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The ARIA Radiation Therapy Management product is a treatment plan and image management application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites and provides tools to verify performed treatments.

The ARIA Radiation Therapy Management product is a treatment plan and image management application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites, and provides tools to verify performed treatments.

ARIA Radiation Therapy Management supports the integration of all data and images in one central database including archiving and restoration. The different ARIA Radiation Therapy Management features support the visualization, processing, manipulation and management of all data and images stored in the system. Images can also be imported through the network using DICOM, the available image import filters or by means of film digitizers.

After reviewing the provided FDA 510(k) summary for "ARIA Radiation Therapy Management," it appears that the document describes a software application for managing radiation therapy data and images, rather than an AI-powered diagnostic or assistive device that would typically undergo rigorous performance studies with specific acceptance criteria, test sets, and ground truth establishment involving expert readers.

The provided text focuses on:

• Device Description and Intended Use: Managing, storing, accessing, and modifying treatment plan and image data, and storing treatment histories.
• Changes to Predicate Device: Listing minor software feature changes, such as improved rigid registration, DICOM UI, and workflow usability.
• Summary of Performance: A generic statement that "Results of verification and validation testing showed conformance to applicable requirements specifications and assured hazard safeguards testing: functioned properly."
• Standards Conformance: Listing relevant IEC standards (e.g., IEC 61217, IEC 62366, IEC 62304).

Crucially, there is no mention of:

• Specific acceptance criteria tied to a particular performance metric (e.g., sensitivity, specificity, accuracy).
• A test set size, data provenance, or details of a study involving human readers or AI performance evaluation.
• Ground truth establishment methods, expert qualifications, or adjudication.
• MRMC studies or standalone algorithm performance.

It seems this device falls under a category where conformance to software engineering standards, functionality testing, and verification/validation against specifications are the primary means of demonstrating safety and effectiveness, rather than a clinical performance study with statistical endpoints. The changes are primarily software enhancements and re-structuring, not the introduction of a new AI algorithm for detection, diagnosis, or prediction that would require such studies.

Therefore, I cannot provide the requested information regarding acceptance criteria and performance studies in the format you've outlined because the provided document does not contain that level of detail for this specific type of device and its claimed modifications.

To answer your request based only on the provided text, the response would be:

Based on the provided FDA 510(k) summary for ARIA Radiation Therapy Management (K133572), the device is a treatment plan and image management application, and the submission primarily addresses software modifications and functional capabilities, not a new AI-powered diagnostic or assistive feature that would necessitate a clinical performance study with specific acceptance criteria measured against a defined test set.

Therefore, the detailed information requested regarding acceptance criteria, study design, sample sizes, ground truth establishment, expert involvement, and MRMC studies is not present within this document.

The document broadly states:

1. A table of acceptance criteria and the reported device performance: This information is not provided in a quantifiable table format. The summary states: "Results of verification and validation testing showed conformance to applicable requirements specifications and assured hazard safeguards testing: functioned properly." This implies the acceptance criteria were likely functional and performance specifications related to data management, accessibility, storage, and processing, rather than clinical efficacy metrics.
2. Sample sized used for the test set and the data provenance: Not specified.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not specified, as ground truth in the context of clinical interpretation/diagnosis is not relevant for this device's modifications.
4. Adjudication method for the test set: Not specified.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not specified. It is highly unlikely for this type of software management system.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not specified.
7. The type of ground truth used: Not specified, given the device's function.
8. The sample size for the training set: Not specified. (This device is not described as involving machine learning training.)
9. How the ground truth for the training set was established: Not applicable, as no machine learning training is described.

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