(88 days)
1717SCC Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures. Not to be used for mammography.
1717SCC is a digital solid state X-ray detector that is based on flat-panel technology. This radiographic image detector and processing unit consists of a scintillator coupled to an a-Si TFT sensor. This device needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis The RAW files can be further processed as DICOM compatible image files by separate console SW (not part of this 510k submission) for a radiographic diagnosis and analysis.
Here's an analysis of the provided 510(k) summary regarding the 1717SCC device, focusing on acceptance criteria and the study that proves the device meets them:
1. Table of Acceptance Criteria and Reported Device Performance
The 510(k) summary primarily focuses on demonstrating substantial equivalence to a predicate device (Xmaru1717) rather than defining specific, numerical acceptance criteria for all performance metrics. However, based on the comparative performance study, we can infer implied acceptance criteria related to equivalent or improved diagnostic image quality.
| Acceptance Criteria (Implied from Substantial Equivalence to Predicate) | Reported Device Performance (1717SCC vs. Xmaru1717) |
|---|---|
| Diagnostic Image Quality (Clinical) | Equivalent or Better: Clinical images taken from both devices (1717SCC and Xmaru1717) were reviewed by a licensed US radiologist. Based on this expert review across age groups and anatomical structures, the submission claims "equivalent or better diagnostic image quality for 1717SCC compared to the predicate device, Xmaru1717." |
| Modulation Transfer Function (MTF) | Lower but Offset by Smaller Pixel Size: The MTF of the Xmaru1717 detector performed better initially than 1717SCC. However, the smaller pixel size (127 µm vs. 143 µm) and a new bonding mechanism in 1717SCC are stated to result in "overall resolution performance and sharpness of 1717SCC is better than Xmaru1717 which results improvement of the ability of the new detector to represent distinct anatomic features within the imaged object." (Implied acceptance: overall resolution/sharpness is at least equivalent or better). |
| Detective Quantum Efficiency (DQE) | Better Performance at Various Spatial Frequencies (but lower at zero-frequency): 1717SCC demonstrated better DQE performance than Xmaru1717 at various spatial frequencies, providing a higher Signal-to-Noise Ratio (SNR) transfer. The zero-frequency DQE values were lower for 1717SCC (0.223) than Xmaru1717 (0.38), but this is associated with "reduced noise" and "improved accuracy of image and reduced the degree of artifacts for the new detector." (Implied acceptance: overall DQE/SNR transfer is improved or equivalent for diagnostic purposes). |
| Noise Power Spectrum (NPS) | Lower Performance: 1717SCC exhibited NPS which has lower performance than Xmaru1717. However, this is presented in the context of the "reduced noise" mentioned with DQE, and ultimately contributing to the claim that "the image quality of 1717SCC is greater than Xmaru1717 at the same patient exposure." (Implied acceptance: acceptable noise characteristics that do not degrade diagnostic image quality, or even enhance it in conjunction with other factors). |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: Not explicitly stated for the clinical image comparison. The description mentions "clinical images are taken from both devices" and evaluated "according to age group and anatomical structures." The specific number of images or cases is not provided.
- Data Provenance: The images were "taken from both devices" (1717SCC and Xmaru1717). The clinical review was performed by a "licensed US radiologist," implying the data was relevant to clinical practice, but doesn't specify if it was retrospective or prospective. Given the nature of a 510(k) for a detector, it's highly probable these were retrospective comparisons of images acquired using the two devices rather than a lengthy prospective clinical trial.
- Non-Clinical Test Set: For MTF, DQE, and NPS, standard phantoms and methodologies (IEC 6220-1) were used. These are simulated test conditions, not patient data in the same sense as clinical images.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
- Number of Experts: One ("a licensed US radiologist").
- Qualifications: "licensed US radiologist." No further details on years of experience or subspecialty are provided.
4. Adjudication Method for the Test Set
- Adjudication Method: Not applicable/None, as only one expert was used for the clinical image review. This was a single-reader assessment.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
- No, an MRMC comparative effectiveness study was not reported. The clinical review was performed by a single radiologist. Therefore, no effect size of human readers improving with AI vs. without AI assistance can be determined from this submission, as AI assistance in the interpretation itself is not described (this is a detector, not an AI image analysis tool). The "AI" in this context refers to the technological advancements in the detector itself.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
- Yes, in the sense of non-clinical performance metrics. The non-clinical tests (MTF, DQE, NPS) are standalone evaluations of the detector's physical performance characteristics, independent of human interpretation. These metrics were compared between the new device and the predicate. The device itself is a digital flat panel detector, not an image analysis algorithm that provides automated interpretations.
7. The Type of Ground Truth Used
- For Clinical Image Review: Expert consensus was not used as only one radiologist reviewed. It was based on expert opinion/qualitative assessment by a single licensed US radiologist, comparing the diagnostic image quality of images acquired with the 1717SCC against those from the predicate device (Xmaru1717). The "ground truth" was essentially the subjective judgment of diagnostic equivalence or superiority by the single expert.
- For Non-Clinical Performance: Objective, quantitative measurements against established physics standards (IEC 6220-1) for MTF, DQE, and NPS.
8. The Sample Size for the Training Set
- The device is a digital X-ray detector, not an AI or machine learning algorithm in the typical sense that requires a "training set" of data for learning patterns. It is a hardware device. Therefore, the concept of a training set as understood for AI software does not apply directly here. The "training" for such a device would be its engineering design and manufacturing process.
9. How the Ground Truth for the Training Set Was Established
- As explained above, there isn't a "training set" or "ground truth for a training set" in the context of an AI algorithm for this hardware device. The device's performance characteristics are inherent to its design and components (scintillator, a-Si TFT sensor, pixel pitch, etc.).
§ 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.