(119 days)
The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.
Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: dose, image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression.
The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction software support a normal throughput for routine CT.
The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity™ CT Images".
The system allows user selection of three strengths of Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preference for the specific clinical need.
The provided text is a 510(k) summary for the GE Healthcare Japan Corporation's "Deep Learning Image Reconstruction" device. It outlines the device's technical characteristics, intended use, and comparison to predicate devices for substantial equivalence determination. However, it does not include detailed information regarding specific acceptance criteria, a comprehensive study proving the device meets these criteria, or specific performance metrics in a tabular format. The document focuses on establishing substantial equivalence based on the fundamental technology being unchanged from the predicate and successful completion of design control testing and quality assurance measures.
Therefore, I cannot extract all the requested information. Here's what can be inferred and what is missing:
1. A table of acceptance criteria and the reported device performance
This information is not provided in the document. The document states: "Design verification and validation, including IQ bench testing, demonstrate that the Deep Learning Image Reconstruction (DLIR) software met all of its design requirement and performance criteria." However, it does not specify what those "design requirement and performance criteria" are or the reported performance data against them.
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not provided in the document. The document mentions "IQ bench testing" and "System Testing" including "Image Performance Testing (Verification)" and "Simulating Use Testing (Validation)," but does not detail the sample sizes or data provenance used for these tests.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
This information is not provided in the document.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided in the document.
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
This information is not provided in the document. The document describes the device as a "deep learning based reconstruction method" that produces images with "similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: dose, image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression." This implies a comparison to other reconstruction methods, but not a MRMC study involving human readers with and without AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, based on the description, the primary testing described is "standalone" algorithm performance. The device is a "deep learning based reconstruction method" and the testing described, such as "IQ bench testing" and "Image Performance Testing," focuses on the intrinsic image quality outputs of the algorithm. There is no mention of human-in-the-loop performance in the context of effectiveness studies.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
This information is not explicitly stated in the document. Given the context of "IQ bench testing" and performance metrics like "image noise," "low contrast detectability," and "spatial resolution," it's highly likely that objective phantom studies and potentially established image quality metrics (which could be considered a form of "ground truth" for image quality, validated against known physical properties) were used. However, expert consensus on clinical diagnostic accuracy or pathology is not mentioned as a ground truth.
8. The sample size for the training set
This information is not provided in the document. It mentions that the device "uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images," but the details of the training set are not disclosed.
9. How the ground truth for the training set was established
This information is not provided in the document. While it states the DNN was "trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V," the method for establishing the ground truth for this training is not detailed.
§ 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.