(116 days)
HYPER AiR is an image processing function intended to be used by radiologists and nuclear medicine physicians to reduce noise and improve contrast of fluorodeoxyglucose (FDG) PET images.
HYPER AiR is a software-only device. HYPER AiR is an image reconstruction technique which incorporates pre-trained neural networks in the iteration reconstruction process to control image noise and contrast. It is intended to be implemented on previously cleared PET/CT devices uMI 550 (K193241) and uMI 780 (K172143). HYPER AiR serves as an alternative to the existing image reconstruction algorithm that are available on the predicate devices.
The provided text describes the 510(k) summary for the HYPER AiR device, a software-only image processing function for FDG PET images. Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the information provided:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are implicitly defined by the performance tests and clinical image evaluation described. The device's performance is reported in terms of improvement over the conventional OSEM (Ordered Subset Expectation Maximization) algorithm.
| Acceptance Criteria (Implicit) | Reported Device Performance |
|---|---|
| Non-Clinical (Bench Testing): | |
| Performance on noise reduction improvement | HYPER AiR can improve image contrast while suppressing background noise. |
| Performance on image contrast improvement | HYPER AiR can improve image contrast while suppressing background noise. |
| Performance on contrast to noise ratio improvement | Performed, indicating improvement. |
| Clinical Image Evaluation: | |
| Better image contrast compared to OSEM | HYPER AiR produces images with better image contrast than OSEM. |
| Lower image noise compared to OSEM | HYPER AiR produces images with lower image noise than OSEM. |
| Image quality sufficient for clinical diagnosis | The image quality was sufficient for clinical diagnosis. |
| Overall similar performance to predicate devices (for SE) | Based on comparison and analysis, the proposed device has similar performance, equivalent safety and effectiveness as the predicate devices. Differences do not affect indications for use, safety, and effectiveness. |
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: The exact number of cases or images in the test set for the clinical image evaluation is not specified. It only states "The clinical image evaluation was performed by comparing HYPER AiR with OSEM."
- Data Provenance: The raw datasets used for evaluation were "obtained on UH's uMI 780 and uMI 550," which are devices from United Imaging Healthcare. The country of origin of this data is not explicitly stated, but given the sponsor's location (Shanghai, China), it can be inferred that the data likely originated from China. The data was retrospective as it involved applying two different reconstruction algorithms (HYPER AiR and OSEM) to the identical raw datasets already obtained.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- Number of Experts: "Each image was read by three board-certified nuclear medicine physicians."
- Qualifications of Experts: "board-certified nuclear medicine physicians." No specific years of experience are mentioned.
4. Adjudication Method for the Test Set
The adjudication method is not explicitly stated. It says "Each image was read by three board-certified nuclear medicine physicians who provided an assessment of image contrast, image noise and image quality." It does not describe how discrepancies among the three readers were resolved or if a consensus mechanism was used.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size
- MRMC Study: A comparative evaluation was done with human readers comparing HYPER AiR reconstructed images to OSEM reconstructed images. This is akin to an MRMC study if the multiple readers evaluated the same cases under both conditions.
- Effect Size: The document states that "HYPER AiR produces images with better image contrast and lower image noise than OSEM while the image quality was sufficient for clinical diagnosis." However, a quantitative effect size (e.g., statistical significance of improvement, specific metrics like AUC difference, or reader confidence scores) is not provided in this summary. It's a qualitative statement of improvement. The study focuses on the standalone performance of the image processing rather than human readers improving with AI assistance vs without, although improved image quality implies potential for human improvement.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
Yes, standalone performance was evaluated through "Engineering bench testing" where the "evaluation and analysis used the identical raw datasets obtained on UH's uMI 780 and uMI 550, and then applies both HYPER AiR and OSEM to do image reconstruction. The resultant images were then compared for: Performance on noise reduction, Performance on image contrast, Performance on contrast to noise ratio." The aim was to show HYPER AiR's intrinsic ability to improve image characteristics compared to OSEM.
7. The Type of Ground Truth Used
The ground truth used for the evaluation was expert consensus/reader assessment by three board-certified nuclear medicine physicians for the clinical image evaluation. For the non-clinical bench testing, the "ground truth" was essentially the quantitative improvement in objective image metrics (noise reduction, contrast, CNR) based on the algorithm's output compared to OSEM. This is not a "true" clinical ground truth like pathology, but rather a technical performance measure.
8. The Sample Size for the Training Set
The document does not specify the sample size used for the training set of the neural networks integrated into HYPER AiR. It only mentions "pre-trained neural networks."
9. How the Ground Truth for the Training Set was Established
The document does not provide information on how the ground truth for the training set was established. It merely states that HYPER AiR "incorporates pre-trained neural networks in the iteration reconstruction process to control image noise and contrast."
§ 892.1200 Emission computed tomography system.
(a)
Identification. An emission computed tomography system is a device intended to detect the location and distribution of gamma ray- and positron-emitting radionuclides in the body and produce cross-sectional images through computer reconstruction of the data. This generic type of device may include signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts, and accessories.(b)
Classification. Class II.