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Hitachi's PROBEAT with DSSS is a medical device designed to produce and deliver a proton beam for the treatment of patients with localized tumors and other conditions susceptible to treatment by radiation.

The PROBEAT with DSSS is a proton beam irradiation system, which provides a therapeutic proton beam for clinical treatment. It is designed to deliver a proton beam with the prescribed dose, dose distribution and directed to the prescribed patient treatment site. The PBTS is designed to be safe and reliable. The equipment to perform the above work is comprised of two main components. One is a beam delivery system whose primary responsibility is to ensure that the desired prescription parameters are properly delivered. The other is the equipment necessary to generate the proton beam and direct it to the beam delivery system.

The provided text describes a Special 510(k) submission for the PROBEAT with DSSS, a modification to an existing proton beam therapy system. This type of submission is used for devices that are substantially equivalent to a predicate device and have only minor modifications that do not raise new questions of safety or effectiveness. As such, the submission primarily focuses on demonstrating substantial equivalence rather than presenting extensive new performance data or clinical studies like those for novel devices.

Therefore, many of the typical acceptance criteria and study details you requested for AI/software-based medical devices are not explicitly mentioned in this document. The document states that performance testing was conducted, but it does not detail specific acceptance criteria in terms of quantitative metrics, sample sizes, or expert involvement as one would expect for an AI algorithm.

Here's an attempt to answer your questions based on the provided text, indicating where information is not available:

1. Table of Acceptance Criteria and Reported Device Performance

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

Not applicable in the context of this 510(k) summary for a hardware modification to an existing proton therapy system. The "test set" would likely refer to engineering and system validation tests rather than a clinical dataset in the AI sense. The document does not specify details of these tests, their sample sizes, or data provenance.

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

Not applicable. This information is not provided. Ground truth in this context would likely be based on physical measurements and engineering specifications, not expert interpretation of data.

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

Not applicable. This information is not provided.

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 applicable. This is a hardware modification for a proton beam therapy system, not an AI-based diagnostic or assistive device that would involve human readers.

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

Not applicable. This refers to the performance of the overall Proton Beam Therapy System with the added DSSS component, which is a physical beam delivery system, not an algorithm.

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

Not explicitly stated. For a proton beam therapy system, ground truth for performance testing would typically involve:

• Physical measurements: Verifying beam parameters (dose, energy, shape, position) using phantoms, dosimeters, and other radiation detectors.
• Engineering specifications: Comparing actual performance against designed specifications.
• Pre-clinical verification: Potentially using biological samples or animal models to assess beam effects (though not explicitly mentioned here for this modification).

8. The sample size for the training set

Not applicable. This is a hardware modification to an existing proton therapy system. The concept of a "training set" is relevant for machine learning algorithms, which is not the primary subject of this 510(k) summary.

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

Not applicable for the same reasons as above.

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Hitachi's PROBEAT is a medical device designed to produce and deliver a proton beam for the treatment of patients with localized tumors and other conditions susceptible to treatment by radiation.

The PROBEAT is a proton beam irradiation system, which provides a therapeutic proton beam for clinical treatment. It is designed to deliver a proton beam with the prescribed dose and dose distribution to the prescribed patient treatment site. The equipment is comprised of two main components. One is a beam delivery system whose primary responsibility is to ensure that the above listed prescription parameters are properly delivered. The other is the equipment necessary to generate the proton beam and direct it to the beam delivery system.

The provided text describes Hitachi's PROBEAT, a proton beam therapy system, and its 510(k) submission for FDA clearance. However, it does not contain detailed information about specific acceptance criteria or a study designed to prove the device meets those criteria in the way typically required for AI/ML-based medical devices (e.g., performance metrics like sensitivity, specificity, or AUC against a ground truth).

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than providing a detailed performance study with quantitative results against established acceptance criteria.

Here's an attempt to answer your questions based only on the provided text, highlighting where information is absent:

1. Table of Acceptance Criteria and Reported Device Performance

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

Information regarding a specific "test set" with a defined sample size for evaluating the PROBEAT's performance as a medical device is not provided. The document mentions general "performance testing" but does not detail the nature of this testing (e.g., patient data, phantom studies) or its provenance.

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

Information about experts establishing ground truth for a test set is not provided. This type of detail is usually relevant for diagnostic devices or those involving human interpretation, which is not the primary focus of this submission for a radiation therapy delivery system.

4. Adjudication Method for the Test Set

Information on an adjudication method for a test set is not provided.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Its Effect Size

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study, which typically evaluates human reader performance with and without AI assistance, was not mentioned and is not relevant for this type of device (a proton beam therapy delivery system). This device delivers the therapy, it does not assist human readers in making diagnoses or interpretations of images in the same way an AI diagnostic tool would.

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

The document refers to "performance testing" conducted by Hitachi but does not explicitly describe it as a "standalone" algorithm-only performance study in the context of AI. The PROBEAT is a physical system, not primarily a software algorithm for interpretation. The performance testing would be focused on the hardware's ability to create and deliver the proton beam accurately and safely.

7. The Type of Ground Truth Used

The document does not specify the type of ground truth used for the "performance testing." For a proton beam therapy system, ground truth would likely involve:

• Physical measurements using dosimeters in phantoms to verify dose distribution and accuracy against planned treatment.
• Engineering specifications and calibration standards.
• Possibly pre-clinical studies or simulations.

8. The Sample Size for the Training Set

Information about a "training set" is not provided. As this is a proton beam therapy delivery system, and not an AI/ML-based diagnostic or predictive algorithm, the concept of a "training set" for the device's core function (beam generation and delivery) is not applicable in the same way it would be for an AI algorithm. Its design and engineering would be based on physics principles and engineering validation, not statistical learning from a large dataset.

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

Information about how ground truth for a "training set" was established is not provided, as the concept of a training set is not applicable to the description of the device's validation in this document.

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