(142 days)
The intended use of the device YSIO X.pree is to visualize anatomical structures of human beings by converting an X-ray pattern into a visible image.
The device is a digital X-ray system to generate X-ray images from the whole body including the skull, chest, abdomen, and extremities. The acquired images support medical professionals to make diagnostic and/or therapeutic decisions.
YSIO X.pree is not for mammography examinations.
The YSIO X.pree is a radiography X-ray system. It is designed as a modular system with components such as a ceiling suspension with an X-ray tube, Bucky wall stand, Bucky table, X-ray generator, portable wireless, and fixed integrated detectors that may be combined into different configurations to meet specific customer needs.
The following modifications have been made to the cleared predicate device:
- Updated generator
- Updated collimator
- Updated patient table
- Updated Bucky Wall Stand
- New X.wi-D 24 portable wireless detector
- New virtual AEC selection
- New status indicator lights
The provided 510(k) clearance letter and summary for the YSIO X.pree device (K250738) indicate that the device is substantially equivalent to a predicate device (K233543). The submission primarily focuses on hardware and minor software updates, asserting that these changes do not impact the device's fundamental safety and effectiveness.
However, the provided text does not contain the detailed information typically found in a clinical study report regarding acceptance criteria, sample sizes, ground truth establishment, or expert adjudication for an AI-enabled medical device. This submission appears to be for a conventional X-ray system with some "AI-based" features like auto-cropping and auto-collimation, which are presented as functionalities that assist the user rather than standalone diagnostic algorithms requiring extensive efficacy studies for regulatory clearance.
Based on the provided document, here's an attempt to answer your questions, highlighting where information is absent or inferred:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state quantitative acceptance criteria in terms of performance metrics (e.g., sensitivity, specificity, or image quality scores) with corresponding reported device performance values for the AI features. The "acceptance" appears to be qualitative and based on demonstrating equivalence to the predicate device and satisfactory usability/image quality.
If we infer acceptance criteria from the "Summary of Clinical Tests" and "Conclusion as to Substantial Equivalence," the criteria seem to be:
| Acceptance Criteria (Inferred) | Reported Device Performance (as stated in document) |
|---|---|
| Overall System: Intended use met, clinical needs covered, stability, usability, performance, and image quality are satisfactory. | "The clinical test results stated that the system's intended use was met, and the clinical needs were covered." |
| New Wireless Detector (X.wi-D24): Images acquired are of adequate radiographic quality and sufficiently acceptable for radiographic usage. | "All images acquired with the new detector were adequate and considered to be of adequate radiographic quality." and "All images acquired with the new detector were sufficiently acceptable for radiographic usage." |
| Substantial Equivalence: Safety and effectiveness are not affected by changes. | "The subject device's technological characteristics are same as the predicate device, with modifications to hardware and software features that do not impact the safety and effectiveness of the device." and "The YSIO X.pree, the subject of this 510(k), is similar to the predicate device. The operating environment is the same, and the changes do not affect safety and effectiveness." |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: Not explicitly stated as a number of cases or images. The "Customer Use Test (CUT)" was performed at two university hospitals.
- Data Provenance: The Customer Use Test (CUT) was performed at "Universitätsklinikum Augsburg" in Augsburg, Germany, and "Klinikum rechts der Isar, Technische Universität München" in Munich, Germany. The document states "clinical image quality evaluation by a US board-certified radiologist" for the new detector, implying that the images themselves might have originated from the German sites but were reviewed by a US expert. The study design appears to be prospective in the sense that the new device was evaluated in a clinical setting in use rather than historical data being analyzed.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Experts
- Number of Experts: For the overall system testing (CUT), it's not specified how many clinicians/radiologists were involved in assessing "usability," "performance," and "image quality." For the new wireless detector (X.wi-D24), it states "a US board-certified radiologist."
- Qualifications of Experts: For the new wireless detector's image quality evaluation, the expert was a "US board-certified radiologist." No specific experience level (e.g., years of experience) is provided.
4. Adjudication Method for the Test Set
No explicit adjudication method (e.g., 2+1, 3+1 consensus) is described for the clinical evaluation or image quality assessment. The review of the new detector was done by a single US board-certified radiologist, not multiple independent readers with adjudication.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and what was the effect size of how much human readers improve with AI vs. without AI assistance.
- MRMC Study: No MRMC comparative effectiveness study is described where human readers' performance with and without AI assistance was evaluated. The AI features mentioned (Auto Cropping, Auto Thorax Collimation, Auto Long-Leg/Full-Spine collimation) appear to be automatic workflow enhancements rather than diagnostic AI intended to directly influence reader diagnostic accuracy.
- Effect Size: Not applicable, as no such study was conducted or reported.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done.
The document does not describe any standalone performance metrics for the AI-based features (Auto Cropping, Auto Collimation). These features seem to be integrated into the device's operation to assist the user, rather than providing a diagnostic output that would typically be evaluated in a standalone study. The performance of these AI functions would likely be assessed as part of the overall "usability" and "performance" checks.
7. The Type of Ground Truth Used
- For the overall system and the new detector, the "ground truth" seems to be expert opinion/consensus (qualitative clinical assessment) on the system's performance, usability, and the adequacy of image quality for radiographic use. There is no mention of pathology, outcomes data, or other definitive "true" states related to findings on the images.
8. The Sample Size for the Training Set
The document does not provide any information about a training set size for the AI-based auto-cropping and auto-collimation features. This is typical for 510(k) submissions of X-ray systems where such AI features are considered ancillary workflow tools rather than primary diagnostic aids.
9. How the Ground Truth for the Training Set was Established
Since no training set information is provided, there is no information on how ground truth was established for any training data.
In summary: The 510(k) submission for the YSIO X.pree focuses on demonstrating substantial equivalence for an updated X-ray system. The "AI-based" features appear to be workflow automation tools that were assessed as part of general system usability and image quality in a "Customer Use Test" and a limited clinical image quality evaluation for the new detector. It does not contain the rigorous quantitative performance evaluation data for AI software as might be seen for a diagnostic AI algorithm that requires a detailed clinical study for clearance.
§ 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.