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The IKOEngelo™ System is indicated for use by radiation oncologists, medical physicists, and medical dosimetrists for tumor and normal tissue contour delineation to support the radiotherapy treatment planning process. The resulting information may then be exported to a treatment planning system for dose calculation.

The IKOEngelo version 2.0 device is a software system upgraded from version 1.0. This submitted new version has better contour modification tool, faster image files loading and display, and a new function for image fusion. For the same purpose of version 1.0, this software will assist radiation oncologists with the assistance of physicists and dosimetrists to more efficiently perform contour delineation of the tumor target and normal tissue on patient's CT images.

The provided document is a 510(k) summary for the IKOEngelo™ Software Version 2.0. It describes the device's intended use and technological characteristics, and importantly, highlights the verification and validation aspects. However, it does not contain a detailed study proving the device meets explicit acceptance criteria in the format requested.

The document states that "The validation test results have demonstrated that the contour modification tools were improved with efficiency and versatility, the image file loading and display were faster and the new image fusion functions were similar to the predicate device." This is a general statement about meeting validation goals rather than specific acceptance criteria with quantitative performance metrics.

Therefore, many of the requested fields cannot be filled directly from the provided text.

Here is an attempt to structure the available information, with clear indications where information is not present in the document:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not specified.
• Data Provenance (country of origin, retrospective/prospective): Not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

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

• Adjudication Method: Not specified.

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: Not specified or implied. The device is a software system to assist radiation oncologists, physicists, and dosimetrists, but no comparative effectiveness study with human readers (with vs. without AI assistance) is mentioned.
• Effect Size: Not applicable, as no MRMC study is detailed.

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

• Standalone Performance: Not explicitly stated as a standalone evaluation. The validation statement indicates "The validation test results have demonstrated that...", suggesting an evaluation of the software's functionality, but it's unclear if this was algorithm-only or if it involved human interaction/evaluation of the output. The device's intended use is to "assist" human experts, implying it's not a standalone diagnostic tool.

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

• Type of Ground Truth: Not specified. The document mentions "contour delineation of the tumor target and normal tissue on patient's CT images" as the core function, but how the "truth" for these contours was established for validation is not described.

8. The sample size for the training set

• Sample Size: Not specified. (The document describes validation, not specifically training of a machine learning model, though the term "improved" tools could imply some form of iterative development and testing).

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

• Ground Truth Establishment: Not specified. (As above, training set details are not provided).

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The IKOEngelo™ System is intended for use in tumor and normal tissue contour delineation to support the radiotherapy treatment planning process.
The IKOEngelo™ System is indicated for use by radiation oncologists, medical physicists, and medical dosimetrists for tumor and normal tissue contour delineation to support the radiotherapy treatment planning process. The resulting information may then be exported to a treatment planning system for dose calculation.

The IKOEngelo device is a software system that will assist radiation oncologists, with the assistance of physicists and dosimetrists, to more efficiently perform contour delineation of the tumor target and normal tissue on patient's CT images.
The sequence of events is illustrated in the following bullet items and diagram:

• Import patient's CT images. .
• Select the proper Expert Case (including the CT image data set and . contours) to match patient's CT.
• Automatically fuse the images to align patient's CT image data sets . with those of the Expert Case.
• Run deformable segmentation to auto-contour on the patient's CT . images.
• Review patient's contours and modify them if necessary. .
• Approval by qualified radiation oncologist. .
• Export patient's CT with its contours to the treatment planning system . used by the facility.

The provided text does not contain detailed information about specific acceptance criteria or the study that definitively proves the device meets such criteria. It primarily focuses on the device's description, intended use, and a comparison to a predicate device for 510(k) submission. Therefore, much of the requested information is not available in the provided document.

However, based on the information that is present, here's what can be extracted and inferred:

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

The document does not explicitly state acceptance criteria or provide a table of reported device performance against those criteria. It offers a "Predicate Comparison Table" which outlines functional equivalence to the QwikSIM Virtual Simulation System (K013531). This comparison serves as the basis for demonstrating substantial equivalence for regulatory purposes, rather than a direct performance study against defined acceptance criteria.

Predicate Comparison Table (Indicating Functional Equivalence, not specific performance metrics against acceptance criteria):

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

This information is not provided in the document. The document describes a general comparison to a predicate device but does not detail a specific test set, its size, or data provenance (e.g., country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

This information is not provided in the document.

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

This information is not provided in the document.

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:

This information is not provided in the document. The document describes the device as a software system to assist radiation oncologists, physicists, and dosimetrists, implying a human-in-the-loop scenario. However, no MRMC study or its results are mentioned.

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

The document states the device will assist radiation oncologists, with assistance of physicists and dosimetrists, to perform contour delineation. It also mentions "Review patient's contours and modify them if necessary" and "Approval by qualified radiation oncologist." This suggests the device is intended for use with human oversight and modification, not as a standalone, fully automated system without human-in-the-loop. Therefore, it is highly likely that a standalone performance study as an algorithm without human interaction was not the primary focus or reported.

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

This information is not provided in the document. The "Expert Case Library" is mentioned as a "Contour Source," which implies pre-existing contours were used, likely established by experts, but the method for their establishment as "ground truth" for validation is not described.

8. The sample size for the training set:

This information is not provided in the document. The document mentions "Select the proper Expert Case (including the CT image data set and contours) to match patient's CT" and "Run deformable segmentation to auto-contour on the patient's CT images." This implies an underlying model that would have been trained, but no details of the training set size are given.

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

This information is not provided in the document. While "Expert Case Library" is identified as a contour source, the method of how those "Expert Cases" (and their contours) were established as ground truth for training (or for the general functioning of the system) is not detailed.

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