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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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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 additions: -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, and evaluating CT vascular images. -The uOmnispace. CT Brain Perfusion 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 soore. -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 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 u0mnispace.CT 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 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.

The provided text describes the performance data for three AI/ML algorithms integrated into the uOmnispace.CT software: Spine Labeling Algorithm, Rib Labeling Algorithm, and TAVR Analysis Algorithm.

Here's a breakdown of the acceptance criteria and study details for each:

1. Spine Labeling Algorithm

Acceptance Criteria Table:

Study Proving Device Meets Acceptance Criteria:

• Sample Size for Test Set: 120 subjects.
• Data Provenance: Retrospective, with data collected from five major CT manufacturers (GE, Philips, Siemens, Toshiba, UIH). Clinical subgroups included U.S. (90 subjects) and Asia (30 subjects) for ethnicity.
• Number of Experts for Ground Truth: At least two licensed physicians with U.S. credentials.
• Qualifications of Experts: Licensed physicians with U.S. credentials.
• Adjudication Method: Ground truth annotations were made by "well-trained annotators" using an interactive tool to set annotation points and assign anatomical labels. All ground truth was finally evaluated by two licensed physicians with U.S. credentials. This suggests a post-annotation review/adjudication by experts.
• MRMC Comparative Effectiveness Study: No, this was a standalone performance evaluation of the algorithm against established ground truth.
• Standalone Performance: Yes, the performance of the algorithm itself was evaluated based on a scoring system against ground truth.
• Type of Ground Truth Used: Expert consensus (annotators + review by licensed physicians).
• Sample Size for Training Set: Not specified, but stated that "The training data used for the training of the spine labeling algorithm is independent of the data used to test the algorithm."
• How Ground Truth for Training Set was Established: Not specified beyond the implication that a ground truth process was followed for training data as well.

2. Rib Labeling Algorithm

Acceptance Criteria Table:

Study Proving Device Meets Acceptance Criteria:

• Sample Size for Test Set: 120 subjects.
• Data Provenance: Retrospective, with data collected from five major CT manufacturers (GE, Philips, Siemens, Toshiba, UIH). Clinical subgroups included U.S. (80 subjects) and Asia (40 subjects) for ethnicity.
• Number of Experts for Ground Truth: At least two licensed physicians with U.S. credentials.
• Qualifications of Experts: Licensed physicians with U.S. credentials.
• Adjudication Method: Ground truth annotations were made by "well-trained annotators" using an interactive tool to generate initial rib masks, which were then refined, and anatomical labels assigned. After the first round, annotators "checked each other's annotation." Finally, all ground truth was evaluated by two licensed physicians with U.S. credentials. This indicates a multi-step adjudication process.
• MRMC Comparative Effectiveness Study: No, this was a standalone performance evaluation of the algorithm against established ground truth.
• Standalone Performance: Yes, the performance of the algorithm itself was evaluated based on a scoring system against ground truth.
• Type of Ground Truth Used: Expert consensus (annotators + cross-checking + review by licensed physicians).
• Sample Size for Training Set: Not specified, but stated that "The training data used for the training of the rib labeling algorithm is independent of the data used to test the algorithm."
• How Ground Truth for Training Set was Established: Not specified beyond the implication that a ground truth process was followed for training data as well.

3. TAVR Analysis Algorithm

Acceptance Criteria Table:

Study Proving Device Meets Acceptance Criteria:

• Sample Size for Test Set: 60 subjects.
• Data Provenance: Retrospective. Clinical subgroups included Asia (25 subjects) and U.S. (35 subjects) for ethnicity, including data from U.S. Facility 1 (25 subjects) and U.S. Facility 2 (10 subjects).
• Number of Experts for Ground Truth: At least two licensed physicians with U.S. credentials for the final evaluation of the ground truth.
• Qualifications of Experts: Licensed physicians with U.S. credentials (specifically, "two MD with the American Board of Radiology Qualification" for the subjective scoring).
• Adjudication Method: Ground truth annotations were made by "well-trained annotators." After the first round of annotation, they "checked each other's annotation." Finally, all ground truth was evaluated by two licensed physicians with U.S. credentials. This indicates a multi-step adjudication process.
• MRMC Comparative Effectiveness Study: No, this was a standalone performance evaluation of the algorithm against established ground truth and subjective expert scoring.
• Standalone Performance: Yes, the performance of the algorithm itself was evaluated based on landmark error and subjective expert scoring.
• Type of Ground Truth Used: Expert consensus (annotators + cross-checking + review by licensed physicians).
• Sample Size for Training Set: Not specified, but stated that "The training data used for the training of the post-processing algorithm is independent of the data used to test the algorithm."
• How Ground Truth for Training Set was Established: Not specified beyond the implication that a ground truth process was followed for training data as well.

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