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OSCAR 15 & OSCAR 15i are a mobile fluoroscopy system designed to provide fluoroscopic and spot film images of the patient during diagnostic, surgical and interventional procedures. Examples of clinical application may include cholangiography, endoscopy, urologic, neurologic, vascular, cardiac and critical care. The system may be used for other imaging applications at the physician's discretion. OSCAR 15i are indicated only for adult patients.

The OSCAR 15 & OSCAR 15i, C-Arm Mobile are used for providing fluoroscopic and radiographic images of patient anatomy, especially during special procedures in a hospital or medical clinics. The fluoroscopic mode of operation is very useful to the attending physician to see the images on real time without the need to develop individual films. These devices are intended to visualize anatomical structures by converting a pattern of x-radiation into a visible image through electronic amplification. The OSCAR 15 & OSCAR 15i consist of the X-ray tube assembly, X-ray controller, Image receptor and some accessories with no wireless function. The difference between OSCAR 15 and OSCAR 15i is only image acquisition parts. (An Flat Panel Detector (FPD) is applied to OSCAR 15, and an Image instensifier is applied to OSCAR 15i.)

The provided text is a 510(k) Pre-Market Notification for the OSCAR 15 & OSCAR 15i mobile fluoroscopy systems. It asserts substantial equivalence to a predicate device (OSCAR 15, K172180) and describes non-clinical performance and safety testing. However, it does not include specific acceptance criteria or a study that directly quantifies device performance against those criteria in a format applicable to AI/CADe devices (e.g., sensitivity, specificity, FROC analysis).

The document is concerned with demonstrating that the devices (OSCAR 15 and the newly added OSCAR 15i) function safely and effectively as fluoroscopic X-ray systems, primarily through comparison to a previously cleared predicate device and compliance with relevant IEC and CFR standards. It describes physical and technical specifications and differences, particularly in the image acquisition parts (Flat Panel Detector vs. Image Intensifier).

Therefore, based on the provided text, I cannot complete the requested tables and information for acceptance criteria and a study proving the device meets those criteria in the way typically expected for AI/CADe devices, as this information is not present. The document focuses on regulatory compliance and substantial equivalence for an imaging hardware device, not an AI/CADe algorithm.

Here's what can be extracted and what is missing based on your request:

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

• Acceptance Criteria: Not explicitly stated in terms of specific performance metrics (like sensitivity, specificity, accuracy) that would be common for AI/CADe devices. The acceptance criteria for this type of device are primarily compliance with safety and performance standards (e.g., IEC 60601 series, 21 CFR 1020.30, 1020.31, 1020.32) and demonstrating "substantial equivalence" to a predicate device.
• Reported Device Performance: The document states that "the performance related to image quality was also different" due to the detector change, and "The image performance was evaluated according to the IEC standard through performance bench testing demonstrated that these differences do not matter and effectiveness in comparison with the predicate device." It also mentions "clinical images have been evaluated by a licensed radiologist confirmed the sufficient diagnostic quality to provide accurate information." However, no quantified performance metrics are provided.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size: Not specified. The document mentions "performance bench testing" and evaluation of "clinical images" but does not give a number for images or cases used in these evaluations.
• Data Provenance: Not specified.

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)

• Number of Experts: One ("a licensed radiologist") is mentioned for evaluating clinical images.
• Qualifications of Experts: Only "a licensed radiologist" is mentioned; no specific experience level or sub-specialty is provided.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Adjudication Method: Not specified. Only one radiologist is mentioned for evaluation, implying no consensus/adjudication process was detailed.

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 study targeting human reader improvement with AI assistance was not mentioned. The device is a fluoroscopy system, not an AI/CADe tool for interpreting images.

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

• Standalone Performance: No, this does not apply. The device is a fluoroscopy system, a hardware imaging device, not an algorithm being evaluated in a standalone capacity.

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

• Type of Ground Truth: For the "clinical images," the "sufficient diagnostic quality" was confirmed by a "licensed radiologist." This implies a form of expert opinion/judgment, but it's not explicitly framed as establishing a ground truth for a diagnostic algorithm. For the hardware performance, ground truth would be adherence to physical and electrical specifications verified through bench testing.

8. The sample size for the training set

• Training Set Sample Size: Not applicable/not mentioned. This device is a hardware fluoroscopy system, not an AI model requiring a training set.

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

• Ground Truth for Training Set: Not applicable/not mentioned, as there is no AI model or training set described.

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OSCAR 15 is a mobile fluoroscopy system is designed to provide fluoroscopic and spot film images of the patient during diagnostic, surgical and interventional procedures. Examples of clinical application may include cholangiography, endoscopy, urologic, orthopedic, neurologic, vascular, cardiac and critical care.

The system may be used for other imaging applications at the physician's discretion.

OSCAR 15 is consist of X-ray Tube, X-ray tube assembly, x-ray controller, detector and accessories. There is no wireless function in this device.

The OSCAR 15, C-Arm Mobile is the device intended to visualize anatomical structures by converting a pattern of x-radiation into a visible image through electronic amplification. This device is used for providing fluoroscopic and radiographic images of patient anatomy, especially during the special procedures in a hospital or medical clinics. The fluoroscopic mode of operation is very useful to the attending physician to see the images on real time without the need to develop individual films.

The provided text is a 510(k) summary for the OSCAR 15 mobile fluoroscopy system. It is a submission to the FDA to demonstrate substantial equivalence to a predicate device, not a study proving the device meets specific acceptance criteria in the context of AI/ML performance.

Therefore, most of the requested information regarding acceptance criteria, reported device performance, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth types, and training set details cannot be extracted from this document as it pertains to AI/ML device validation.

Here's what can be extracted and a general explanation for the missing information:

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

The document does not present specific acceptance criteria in terms of numerical performance metrics for an AI/ML component. Instead, it focuses on demonstrating substantial equivalence to a predicate device (ZEN-7000) based on design features, indications for use, and compliance with industry standards for safety and electrical performance.

The reported device performance is primarily in relation to physical and technical specifications, and safety/EMC compliance.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This document describes a medical imaging device (C-arm fluoroscopy system), not an AI/ML algorithm. Therefore, there is no "test set" in the context of an AI/ML model. The evaluation is based on engineering tests, compliance with standards, and comparison of specifications with a predicate device. "Bench and clinical evaluation" is mentioned, suggesting some testing with human interaction, but no details on sample size or data provenance for such evaluations are 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)

Not applicable, as this is not an AI/ML device requiring expert-labeled ground truth for model validation.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable, as this is not an AI/ML device.

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 a fluoroscopy system, which provides images directly to the physician. It does not include an AI assistance component whose effectiveness would be measured in an MRMC study.

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

Not applicable, as this is not an AI/ML device with an "algorithm only" performance to evaluate. The device itself is the standalone product.

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

Not applicable, as the evaluation is based on technical specifications, safety standards compliance, and image quality metrics (DQE, resolution, etc.), not the diagnostic accuracy of an AI/ML model against a clinical ground truth.

8. The sample size for the training set

Not applicable, as this is not an AI/ML device.

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

Not applicable, as this is not an AI/ML device.

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