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The MiniCAT is an X-ray imaging device that constructs a three dimensional model of the head and neck from images taken during a rotational X-ray sequence. The MiniCAT is optimized for the imaging of the maxillofacial complex, temporal bone, sinuses, and for neuro-angiography.

The MiniCAT is a dedicated X-ray imaging device that acquires a 360-degree rotation x-ray sequence of images. The MiniCAT consists of both software and hardware components. The software is used for image acquisition, processing, and viewing and consists of the following main functional units patient database, study (scan) acquisition, study reconstruction, study view, report generation, and file saving. The software also controls the hardware components of the device. The major hardware components include the X-ray source, scanning arm (motor), and detector.

This submission is for a special 510(k) to address reported software failures in the MiniCAT device, specifically regarding a forced 10-minute delay between scans, running the software as a Windows shell, a daily calibration date checking function, and DICOM image unique identifiers compliance. This is a modification to a previously cleared device (K032243). The focus of the performance data is on software verification and validation, as the changes are software-only and do not raise new questions of safety or effectiveness regarding the core imaging performance.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The provided text does not specify a sample size for a test set in the context of clinical image evaluation, nor does it mention data provenance (e.g., country of origin, retrospective or prospective) for such a test set. This submission is for software modifications, and the "performance data" section focuses on software verification and validation.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of those Experts

The provided text does not mention the use of experts to establish a ground truth for a test set. The submission focuses on software functionality rather than diagnostic performance assessed by human readers.

4. Adjudication Method for the Test Set

The provided text does not mention an adjudication method. As no expert review or test set is described, adjudication would not be applicable within the scope of this regulatory submission.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the Effect Size of Improvement

An MRMC comparative effectiveness study was not done or at least not reported in this submission. The submission is for software modifications to an existing device, not for a new device requiring a comprehensive clinical performance evaluation with human readers.

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

A standalone performance study of the algorithm's diagnostic capabilities (without human-in-the-loop) was not explicitly described for this software modification. The performance data focuses on verification and validation of the software changes themselves. The core imaging algorithm's standalone performance would have been assessed in the initial 510(k) for the MiniCAT (K032243).

7. The Type of Ground Truth Used

For the software modifications, the "ground truth" was programmatic and functional:

• Compliance with specifications: The "forced 10-minute delay" was verified to correctly implement the delay.
• Operating system compatibility: "Running as a Windows shell" was verified for correct integration.
• Warning message functionality: The "daily calibration date checking function" was verified to display the warning under the correct conditions.
• DICOM standard adherence: "DICOM image unique identifiers (UIDs) have been brought into compliance" was verified against the DICOM standard.

There is no mention of pathology, outcomes data, or expert consensus being used as ground truth for diagnostic performance, as that was not the scope of this specific software modification submission.

8. The Sample Size for the Training Set

The provided text does not mention a training set or its sample size. This submission is about software modifications, not the development or training of a new AI/machine learning algorithm.

9. How the Ground Truth for the Training Set was Established

Since there is no mention of a training set, there is no information on how its ground truth was established.

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XoranConnect is a medical imaging software system intended to display, edit, review, store, print and distribute images acquired from imaging devices such as Computed Tomography (CT), Magnetic Resonance (MR), Computed Radiography (CR), Ultrasound (US), Nuclear Medicine (NM), and other devices.

XoranConnect™ is a web-based service designed to complement point-of-care medical imaging systems by providing point-of-expertise viewing and interpretation tools. XoranConnect™ runs on standard offthe-shelf PC and networking hardware, all of which has been manufactured by third party reputable manufacturers.

The system performs these major functions.

• Off-site data storage allows user studies to be stored off-site. .
• Study viewer allows studies to be visualized and manipulated in 3D, multiplanar and other forms. .

To accommodate the major functions, the overall system architecture includes the following distinct components:

• XoranVault™ storage system (storage servers, dispatcher) .
• XoranConnect™ viewing system (study server, web server, web client) .

The storage (backup) system consists of one or more servers designed to store and serve stored imaging content on a RAID5 storage device. Imaging modality negotiates the connection and transfers study to the storage server.

Once stored, imaging data is ready to be served to the requesting clients. When a user logs into the system by visiting a XoranConnect web page, study server begins to serve the data, which in turn is displayed in the user's web browser window. Any storage server may simultaneously serve multiple requests for images.

It is the user's responsibility to ensure that the appropriate PC hardware and display device are used to run the XoranConnect™ software application. XoranConnect™ requires at all times an active Internet connection to operate. It is the user's responsibility to ensure that the connection that is being used provides the speed, stability and reliability required to utilize XoranConnect™ for any given purpose or application.

The provided text describes XoranConnect™, a web-based service for medical imaging, and its 510(k) premarket notification. However, the document does NOT contain information about specific acceptance criteria for device performance, nor does it detail any study that proves the device meets such criteria.

The 510(k) summary focuses on demonstrating substantial equivalence to predicate devices (Visage PACS/CS and NovaPACS) based on similar functions and intended use, rather than presenting a performance study with defined acceptance criteria.

Therefore, most of the requested information cannot be extracted from the provided text.

Here's a breakdown of what can be inferred from the document regarding a "study":

1. Table of acceptance criteria and reported device performance:
Not present in the document. The submission is based on substantial equivalence, not on head-to-head performance against specific criteria.

2. Sample size used for the test set and the data provenance:
Not applicable. No performance study with a test set is described.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable. No performance study with ground truth establishment is described.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
Not applicable. No performance study is described.

5. Multi reader multi case (MRMC) comparative effectiveness study:
No, an MRMC comparative effectiveness study is not mentioned. The submission focuses on functional equivalence to predicate devices.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) study:
Not applicable. XoranConnect™ is a PACS system designed for human interpretation, and no standalone algorithmic performance study is described.

7. The type of ground truth used:
Not applicable. No performance study requiring ground truth is described.

8. The sample size for the training set:
Not applicable. XoranConnect™ is a PACS system, not an AI/ML algorithm that typically requires a training set.

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

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The XCAT is intended to be used for x-ray computed tomography imaging of anatomy that safely fits into the imaging gantry (such as the head, neck, wrist, ankle, hand, and foot).

The xCAT™ is a dedicated X-ray imaging device that acquires a 360° rotational X-ray sequence, reconstructs a three-dimensional matrix of the examined volume and produces two dimensional views of this volume. The xCAT™ can measure distances and thickness on two dimensional images. Images produced by the xCAT can be exported via Ethernet or onto optical media or a memory stick. The building blocks of the xCAT are a motorized scanning arm carrying an Xray source and image detector, and a computer running the xCAT software. The scanning arm facilitates the acquisition of a full X-ray sequence by the software. The software receives the two dimensional images acquired by the detector transforms them into three dimensional images and displays them on the computer monitor for viewing.

The provided 510(k) summary for the Xoran xCAT™ does not contain information regarding specific acceptance criteria, a study proving device performance against such criteria, or details typically found in a clinical or performance study report. The document focuses on establishing substantial equivalence to predicate devices rather than proving performance against defined metrics.

Therefore, most of the requested information cannot be extracted from the provided text.

Here's what can be stated based on the text:

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

• Not provided. The document does not specify any quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy, resolution, noise levels) or report specific performance metrics for the xCAT™ beyond its intended imaging capabilities and reconstruction process.

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

• Not provided. No test set, sample size, or data provenance are mentioned, as a formal performance study against detailed acceptance criteria is not described.

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 provided. Ground truth establishment is not discussed, as there is no described test set or performance evaluation.

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

• Not provided. Adjudication methods are not applicable as no test set or ground truth establishment process is described.

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 provided. An MRMC study is not mentioned. The xCAT™ is described as an imaging device that produces images for human viewing and measurement, without explicit mention of AI assistance or human reader performance improvement studies.

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

• Not provided. The document describes the device as producing images for viewing and measurement by a user, implying human intervention. There is no mention of a standalone algorithm performance evaluation without human-in-the-loop.

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

• Not provided. As no performance study is detailed, the type of ground truth used is not specified.

8. The sample size for the training set:

• Not provided. The document describes the xCAT™ as an imaging device that reconstructs 3D images from X-ray sequences. It does not mention any machine learning or AI components that would require a "training set."

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

• Not provided. This is not applicable, as no training set or AI component requiring ground truth for training is described.

Summary of what the document does provide regarding device evaluation:

The 510(k) submission establishes substantial equivalence to predicate devices (Neurologica's NL 3000 CereTom and Xoran Technologies Inc. MiniCATTM) rather than providing a detailed performance study with specific acceptance criteria. The basis for clearance is that its design, material, functionality, and technology are comparable to these existing, legally marketed devices, and it does not pose new safety or effectiveness issues.

The "study" implicitly referenced is the comparison to predicate devices, which allows the FDA to conclude that the device is substantially equivalent for its intended use. Potential hazards are stated to be controlled by a risk management system, including Hazard Analysis and Software Development and Validation Process, but no details of these processes or their outcomes are provided.

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The MiniCAT™ is an X-ray imaging device that constructs a three dimensional model of the head and neck are from images taken during a rotational X-ray sequence. The MiniCAT™ is optimized for the imaging of the maxillofacial complex, temporal bone, sinuses, and for neuro-angiography.

The MiniCAT™ is a dedicated X-ray imaging device that acquires a 360° rotational Xray sequence, reconstructs a three-dimensional matrix of the examined volume and produces two dimensional views of this volume. The MiniCAT™ can measure distances and thickness on two dimensional images. Images produced by the MiniCAT™ can be printed or exported on magnetic and optical media.

The building blocks of the MiniCAT™ are a motorized scanning arm carrying an X-ray source and image detector, and a computer running the MiniCAT™ software. The scanning arm facilitates the acquisition of a full X-ray sequence by the software. The software receives the two dimensional images acquired by the detector transforms them into three dimensional images and displays them on the computer monitor for viewing.

The provided text describes the MiniCAT™ imaging device and its 510(k) submission to the FDA. However, it does not contain any information regarding acceptance criteria or a study that proves the device meets specific performance criteria.

The document states that the MiniCAT™ "complies with the requirements of 21 CFR 807.87(h) and does not pose any new safety risks or effectiveness issues.". This is a regulatory statement confirming substantial equivalence to predicate devices, but it does not detail specific performance metrics, acceptance criteria, or a study to demonstrate these. The document focuses on device description, intended use, and comparison with predicate devices to establish substantial equivalence.

Therefore, I cannot provide the requested table or details about a performance study based on the input given.

Summary of missing information:

• No acceptance criteria are mentioned.
• No study proving the device meets acceptance criteria is described.
• No performance metrics for the device are reported.
• No sample size for test or training set, data provenance, expert qualifications, or ground truth methods are provided.
• No multi-reader multi-case (MRMC) or standalone study details are available.

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