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The VeriSuite patient position system is used for verification and correction of the patient's position during a radiotherapy treatment with external beams or charged particles. It is based on stereoscopic X-ray images and DRRs calculated from a CT image series of the treatment region of the patient and information from the treatment planning.

VeriSuite is an image processing system for verification and correction of the patient position during a radiation therapy treatment. The verification or correction is performed by a comparison of X-ray images that are acquired during the treatment with DRRs (digital reconstructed radiographs) calculated from a CT image series of the patient and information from the radiation therapy planning, The correction can also be based on fiducial, radio-opaque markers that are implanted in the patient.

VeriSuite is a system of devices consisting of the VeriSuite software and a number of hardware devices:

• Beam limiting collimator device (Ralco 302)
• X-ray generator (Sedecal XHF 835) -
• X-ray tubes (Varian A277 or A272)
• Flat panel digital imager (Varian 4030E -
  Optional:
  All these hardware devices are legally marketed in the US as listed in previous section D.

This document, a 510(k) submission for VeriSuite 1.8, does not contain information regarding detailed acceptance criteria, the methodology of a study to prove device performance against these criteria, or specific data pertaining to ground truth establishment, expert qualifications, or sample sizes for training and test sets.

The document primarily focuses on:

• Device Identification: Trade names, common names, classification, and product codes of VeriSuite and its components.
• Predicate Device: Details of VeriSuite 1.6 and other predicate devices (X-Ray Generator, X-Ray Tubes, Flat Panel Digital Imager, Collimator).
• Device Description: VeriSuite is an image processing system for verifying and correcting patient position during radiation therapy by comparing X-ray images with DRRs or fiducial markers. It's a system comprising software and legally marketed hardware components.
• Intended Use: Verification and correction of patient position during radiation therapy using stereoscopic X-ray images and DRRs. Emphasizes evaluation and approval by an authorized person, and notes it's not for diagnostic purposes.
• Summary of Technical Considerations: States substantial equivalence to the predicate device.
• FDA Communication: A letter from the FDA confirming the review of the 510(k) premarket notification and determining substantial equivalence for the stated indications for use.
• Indications for Use Statement: A formal statement on the intended use of the VeriSuite patient position system.

Therefore, I cannot provide the requested information as it is not present in the provided text.

To answer your request, the document would need to include sections on:

1. Clinical Performance Study, Validation Study, or Bench Testing: Describing the study design, objectives, and methods for evaluating the device's accuracy and performance.
2. Acceptance Criteria: Specific quantitative thresholds for performance metrics (e.g., accuracy, precision, repeatability) that the device must meet to be considered safe and effective.
3. Study Results: The measured performance of the device against the established acceptance criteria.
4. Sample Size and Data Provenance: Details on the number of cases/patients used, where the data originated (e.g., specific hospitals, countries), and whether it was collected retrospectively or prospectively.
5. Ground Truth Establishment: How the "true" patient position or correction vector was determined for the study data, including the number and qualifications of experts involved, and their adjudication method (e.g., consensus, majority vote).
6. Standalone vs. Reader Study: Whether the device's performance was evaluated independently (algorithm only) or as part of a human-in-the-loop study (MRMC). If MRMC, the effect size of AI assistance on human readers.
7. Training Data: Information on the size and characteristics of the dataset used to train the VeriSuite algorithm (if applicable, as this is an older device submission, specific AI/ML training details might not be explicit in this format).
8. Training Ground Truth: How the ground truth for the training set was established.

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