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The IQM Integral Quality Monitor is a large-area ionization chamber intended to be used for quality assurance verification measurements and documentation of the treatment delivery accuracy (beam shape, position and dose) from medical linear accelerators used for intensity modulated radiation therapy.

The data acquired by IQM is used to compare and verify a treatment dose (delivered dose) to the expected dose and to compile treatment delivery radiation beam data over time as part of a quality assurance program.

The IQM System is designed for the verification of radiation therapy treatments delivered with a linear accelerator. The IQM consists of an electronic detector device and software used to control the device and to process and display the results. Comparisons may be made with expected / previously measured data, results reviewed and documented for quality assurance purposes, and deviations from the expected signal can be detected and reported. Where results can be presented on a segment by segment basis, the realtime reporting of results gives the user the opportunity to detect serious deviations still during treatment, potentially aiding radiation therapy professionals in increasing patient safety.

Here's an analysis of the provided text regarding the acceptance criteria and study for the IQM Integral Quality Monitor, structured according to your requested information:

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

Based on the provided text, the specific numerical acceptance criteria are limited, but the document repeatedly emphasizes performance relative to the predicate device and established standards.

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

• Sample Size for Test Set: The document mentions that "Test users at 17 sites participated in various tests." This refers to the number of sites where the device was tested, not necessarily the number of treatments or data points collected. The specific number of test cases or data points within these sites is not provided.
• Data Provenance: The testing was conducted "in multiple hospital environments where the IQM was installed and tested in the clinical workflow." While specific countries are not mentioned, the submitter's address is Koblenz, Germany, and the FDA review is for the US market, suggesting international and potentially US sites. The tests were performed "under clinically representative conditions with Elekta and Varian brand linear accelerators, commissioned and used clinically at the respective hospitals," indicating prospective data collection in real-world clinical settings, though not on patients.

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)

The concept of "ground truth" for the IQM device relates to the expected dose and beam characteristics from the linear accelerator.

• Number of Experts: This is not explicitly stated as a number of experts, but "qualified personnel, through iRT staff and hospital medical physicists and radiation therapists at the respective hospital locations" were involved in performing the tests.
• Qualifications of Experts: The experts involved were "hospital medical physicists and radiation therapists." While years of experience are not specified, their roles imply expertise in radiation therapy and linear accelerator operation/calibration.

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

The document does not describe an adjudication method with multiple independent reviewers determining ground truth for the test set. The "ground truth" seems to be derived from the expected dose and previously measured data generated by the linear accelerators themselves, which are calibrated and operated by the medical physicists. The IQM's output is then compared against these established expected values.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done.
• Effect Size of AI Improvement: Since no MRMC study was performed involving human readers and AI assistance (as the IQM is a quality assurance device, not an AI diagnostic tool), there is no reported effect size for human reader improvement. The device's purpose is to automatically verify treatment delivery, not to assist humans in interpreting images or making diagnoses.

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

Yes, the studies described are essentially standalone performance evaluations of the device (IQM). The IQM itself acts as an automated "integral quality monitor" to verify radiation therapy treatments. It "detects deviations and lack of deviations in beam intensity, shape, and position" and compares delivered doses to expected doses. While "test users" were observed for usability, the core function of the device's measurement and comparison is an algorithm-driven process without continuous human interpretation for each result.

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

The ground truth used for evaluating the IQM appears to be based on:

• Expected Dose/Beam Characteristics: The "expected dose" or "previously measured data" from the linear accelerators, established through standard calibration and treatment planning processes performed by qualified medical physicists.
• Design Specifications: The device's performance was evaluated against its own design specifications (device performance and functionality fulfill the design specifications).

8. The sample size for the training set

The document does not report a sample size for a training set. This is because the IQM Integral Quality Monitor is described as "a large-area ionization chamber intended to be used for quality assurance verification measurements." It's a measurement device, not a machine learning or AI-based diagnostic tool that typically requires a large training dataset for pattern recognition or prediction. Its operation is based on physical principles of radiation detection and comparison against established reference data, rather than learning from a training set.

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

As there is no mention of a training set for a machine learning algorithm, there is no information provided on how ground truth for a training set was established.

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