(140 days)
uCT ATLAS Astound is a computed tomography x-ray system, which is intended to produce cross-sectional images of the whole body by computer reconstruction of x-ray transmission data taken at different angles and planes. uCT ATLAS Astound is applicable to head, whole body, cardiac, and vascular x-ray Computed Tomography.
uCT ATLAS Astound is intended to be used for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society. * Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.
uWS-CT-Dual Energy Analysis 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 various materials of an anatomical region of interest have different attenuation coefficients, which depend on the used energy. These differences provide information on the chemical composition of the scanned body materials and enable images to be generated at multiple energies within the available spectrum. uWS-CT-Dual Energy Analysis software combines images acquired with low and high energy spectra to visualize this information.
The proposed device uCT ATLAS Astound with uWS-CT-Dual Energy Analysis includes image acquisition hardware, image acquisition, reconstruction and dual energy analysis software, and associated accessories.
The uCT ATLAS Astound is a multi-slice computed tomography scanner that features the following specification and technologies.
- 40 mm z-coverage in a single axial exposure with a 80-row 0.5 mm-slice Z-● Detector
- . 0.25 s rotation speed for high temporal resolution, and maximum 310 mm/s fast helical scanning capability
- 82 cm bore size, 318 kg (700 lbs) maximum table load capacity allows flexible . positioning and access for all patients
- . The new generation reconstruction method, Deep IR (also named AIIR), which combines the model-based iterative reconstruction and deep learning technology together, in order to reduce image noise and artifacts, while at the same time improving low contrast detectability and spatial resolution
- The uAI Vision patient positioning assistance
Built upon these technologies, the uCT ATLAS Astound is designed to use less radiation dose. Further, the fast scanning capability benefits the clinical applications, especially for cardiac imaging, dynamic whole organ imaging and fast body and vascular imaging.
The uWS-CT-Dual Energy Analysis 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 various materials of an anatomical region of interest have different attenuation coefficients, which depend on the used energy. These differences provide information on the chemical composition of the scanned body materials. CT dual energy analysis application combines images acquired with low and high energy spectra to visualize this information.
It appears that the provided document is a 510(k) summary for a medical device (uCT ATLAS Astound with uWS-CT-Dual Energy Analysis) being submitted to the FDA. While it discusses the device's indications for use, technological characteristics compared to a predicate device, and various non-clinical performance data (electrical safety, EMC, software, biocompatibility, etc.), it does not contain detailed information about a specific clinical study aimed at proving the device meets quantitative acceptance criteria related to its performance in terms of diagnostic accuracy or reader improvement.
The section titled "Clinical Image Evaluation" mentions that "Sample image of head, neck, chest, abdomen, spine, hip, knee, pelvis and so on were provided with a board certified radiologist to evaluate the image quality in this submission. Each image was reviewed with a statement indicating that image quality are sufficient for clinical diagnosis." This describes a qualitative assessment of image quality by a radiologist, rather than a rigorous study with predefined acceptance criteria, statistical analysis, and a detailed breakdown of test set characteristics as requested.
Therefore,Based on the provided document, the specific details required to answer your request regarding acceptance criteria and the study that proves the device meets them (especially in the context of diagnostic performance or human reader improvement with AI assistance) are not present. The document focuses on showing substantial equivalence to a predicate device primarily through technical comparisons and non-clinical testing, along with a high-level, qualitative statement about clinical image evaluation.
To directly answer your questions based only on the provided text:
1. A table of acceptance criteria and the reported device performance:
- Not provided. The document does not list quantitative acceptance criteria for diagnostic performance or reported performance metrics against such criteria. It states that non-clinical tests (dosimetry, image performance) verified the device met design specifications and that image quality was "sufficient for clinical diagnosis" based on a radiologist's review.
2. Sample size used for the test set and the data provenance:
- Sample size: Not specified for the "Clinical Image Evaluation" or any other diagnostic performance test.
- Data provenance: Not specified (e.g., country of origin, retrospective/prospective). The general statement "Sample image of head, neck, chest, abdomen, spine, hip, knee, pelvis and so on were provided" does not offer these details.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of experts: Singular ("a board certified radiologist").
- Qualifications: "board certified radiologist." No mention of years of experience.
- Ground Truth Establishment: Not described. The radiologist "evaluate[d] the image quality" and provided "a statement indicating that image quality are sufficient for clinical diagnosis." This is an evaluation of image quality, not the establishment of a ground truth for a specific diagnostic task from a test set.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- None described. Given only one radiologist is mentioned for image quality evaluation, formal adjudication is not implied.
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:
- No, an MRMC comparative effectiveness study was not explicitly mentioned or detailed. The device is a CT system with dual-energy analysis software; the document does not describe AI assistance for human readers or a study evaluating reader improvement.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- No standalone diagnostic performance study by the algorithm is described. The software performs image post-processing and analysis (e.g., generating mono-energetic images, material base pairs, virtual non-contrast images), but there's no mention of a study where the algorithm itself made a diagnosis or provided a quantitative output that was evaluated against ground truth without human input.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Not explicitly stated for diagnostic performance. The "Clinical Image Evaluation" relied on a radiologist's qualitative judgment of "image quality" and its sufficiency for "clinical diagnosis," which isn't a direct ground truth for disease presence/absence.
8. The sample size for the training set:
- Not applicable/Not provided. The document describes a CT scanner and post-processing software. While the device incorporates "Deep IR (also named AIIR)" which is described as combining "model-based iterative reconstruction and deep learning technology," there is no mention of a separate "training set" in the context of device performance claims or a diagnostic AI component being evaluated. This deep learning component appears to be part of the image reconstruction process, not necessarily a diagnostic AI algorithm that is trained on a specific dataset with ground truth labels for a clinical condition.
9. How the ground truth for the training set was established:
- Not applicable/Not provided. (See point 8).
In summary, the provided FDA 510(k) summary focuses on demonstrating substantial equivalence to a predicate device through technical specifications, non-clinical tests, and a qualitative clinical image evaluation. It does not present the type of detailed clinical study data, acceptance criteria, or ground truth establishment relevant to evaluating diagnostic AI performance or human reader improvement with AI assistance as requested in your prompt.
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.