quantitative Tmax measurement. The acceptance criteria for Tmax accuracy were based on comparing the subject device's measurements directly against the ground truth (average of reference devices) in ROIs, without explicit human intervention in the measurement process for the test cases. While the "ground truth" itself is derived from other devices (which are used by humans), the comparison of the algorithm's output to this ground truth represents a standalone assessment of the algorithm's quantitative accuracy.

7. The Type of Ground Truth Used

• Qualitative Clinical User Evaluation: The ground truth for this evaluation appears to be the performance of the predicate and reference devices, as the subject device's maps were compared to these for substantial equivalence. It's a comparative assessment rather than an absolute ground truth (e.g., pathology).
• Quantitative Tmax Measurement Accuracy: The ground truth for Tmax measurements was defined as the average Tmax measurement of the two reference devices (GE Medical Systems FastStroke CT Perfusion 4D (K193289) and ISchemaView RAPID (K182130)) for a given ROI.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set for the TeraRecon Neuro algorithm.

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. Training set details are not discussed.

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