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Intended for use by a qualified/trained doctor or technologist on both and pediatric patients for taking diagnostic radiographic exposures of the skull, spinal column, extremities, and other body parts on both adult and pediatic patients. Applications can be performed with patient sitting, standing or lying in the prone or supine positions. Not intended for mammography

This device is a medical x-ray image acquisition device. X-rays generated by X-ray generator/ tube that penetrate patient's body are converted to a digital file by the detector. After that the detector sends this digital file to a pc where the companion software has been installed. This software was cleared in our previous submission, K170202. A monitor displays this image. Images can then be transferred via the DICOM protocol. This device is nearly identical to the predicate but now the digital panels have Wi-Fi in addition to Ethernet interfaces. The Aquarius 8600 will be marketed in two possible configurations: Aquarius 8600 1717WCl (tethered or wireless), 17 x 17 inch flat panel as a retrofit package with Magellan software). Aquarius 8600 1417WCl (tethered or wireless), 14 x 17 inch flat panel as a retrofit package with Magellan software).

The provided text is a 510(k) premarket notification for a stationary x-ray system (Aquarius 8600 1417WCI; Aquarius 8600 1717WCI). It describes the device, its similarity to a predicate device, and the non-clinical tests conducted to establish substantial equivalence.

Based on the provided information, no specific acceptance criteria or a dedicated study proving the device meets those criteria are explicitly detailed in a format that would allow for a complete answer to all parts of your request.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (Aquarius 8600 1417TC/1717TC, K170202) and references a cleared flat panel detector (K160810). The testing described is primarily non-clinical.

Here's an analysis of what information can be extracted and what is missing:

1. A table of acceptance criteria and the reported device performance

No explicit table of acceptance criteria is provided. The document states "Performance characteristics" are similar to the predicate and provides some technical specifications (MTF, DQE, Panel Resolution, Pixel Size) for both the predicate and the new device. It notes that the new panels have "slightly better MTF" and "slightly lower DQE," implying these performance characteristics were measured against the predicate's performance rather than predefined acceptance criteria.

2. Sample size used for the test set and the data provenance

The document mentions "Clinical images were provided," but explicitly states "these images were not necessary to establish substantial equivalence based on the modifications to the device... but they provide further evidence... that the complete system works as intended."

• Sample Size: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable, as the clinical images were not presented as part of a formal study with ground truth established by experts for performance evaluation against a specific clinical claim. They were "further evidence" of system functionality.

4. Adjudication method for the test set

Not applicable, as no formal clinical study with ground truth and expert adjudication is described for the purpose of establishing device performance against acceptance criteria.

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

Not applicable. This device is an x-ray imagine acquisition device, not an AI-powered diagnostic tool. No AI component is mentioned.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

Not applicable. This device is an x-ray imagine acquisition device, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

Not applicable, as no formal clinical study with ground truth for performance evaluation is described. For the non-clinical bench testing, the "ground truth" would be the physical properties and measurements of the device components themselves, following established engineering and physics principles.

8. The sample size for the training set

Not applicable. This is a hardware device (x-ray panel and system), not an AI algorithm requiring a training set.

9. How the ground truth for the training set was established

Not applicable. This is a hardware device, not an AI algorithm.

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Intended for use by a qualified/trained doctor or technologist on both adult and pediatric patients for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts on both adult and pediatric patients. Applications can be performed with patient sitting, standing or lying in the prone or supine positions. Not intended for mammography.

The Aquarius 8600 1417WC is a digital flat panel (specifically termed solid state digital X-Ray detector), with the option for users to use the detector in either a tethered or wireless mode. This technology couples a scintillator with an a-Si TFT sensor, and through integration with a radiographic imaging system, x-ray images can be captured and digitalized. The resulting RAW files are DICOM 3.0 compatible allowing image files to be processed by IDC Magellan software.

This 510(k) submission describes the Aquarius 8600 1417WC, a digital flat panel X-ray detector. It is a modification of a previously cleared device (Aquarius 8600 1717TC) and integrates a 510(k)-cleared tethered/wireless flat panel detector with existing software and workstation components. The submission focuses on demonstrating substantial equivalence to the predicate device.

Here's an analysis of the provided text in the context of acceptance criteria and supporting studies:

1. A table of acceptance criteria and the reported device performance:

The document doesn't explicitly define "acceptance criteria" in a quantitative manner for specific diagnostic tasks. Instead, it compares the technological characteristics and performance metrics of the proposed device (Aquarius 8600 1417WC) with those of the predicate device (Aquarius 8600 1717TC), aiming to demonstrate "similar" or "better" performance, thereby supporting substantial equivalence.

2. Sample size used for the test set and the data provenance:

• Sample Size for DQE, MTF, NPS: Not explicitly stated as a numerical count of images or measurements. These are physical characteristics of the detector itself, typically measured under controlled laboratory conditions, not on a "test set" of patient images in the traditional sense.
• Sample Size for Diagnostic Similarity: "Laboratory images using phantoms were acquired with the proposed Aquarius 8600 1417WC detector and compared to images acquired with the Aquarius 1717TC predicate device." The number of phantoms or images is not specified.
• Data Provenance: The DQE, MTF, and NPS measurements are laboratory-derived data. The "laboratory images using phantoms" are also laboratory-derived. There is no indication of country of origin for the data; it would likely be from the manufacturer's testing facilities. The tests are non-clinical.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: "A Radiological Technologist certified in the United States of America and Canada has reviewed the images (included in this submission)..." This indicates one radiological technologist was involved.
• Qualifications of Experts: "Radiological Technologist certified in the United States of America and Canada." (No mention of years of experience or specialization beyond general radiology).

4. Adjudication method for the test set:

• No formal adjudication method (e.g., 2+1, 3+1) is described for the image review to establish diagnostic similarity. It appears to be a single reviewer's assessment.

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 MRMC study was done. The device is a digital X-ray detector, not an AI-assisted diagnostic tool. The comparison is between the performance of the proposed detector and a predicate detector, not between human readers with and without AI assistance.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:

• Yes, in spirit, for the detector's physical performance. The DQE, MTF, and NPS measurements represent the standalone performance of the detector hardware. While a human is involved in analyzing the output (e.g., in phantom image review), the core metrics (DQE, MTF, NPS) characterize the intrinsic performance of the detector itself, independent of a diagnostic interpretation by a human.

7. The type of ground truth used:

• For DQE, MTF, NPS: These are objective physical performance metrics measured under controlled laboratory conditions using established methodologies. The "ground truth" is the accurately measured physical response of the detector.
• For Diagnostic Similarity: The "ground truth" is essentially the predicate device's image quality, which is already considered diagnostically acceptable. The expert's role was to confirm that the proposed device's images (of phantoms) were "diagnostically similar" to those of the predicate device. This is a form of expert consensus/comparison against an established benchmark rather than an independent "ground truth" for disease detection.

8. The sample size for the training set:

• Not applicable. This submission is for a digital X-ray detector, which captures images. It does not describe an AI/machine learning model that would require a "training set" of images to learn from. The software (Magellan 3) processes and displays images but is not an AI algorithm in the context of typical training sets.

9. How the ground truth for the training set was established:

• Not applicable. As no training set for an AI/ML model is described, there's no mention of how ground truth for such a set would be established.

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