(139 days)
Atlas-Based Autosegmentation is a standalone software application that produces estimates of anatomy boundary contours needed for the creation of a radiotherapy treatment plan.
Contouring of radiation therapy targets and surrounding anatomical structures (also known as image segmentation) is a critical part of radiation treatment planning that can be extremely time consuming. Atlas-Based Autosegmentation (ABAS) is a software application that automates the contouring process using atlas-based autosegmentation. This method uses an already-segmented image set (atlas) to segment a set of new, user-input images using deformable registration algorithms. The contours ABAS generates are not usable for treatment as-is; they must be exported to a treatment planning system for editing. However, Atlas-based Autosegmentation provides a good starting point from which minimal editing is required, enabling the clinician to create a high quality treatment plan more efficiently.
Here's a summary of the acceptance criteria and study information for the Atlas-Based Autosegmentation (ABAS) device, based on the provided text:
Important Note: The provided document is a 510(k) summary, which often focuses on demonstrating substantial equivalence rather than detailed clinical performance studies. As such, information regarding specific quantitative acceptance criteria or detailed clinical trial results is limited. The document explicitly states: "Clinical trials were not performed as part of the development of this product. Clinical testing is not advantageous in demonstrating substantial equivalence or safety and effectiveness of the device since testing can be performed such that no human subjects are exposed to risk. Clinically oriented validation test cases were written and executed in-house by CMS customer support personnel. ABAS was deemed fit for clinical use."
Therefore, many of the requested sections below will reflect the absence of such clinical studies.
Acceptance Criteria and Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Functional Verification | ABAS successfully passed verification testing as documented in the "ABAS Verification Test Report." This testing ensured the system operates as designed. |
| Clinical Suitability | "Clinically oriented validation test cases were written and executed in-house by CMS customer support personnel. ABAS was deemed fit for clinical use." |
| Substantial Equivalence | Found substantially equivalent by the FDA to predicate devices (BrainLAB iPlan RT Dose (K053584), Pinnacle3 (K041577), IKOEngelo (K061006)). |
Study Details
-
Sample size used for the test set and the data provenance:
- Sample Size: Not specified in the provided document. The text mentions "clinically oriented validation test cases" but does not quantify the number of cases.
- Data Provenance: The document does not specify the country of origin of the data. The "clinically oriented validation test cases" were executed "in-house by CMS customer support personnel," implying they were likely internal or simulated datasets, not from external clinical sites. The data was retrospective as clinical trials were not performed.
-
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- This information is not provided. The phrase "clinically oriented validation test cases" suggests some form of clinical relevance, but the method and personnel for establishing ground truth are not described.
-
Adjudication method for the test set:
- Not specified. Given the testing was "in-house by CMS customer support personnel" and clinical trials were not performed, a formal adjudication process akin to clinical studies is unlikely to have occurred.
-
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, an MRMC comparative effectiveness study was not done. The document explicitly states: "Clinical trials were not performed as part of the development of this product." The device's primary function is described as providing an "initial contouring function" that requires further editing by clinicians in a treatment planning system. Therefore, a study on human reader improvement with AI assistance (i.e., human-in-the-loop performance) was not presented.
-
If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, in a sense, standalone performance was assessed through non-clinical verification and internal "clinically oriented validation test cases." The device is described as a "standalone software application that produces estimates of anatomy boundary contours." The "Summary of Non-Clinical Testing" indicates "Verification tests were written and executed to ensure that the system is working as designed," and the "clinically oriented validation test cases" were used to deem ABAS "fit for clinical use." However, quantitative metrics of accuracy, precision, etc., for this standalone performance against a defined ground truth, are not provided in this summary. It's also important to note the disclaimer that "The contours ABAS generates are not usable for treatment as-is; they must be exported to a treatment planning system for editing."
-
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Not explicitly stated. For the "clinically oriented validation test cases," the nature of the ground truth is not detailed beyond "already-segmented image set (atlas)" being used in the process. It's reasonable to infer that the "atlas" itself serves as a form of expert-derived ground truth for the autosegmentation process.
-
The sample size for the training set:
- Not specified. The device uses an "atlas-based autosegmentation" method, which implies a training set or an "atlas" library. However, the size or composition of this atlas is not mentioned. It states that users can "expand its library of atlases," suggesting a flexible and potentially user-managed training-like data source.
-
How the ground truth for the training set was established:
- The document implies that the ground truth for the model's operation is derived from "already-segmented image set (atlas)." How these initial atlas segmentations were created (e.g., by experts, manually) is not detailed in this summary.
§ 892.5050 Medical charged-particle radiation therapy system.
(a)
Identification. A medical charged-particle radiation therapy system is a device that produces by acceleration high energy charged particles (e.g., electrons and protons) intended for use in radiation therapy. This generic type of device may include signal analysis and display equipment, patient and equipment supports, treatment planning computer programs, component parts, and accessories.(b)
Classification. Class II. When intended for use as a quality control system, the film dosimetry system (film scanning system) included as an accessory to the device described in paragraph (a) of this section, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.