(16 days)
Venul 717X is indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. It is intended to replace film/screen systems in general-purpose diagnostic procedures.
Venu1717X is a cassette-size tethered X-ray flat panel detector based on amorphous silicon thin-film transistor technology. It is designed to provide the high quality radiographic image which contains an active matrix of 3070×3070 with 139um pixel pitch. The scintillator of Venu1717X is CsI(Caesium Iodide). The technology of CsI direct growth reduces the exposure dose and improves the image quality. Since Venu1717X supports multiple trigger modes, it can satisfy both of the general DR system and retrofit DR system.
iRay SDK(include iDetector) is intend to supply API interface for DR system manufacturers.DR system manufacturer control the detector by SDK interface. SDK is not intended to be used directly by other users beside DR system manufacturers. The iRay SDK is unchanged from the predicate device.
The information provided indicates that the iRay Technology Taicang Ltd. Flat Panel Detector (Venu1717X) is a digital imaging solution for general radiographic diagnosis. While the provided text describes the device's technical specifications and non-clinical studies to establish substantial equivalence to a predicate device (Mars1717V-VSI, K201043), it does not contain details about specific acceptance criteria for diagnostic accuracy metrics (like sensitivity or specificity) for a clinical study.
Instead, the provided text focuses on demonstrating substantial equivalence primarily through technical performance characteristics and a "concurrence study" of clinical images.
Here's an attempt to answer your request based only on the provided text, highlighting what is available and what is missing:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state "acceptance criteria" in terms of diagnostic performance metrics for a clinical study (e.g., sensitivity, specificity, or AUC with target thresholds). It focuses on demonstrating equivalence through technical performance.
| Criterion Type | Acceptance Criteria (Not explicitly stated as such for clinical performance in text) | Reported Device Performance (as presented) | Notes |
|---|---|---|---|
| Technical Performance | Demonstrated substantial equivalence to predicate device (Mars1717V-VSI, K201043) | Spatial Resolution: Min. 3.4 lp/mm (Predicate: Min. 3.6 lp/mm) | While the proposed device's spatial resolution is slightly lower than the predicate, the document likely considers this within acceptable variation for substantial equivalence, especially when considered with other factors. |
| Modulation Transfer Function (MTF): 0.66 at 1 lp/mm (Predicate: 0.65 at 1 lp/mm) | Improved MTF compared to predicate. | ||
| Detective Quantum Efficiency (DQE): 0.28 at 1 lp/mm (RQA5, 2.5µGy) (Predicate: 0.40 at 1 lp/mm (RQA5, 2.5µGy)) | Note: The proposed device's DQE is lower than the predicate. This is a significant difference in a key image quality metric. The justification for substantial equivalence despite this difference is not explicitly detailed beyond the overall conclusion. It often implies that other aspects of performance or the context of use mitigate this difference for diagnostic purposes. | ||
| Electrical Safety and EMC: Meet IEC/ES 60601-1, IEC60601-2-54, and IEC 60601-1-2 standards. | All test results reported to meet standard requirements. | ||
| Biological Evaluation: Confirmed safety as predicate device per ISO 10993-1. | Evaluated and assured safety. | ||
| Cybersecurity: Passed assessment related to Cybersecurity. | Passed the required assessments. | ||
| Clinical Performance | No significant difference between images of the proposed and predicate device. | "There was no significant difference between the images of the Venu1717X and those of the predicate device." (from a "concurrence study of 30 clinical images") | This is the closest statement to a clinical performance outcome. However, "no significant difference" is a qualitative assessment and not tied to specific quantitative diagnostic accuracy metrics. The study's purpose was to "provide further evidence in addition to the laboratory performance data to show that the complete system works as intended," rather than to establish diagnostic accuracy against a specific acceptance criterion. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample Size for Test Set: 30 clinical images.
- Data Provenance: Not specified (e.g., country of origin, retrospective or prospective). The document only states "Clinical images were provided".
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)
The document mentions a "concurrence study" but does not specify the number of experts, their qualifications, or how ground truth was established for the 30 clinical images. The statement "There was no significant difference between the images" implies a qualitative comparison by human readers, but details are missing.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
The document does not specify any adjudication method for the "concurrence study" of the 30 clinical images.
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, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described. The study mentioned is a "concurrence study of 30 clinical images" comparing the proposed device to a predicate device, which is different from an MRMC study designed to assess reader improvement with AI assistance. The device itself is a Flat Panel Detector, which is hardware for image acquisition, not inherently an AI-driven diagnostic assistance tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This section is not applicable as the device is a Flat Panel Detector, a hardware component for imaging, not an AI algorithm performing diagnostic tasks in a standalone manner. The software mentioned (iRay SDK, iDetector) are for controlling the detector and integration, not for standalone diagnostic interpretation.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
For the "concurrence study" of 30 clinical images, the type of "ground truth" and how it was established is not detailed. The study aimed to show "no significant difference" between images of the proposed and predicate device, rather than assessing diagnostic accuracy against an independent ground truth.
8. The sample size for the training set
This information is not applicable as the description refers to a medical imaging device (Flat Panel Detector) and its associated control software, not an AI model that would typically have a "training set" in the context of machine learning.
9. How the ground truth for the training set was established
This information is not applicable for the same reasons as #8.
§ 892.1680 Stationary x-ray system.
(a)
Identification. A stationary x-ray system is a permanently installed diagnostic system intended to generate and control x-rays for examination of various anatomical regions. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II (special controls). A radiographic contrast tray or radiology diagnostic kit intended for use with a stationary x-ray system only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.