(253 days)
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
| Application | Algorithm | Validation Type | Acceptance Criteria (Dice Score) | Reported Device Performance (Dice Score) |
|---|---|---|---|---|
| Lung Density Analysis | Lung segmentation | Dice | 0.97 | 0.9801 |
| Lung Density Analysis | Airway segmentation | Dice | 0.86 | 0.8954 |
| Vessel Analysis | Bone removal (Abdomen & Limbs) | Dice | 0.90 | 0.96957 |
| Vessel Analysis | Bone removal (Head & Neck) | Dice | 0.93 | 0.955 |
| Heart | Coronary artery extraction | Dice | 0.870 | 0.916 |
| Heart | Heart chamber segmentation | Dice | 0.910 | 0.970 |
| Liver Evaluation | Liver segmentation | Dice | 0.97 | 0.981 |
| Liver Evaluation | Hepatic artery segmentation | Dice | 0.85 | 0.927 |
| Liver Evaluation | Hepatic portal vein segmentation | Dice | 0.89 | 0.933 |
| Liver Evaluation | Hepatic vein segmentation | Dice | 0.86 | 0.914 |
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)
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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.
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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).
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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.
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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.
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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.
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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.
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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."
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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)
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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.
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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."
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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.
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If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.
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If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Yes.
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The type of ground truth used: Expert consensus.
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The sample size for the training set: Not specified.
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How the ground truth for the training set was established: Not specified.
3. Heart (Coronary Artery Extraction & Heart Chamber Segmentation)
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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.
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If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Yes.
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The type of ground truth used: Expert consensus.
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The sample size for the training set: Not specified.
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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)
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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.
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The sample size for the training set: Not specified.
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How the ground truth for the training set was established: Not specified.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).