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D2RS & D2RS9090 Digital Dynamic Remote System is indicated for use in generating fluoroscopic images of human anatomy for vascular angiography, diagnostic, and interventional procedures. It is also indicated for generating fluoroscopic images of human anatomy for cardiology, diagnostic, and interventional procedures. It is intended to replace fluoroscopic images obtained through image intensifier technology. Not intended for mammography applications.

As compared to the predicate system, we have added a newly compatible Digital Dynamic X-ray receptor panel, model: Pixium RF 4343 FL (Model 4). This panel has already received FDA clearance (reference device above). The original Canon panels remain available. The D2RS & D2RS9090 are direct digital dynamic remote-controlled fluoroscopy and radiography systems equipped with the latest generation of Canon Flat Panel Detector (FPD). The single FPD can perform both fluoroscopy and radiography and is detachable and portable for direct projections to create a unique and highly versatile 3-in-1 imaging solution. The receptor panel directly converts the X-ray images captured by the sensor into a high-resolution digital images. The instrument is suited for use inside a patient environment. This unit converts the Xrays into digital signals. The unit can acquire still and moving images. The system includes a remotely controlled tilting/elevating table. Panel information: Based on a flat-panel digital detector with the largest field-of-view in the market, the Pixium RF 4343 FL (Model 4) is designed for easy integration in classical R&F tables. It allows manufacturers to offer radiologists an alldigital real-time system, generating high-quality images for both routine dynamic applications and high-end static ones. Digital technology facilitates the work of X-ray technicians and speeds up the process, allowing medical centers to optimize patient throughput. Universal: a single detector for both Radiography or Fluoroscopy. A new tilting table has been introduced (D2RS9090). This new variant of D2RS consists of a modification of the remote controlled table. Instead of being down the table, this one is now behind. This allows a tilting from -90 to +90° (versus -25 to 90° with previous version) and an elevation from 368 to 1455 cm (versus 640 to 930 cm with previous version). The control console has been updated to a more modern looking design but is functionally identical to the predicate.

The provided text is a 510(k) summary for the D2RS & D2RS9090 Digital Dynamic Remote System. It describes the device, its indications for use, and a comparison to a predicate device. However, it does not contain information about specific acceptance criteria or a study proving the device meets those criteria from an AI/algorithm performance perspective.

The document primarily focuses on explaining that the device is a modified fluoroscopic x-ray system, specifically incorporating a new digital x-ray detector (Pixium RF 4343 FL Model 4) that had already received FDA clearance as a reference device (K202235). The key arguments for substantial equivalence are based on:

1. Technological characteristics: The new panel has similar or better characteristics (e.g., DQE, MTF, pixel pitch, matrix size) compared to the original panel in the predicate device.
2. Bench/Performance Testing: Systems covering all generator/panel combinations were assembled and tested for proper operation. Software was validated, and cybersecurity recommendations were observed. The physical components also comply with various IEC standards.
3. Clinical Evaluation: It explicitly states that clinical studies were NOT required because the added compatible digital x-ray receptor panel had previously received FDA clearance (as a reference device, K202235). This means there was no new clinical study specifically demonstrating the performance of this integrated system in terms of diagnostic accuracy or a comparison with human readers.

Therefore, I cannot provide the requested information regarding acceptance criteria and performance data for an AI/algorithm because the document does not describe the device as having an AI/algorithmic component that processes or interprets images for diagnostic purposes. The device is a fluoroscopic imaging system, and the "performance" described relates to its image quality and safety as an imaging modality, not to an AI's diagnostic performance.

To directly answer your request based on the provided text, while acknowledging its limitations for an AI/algorithm context:

There is no mention of acceptance criteria or a study proving an AI/algorithm meets those criteria because the device described is an X-ray imaging system, not an AI-powered diagnostic tool. The "performance" assessment is based on physical characteristics and compliance with standards for X-ray devices.

Here's what can be extracted, interpreted, or explicitly stated as "not present" based on your specific questions, assuming for a moment you were expecting details on a diagnostic AI:

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

   • Acceptance Criteria (Interpreted for a Hardware Device): The acceptance criteria are implicit in the comparison table (pages 5-6) and the compliance with international standards (pages 6-7). The core criterion for substantial equivalence in this context is that the new panel's characteristics (e.g., DQE, MTF, pixel pitch, matrix) are "equal to or better than the predicate device" and that the system complies with safety and performance standards.
   • Reported Device Performance:

     Characteristic	Acceptance Criteria (Predicate / K213479)	Reported Performance (New Device / K221335)
     Name	Canon CXDI-RF Wireless B1	Pixium RF 4343 FL (Model 4)
     Dimension	480 x 460 mm	500 x 490 mm
     Useful Area	42 x 43 cm	43 x 43 cm
     Scintillator	Csl	Csl
     Pixel pitch	160 µm	148 µm
     Matrix	2592 × 2656 pixels	2874 x 2840 pixels
     AD conversion	16 bits	16 bits
     DQE	60% (0.5 lp/mm)	73% typ (0 lp/mm) (Similar performance)
     MTF	38% (2 lp/mm)	35% typ. (2 lp/mm) (Similar performance)
     Fps Max	30 fps	30 fps
     Interface	Ethernet/Wireless WiFi	Ethernet
     DICOM 3	YES	YES
     Operating temperature	15-40°C	15-40°C

2. Sample size used for the test set and the data provenance: Not applicable/not provided. This type of information would be relevant for a study validating an AI/diagnostic algorithm using a test dataset of patient cases. This document describes a new hardware component (detector) being integrated into an existing system, and its performance validation is through bench testing and comparison of physical specifications.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable/not provided. Ground truth establishment with experts is for diagnostic AI models, not for an X-ray imaging system's hardware performance.

4. Adjudication method for the test set: Not applicable/not provided.

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, as the device is not an AI assistance tool for human readers. Clinical studies were explicitly stated as "not required" because the new panel component had already received clearance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable/not provided. The device is a system that produces images for human interpretation.

7. The type of ground truth used: For this device, the "ground truth" for its performance is derived from physical measurements (e.g., DQE, MTF), compliance with established industry standards (IEC, FDA guidance documents), and proper functioning during bench testing.

8. The sample size for the training set: Not applicable/not provided. This information is relevant for AI models, not for the manufacturing and testing of an X-ray imaging system.

9. How the ground truth for the training set was established: Not applicable/not provided.

In summary, the provided document is a 510(k) summary for a hardware modification to a medical imaging device. It is not a study that proves an AI/algorithm meets acceptance criteria, and therefore, most of your specific questions regarding AI validation studies cannot be answered from this text.

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D2RS & D2RS9090 Digital Dynamic Remote System is indicated for use in generating fluoroscopic images of human anatomy for vascular angiography, diagnostic, and interventional procedures. It is also indicated for generating fluoroscopic images of human anatomy for cardiology, diagnostic, and interventional procedures. It is intended to replace fluoroscopic images obtained through image intensifier technology. Not intended for mammography applications.

The D2RS & D2RS9090 are direct digital dynamic remote-controlled fluoroscopy and radiography systems equipped with the latest generation of Canon Flat Panel Detector (FPD). The single FPD can perform both fluoroscopy and radiography and is detachable and portable for direct projections to create a unique and highly versatile 3-in-1 imaging solution. The receptor panel directly converts the X-ray images captured by the sensor into a high-resolution digital images. The instrument is suited for use inside a patient environment. This unit converts the X-rays into digital signals. The unit can acquire still and moving images. The system includes a remotely controlled tilting/elevating table. A new tilting table has been introduced (D2RS9090). This new variant of D2RS consists of a modification of the foot of the remote controlled table. Instead of being down the table, this one is now behind. This allows a tilting from -90 to +90° (versus -25 to 90° with previous version) and an elevation from 368 to 1455 cm (versus 640 to 930 cm with previous version). The control console has been updated to a more modern looking design but is functionally identical to the predicate.

The provided text describes a 510(k) premarket notification for a medical device called D2RS & D2RS9090 Digital Dynamic Remote System. The submission aims to establish substantial equivalence to a predicate device (D2RF Digital Dynamic Remote System). Here's a breakdown of the requested information based on the provided text, with notes where information is not explicitly stated.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are generally implied to be "as safe and effective as the predicate device" and compliance with relevant IEC standards and FDA guidances. The performance is assessed against these implicit criteria and through a comparison of technological characteristics.

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

• Sample Size for Test Set: Not explicitly stated. The text mentions "both static and moving images" were reviewed, but doesn't quantify the number of images or cases.
• Data Provenance: Not explicitly stated (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

• Number of Experts: "A board certified radiologist" (singular) was used.
• Qualifications: "board certified radiologist." Specific experience level (e.g., years of experience) is not mentioned.

4. Adjudication method for the test set

• Adjudication Method: "A board certified radiologist reviewed... and found them to be of excellent diagnostic quality." This implies a single-reader review rather than an adjudication process involving multiple experts. No formal adjudication method (like 2+1 or 3+1) is mentioned.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not explicitly done. The study described is a single-reader review for diagnostic image quality, comparing the device's output to an implicit standard of "excellent diagnostic quality," likely in comparison to the predicate device's expected performance. This submission is for an imaging system, not an AI-assisted diagnostic tool.
• Effect Size: Not applicable, as no MRMC study with AI assistance was performed.

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

• Standalone Performance: Not applicable in the context of this submission. The device is an X-ray imaging system, where human interpretation of the images is inherent to its use. The "algorithm" here refers to the system's image acquisition and processing capabilities, not an AI for diagnosis. The clinical evaluation focuses on the quality of the images produced for human interpretation.

7. The type of ground truth used

• Type of Ground Truth: The ground truth for the clinical evaluation was based on the subjective assessment of a "board certified radiologist" who determined the images to be of "excellent diagnostic quality." This is an expert opinion/consensus implicitly benchmarked against diagnostic standards. It is not pathology, or outcomes data.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. The device described is an X-ray imaging system, not an AI/machine learning algorithm that requires a training set in the conventional sense. The "training" of such a system involves engineering and calibration to produce high-quality images according to technical specifications and clinical needs, rather than learning from a dataset.

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

• Ground Truth for Training Set: Not applicable. As mentioned above, this is an X-ray imaging system, not an AI/ML algorithm that generates a "ground truth" during a training phase.

In summary, the submission focuses on demonstrating substantial equivalence primarily through technological comparison to a predicate device, adherence to relevant technical standards, and a qualitative assessment of image quality by a radiologist. It is not an AI-based diagnostic device; therefore, many of the questions related to AI studies (MRMC, training sets, etc.) are not directly applicable.

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