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The NOMOS® CRANE II is intended to be used as an accessory to powered, radiation therapy patient support assemblies. The CRANE II verifies and describes, via a set of X, Y and Z coordinates, the set-up of the couch/patient prior to treatment.

The NOMOS CRANE II is an accessory to powered radiation therapy patient support assemblies. The NOMOS CRANE II attaches to the stationary base of the treatment table through a permanently installed interface plate and clamps to the accessory rails of the moveable couch top. A treatment table foot brake is also installed, if not already part of the treatment table, to further stabilize the stationary base. The treatment table foot brake is typically an accessory that can be purchased from the treatment table original equipment manufacturer (OEM). In cases where the foot brake is not an OEM accessory, one will be adapted or custom designed for the application. The CRANE II can be removed from the treatment table with only the interface plate remaining on the stationary base of the treatment table.

The CRANE II uses precision ball screw drive positioning tables, one for the Y-axis and one for the X-axis, to position the couch top relative to the stationary base of the treatment table. The positioning tables are operated using a small hand crank.

Each axis of the CRANE II incorporates a battery powered digital scale that measures the movement of the positioning table and therefore, the movement of the couch top itself. The digital scale may be set to display the value in inches, however, the operator's manual instructs the user to set the digital scale to display millimeters With the CRANE II attached, the couch top can be manipulated over a range of 12 inches (304.8 mm) in both the Y and X axis with no impedance on the couches range of travel in the Z-axis.

Here's an analysis of the provided text regarding the NOMOS® CRANE II, focusing on the acceptance criteria and study details:

Acceptance Criteria and Device Performance for NOMOS® CRANE II

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample Size and Data Provenance:

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective nature). It only mentions that validation data for the more precise accuracy claim was included in "Appendix 5 of this submission."

3. Number and Qualifications of Experts for Ground Truth:

The document does not mention the number or qualifications of experts used to establish the ground truth for the test set.

4. Adjudication Method:

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) for the test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

No mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study is made in the provided text. Therefore, no effect size of human readers improving with AI vs. without AI assistance can be determined. This device is a caliper, not an AI system.

6. Standalone Performance Study:

A standalone performance study was done to validate the accuracy claim of ± 0.1 mm. The specific details of this study are not provided in the summary but are referenced as being in "Appendix 5 of this submission." The device itself acts as a measurement tool, so its "standalone" performance refers to its ability to accurately measure without human interpretation of images.

7. Type of Ground Truth Used:

The type of ground truth used is not explicitly stated, but given the nature of the device (a caliper for measuring positioning), it would most likely be a reference standard measurement obtained through a highly precise and validated alternative method, or an established physical dimension.

8. Sample Size for the Training Set:

The concept of a "training set" is not applicable to this device, as it is a mechanical measuring tool and not an AI/machine learning system. Therefore, no information on training set sample size is provided.

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

As the device is not an AI/machine learning system, there is no "training set" or corresponding ground truth establishment process in that context.

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