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The TomoTherapy HI-ART System is intended to be used as an integrated system for the planning and precise delivery of radiation therapy, stereotactic radiotherapy, or stereotactic radiosurgery to tumors or other targeted tissues while minimizing the delivery of radiation to vital healthy tissue. The megavoltage x-ray radiation is delivered in a rotational, non-rotational, modulated (IMRT), or non modulated (non-IMRT/three dimensional conformal) format in accordance with the physician approved plan.

The TomoTherapy HI-ART System is a radiation therapy system that integrates planning, dose calculation, megavoltage CT scanning for IGRT functionality, and helical radiation therapy treatment capabilities into a single comprehensive system. The megavoltage CT image is not for diagnostic use. This modification provides a licensable option that adds the capability for fixed beam treatments from multiple user defined angles as well.

The provided text is a 510(k) Premarket Notification for the TomoTherapy HI-ART System, which is a radiation therapy system. It describes the device, its intended use, safety considerations, and compliance with standards. However, it does not contain the detailed information necessary to complete a table of acceptance criteria and reported device performance, nor does it describe a study in the way typically required for AI/ML-based medical devices.

The document is a traditional 510(k) submission for a modified radiation therapy system, not for an AI/ML-based diagnostic or prognostic device that would involve performance metrics like sensitivity, specificity, or reader studies. The validation section mentions "system functionality, including planning, imaging, delivery, database management, DICOM communications, etc." and that "Test tools utilized in this testing included IMRT phantoms, ion chambers and other test phantoms." This indicates performance validation against physical phantoms and established engineering/system metrics, rather than clinical performance based on patient data and ground truth as would be the case for an AI/ML device.

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

Here's a breakdown of what can be extracted and what cannot:

1. Table of acceptance criteria and the reported device performance:

• Acceptance Criteria: Not explicitly stated in terms of quantitative performance metrics (e.g., accuracy, sensitivity, specificity) for clinical outcomes in the provided text. The document refers to compliance with safety standards and system functionality.
• Reported Device Performance: Not detailed in terms of clinical performance metrics. It mentions validation using IMRT phantoms and ion chambers, implying physical measurement of radiation delivery accuracy and dose calculation, but specific numerical results or criteria are not provided.

2. Sample size used for the test set and the data provenance: Not applicable in the context of clinical data for AI/ML. The validation involved test tools like phantoms.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for phantoms is typically derived from physical measurements and established parameters, not expert consensus on clinical findings.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.

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: No. This is not an AI/ML device, and no MRMC study is mentioned. The device assists in planning and delivering radiation, not in interpreting medical images or making diagnoses.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The device is a system with human-in-the-loop for planning and operation. The validation concerns the system's performance, not an isolated algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For the described validation, the ground truth would be based on physical measurements of dose, system functionality, and compliance with engineering specifications when tested with phantoms and other test tools. It is not clinical ground truth from patient data.

8. The sample size for the training set: Not applicable. This is not an AI/ML device that requires training data in the computational sense.

9. How the ground truth for the training set was established: Not applicable.

Summary based on the provided text:

Reported Device Performance: Validation included "system functionality, including planning, imaging, delivery, database management, DICOM communications, etc." No specific numerical performance values are provided. |
| 2. Sample size for test set & data provenance | Not applicable for clinical data. Validation involved "IMRT phantoms, ion chambers and other test phantoms." The 'sample size' would pertain to the set of tests conducted on these physical phantoms. |
| 3. Number of experts & qualifications for ground truth | Not applicable. Ground truth for phantom testing is based on known physical properties and measurements, not expert consensus on clinical findings. |
| 4. Adjudication method for test set | Not applicable. |
| 5. MRMC comparative effectiveness study | No. This document does not describe an AI/ML device or an MRMC study. |
| 6. Standalone (algorithm only) performance study | Not applicable. The device is an integrated system (hardware and software) with human-in-the-loop operation for radiation therapy planning and delivery. |
| 7. Type of ground truth used | For the validation described, the ground truth would be based on established physical measurements and engineering specifications for system components when tested with IMRT phantoms and ion chambers. It is not expert consensus, pathology, or outcomes data from patients. |
| 8. Sample size for training set | Not applicable. This is not an AI/ML device that undergoes a training phase with a dataset. |
| 9. How ground truth for training set was established | Not applicable. |

Conclusion:

The provided document describes a 510(k) submission for a conventional medical device (radiation therapy system) with modifications, not an AI/ML-driven device. Therefore, the request for detailed information on acceptance criteria, reader studies, and training/test set specifics, which are typical for AI/ML device evaluations, cannot be fulfilled from this text. The "study" mentioned is a system validation against engineering standards and physical test phantoms, rather than a clinical performance study with patient data and expert ground truth.

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