(81 days)
The deep learning based Max Field-of-View (MaxFOV 2) is a CT image reconstruction method intended to produce images of the head and whole-body using Axial, Helical, and Cine acquisitions.
MaxFOV 2 is designed to extend the nominal display field of view (DFoV) for cases where patient size and positioning requirements result in a portion of the patient's body to be outside of the nominal DFoV.
These extended FoV images are intended for use in radiation therapy planning and are clinically useful for the simulation and planning of radiation therapy for the treatment of cancer for patients. They can also be used for visualization of patient anatomy for cases not involving therapy planning. MaxFOV 2 is intended for patients of all ages, especially bariatric patients.
The MaxFOV 2 is an enhanced Extended Field of View (EFOV) reconstruction option for GE's CT scanners. The MaxFOV 2 utilizes a new deep learning algorithm to extend the display field of view (DFOV) beyond the CT system's scan field of View (SFOV) of 50cm to up to 80cm depending on the bore size of the CT system. CT scanners use the EFOV reconstruction algorithms to visualize tissue truncated due to large patient habitus and/or off-center patient positioning. Same as the Wide View option on the predicate, the MaxFOV 2 is designed to enable a clinically useful visualization of the skin line and CT Number of human body parts located outside of the SFOV. EFOV images are especially useful for radiation therapy planning and they can also be used for visualization of patient anatomy outside of the SFOV for routine CT imaging. This DL enabled new MaxFOV2 EFOV reconstruction process offers improved performance over the existing WideView option on the predicate device.
The DL MaxFOV2 algorithm was designed and tested for GE's multiple CT scanner platforms of various bore sizes from 70cm to 80cm. These CT systems with the integrated MaxFOV 2 option remain compliant with the same standards as base CT systems.
This option is commercially marketed as MaxFOV2.
The provided text describes the MaxFOV 2, a CT image reconstruction method that utilizes deep learning to extend the display field of view. The document outlines the device's indications for use, technological characteristics, and the summary of non-clinical and clinical testing performed to support its substantial equivalence to a predicate device.
Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:
Acceptance Criteria and Reported Device Performance
The document doesn't explicitly state quantitative acceptance criteria in a table format with pass/fail thresholds. Instead, it describes various tests and a clinical reader study designed to demonstrate performance that is "consistent and acceptable," does not "raise new or different questions of safety and effectiveness," and supports "substantial equivalence and performance claims." The performance is evaluated relative to the predicate device, "Wide View (K023332)".
Assuming the core acceptance criteria revolve around image quality, accuracy of patient contour (skin line), and CT number accuracy in the extended FOV, the reported performance is qualitative but positive:
| Acceptance Criterion (Inferred from documentation) | Reported Device Performance |
|---|---|
| Image Quality Performance | "A suite of engineering bench testing using phantoms was performed to evaluate image quality performance of MaxFOV 2... all test results demonstrated MaxFOV 2's consistent and acceptable performance." |
| MaxFOV 2 Patient contour (Skin line) accuracy | Tested as part of engineering bench tests. Results demonstrated "consistent and acceptable performance." |
| MaxFOV 2 CT Number accuracy | Tested as part of engineering bench tests. Results demonstrated "consistent and acceptable performance." |
| Does not raise new/different safety & effective questions compared to predicate | "The complete testing and results did not raise different questions of safety and effectiveness than associated with predicate device." "MaxFOV2's design, verification, validation and risk management processes did not identify any new hazards, unexpected results, or adverse effects stemming from the changes to the predicate." |
| Clinical Acceptability (Reader Study) | "The results of the study support substantial equivalence and performance claims." Images were scored using a 5-point Likert scale for "depiction of the patient's skin surface; depicted tissue densities in the extended FOV region; and overall image quality." The implicit acceptance is that a majority of readers, for a majority of cases, rated the images as clinically acceptable. |
Study Details
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: 49 CT exams were used in the clinical reader study.
- Data Provenance: The exams were "acquired from different GE CT system platforms." The text does not specify the country of origin or whether the data was retrospective or prospective. It states the exams "represent typical and challenging RTP-relative scenarios where the MaxFOV2 will likely be used."
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: 5 external, clinical readers.
- Qualifications of Experts: The specific qualifications (e.g., years of experience, subspecialty) are not explicitly stated, beyond them being "external, clinical readers."
4. Adjudication Method for the Test Set
The text indicates that readers scored images using a 5-point Likert scale. It does not mention an explicit adjudication method (e.g., 2+1, 3+1 consensus) for establishing a single "ground truth" score per image. It seems the readers' individual scores were aggregated and analyzed to support the claims.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Was it done?: A clinical reader study was performed, which is a type of MRMC study, though the primary goal seems to be demonstrating substantial equivalence rather than a direct comparison of human readers with and without AI assistance to quantify an "improvement effect size." The study evaluates the output of the AI model (the reconstructed images) through human reader assessment.
- Effect Size of Human Improvement with AI vs. without AI assistance: Not explicitly quantified in terms of human reader improvement. The study assesses the quality of images produced by MaxFOV2, which uses a deep learning component. The improvement is implied for the images produced by MaxFOV2 over the predicate device. "This DL enabled new MaxFOV2 EFOV reconstruction process offers improved performance over the existing WideView option on the predicate device." However, this statement refers to the device's performance, not specifically how human readers improve their diagnostic accuracy or efficiency when using the AI-assisted images compared to reading images generated without the AI.
6. Standalone Performance (Algorithm Only)
The text describes "engineering bench testing using phantoms." These tests (MaxFOV 2 Patient contour (Skin line) accuracy and CT Number accuracy, MaxFOV 2 IQ Performance Evaluation using a very large phantom, MaxFOV 2 Performance Evaluation Using an anthropomorphic phantom) appear to be standalone performance assessments of the algorithm and its output, independent of a human reader's interpretation in a diagnostic context. These tests would evaluate the algorithm's output directly against known phantom characteristics.
7. Type of Ground Truth Used for Test Set
- For Bench Testing: The ground truth for bench testing (e.g., image quality, skin line accuracy, CT number accuracy) would be established by the known physical properties and measurements of the phantoms used.
- For Clinical Reader Study: The "ground truth" for clinical acceptability in the reader study is based on the expert consensus/opinion of the 5 external clinical readers using a Likert scale. This is a subjective assessment of image quality and clinical utility rather than an objective "pathology" or "outcomes" ground truth. The study evaluates how well the generated images aid human interpretation.
8. Sample Size for the Training Set
The document does not explicitly state the sample size used for the training set of the deep learning algorithm ("CNN"). It only mentions that the MaxFOV2 "uses a CNN which is trained on multiple CT scanners."
9. How the Ground Truth for the Training Set Was Established
The document does not provide details on how the ground truth for the training set was established. It simply states that the CNN was "trained on multiple CT scanners." For deep learning image reconstruction, the training might involve paired data (e.g., truncated vs. full FOV images, or images generated with existing algorithms as a reference), or simulated data, but the specific method of ground truth establishment is not described.
§ 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.