(34 days)
The p.d software takes a Treatment Planning System design of a compensating filter used for radiation therapy which contains steep, narrow, and unmachinable areas and then smoothes them out into a machinable surface. The customer will use the software in-house before sending the file to .decimal to be manufactured. Each filter must be QA'd by the customer before use on a patient.
.decimal created a software translator to be used with a Radiation Therapy Treatment Planning Systems (TPS). The software takes the TPS design of a compensating filter used for radiation therapy which contains steep, narrow, and unmachinable areas and then smoothes them out into a machinable surface.
The provided text, K061440, describes a software translator called "p.d" designed to smooth out unmachinable areas in radiation therapy compensating filters. The document is a 510(k) summary for a premarket notification to the FDA in 2006.
However, the K061440 document does not contain the detailed acceptance criteria or a study that addresses the specific points requested in the prompt. It provides general information about the device, its intended use, and substantial equivalence to a predicate device, but lacks the specific performance data, sample sizes, expert qualifications, or study methodologies that would typically be found in a comprehensive validation or clinical study report.
Here's an analysis of what is and is not present in the provided text in relation to your request:
1. Table of acceptance criteria and the reported device performance:
- Not present. The document states that "p.d Software Validation" (PD-11) is attached in Section 11 and "Summary of Clinical Testing" is in Section 12, but these sections are not included in the provided text. Therefore, specific acceptance criteria (e.g., maximum deviation from original design, smoothing parameters, processing time) and quantitative performance metrics are not available.
2. Sample size used for the test set and the data provenance:
- Not present. The document mentions "clinical testing" but does not provide any details about the test set size, data origin (country), or whether it was retrospective or prospective.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not present. There is no mention of experts, ground truth establishment, or their qualifications. The intended use states the customer verifies the filters, but this is an operational process, not a ground truth methodology for a study.
4. Adjudication method for the test set:
- Not present. Without details of a test set or ground truth establishment, an adjudication method is not mentioned.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:
- Not present. This type of study is not mentioned. The device is a software translator for manufacturing, not an AI-assisted diagnostic tool that human readers would interpret.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Implicitly yes, but no details of the study. The device is a "software translator" that takes a TPS design and smoothes it out. Its function is inherently standalone in that it performs an automated transformation of a digital design. However, there is no study described that evaluates its standalone performance against specific metrics. The "clinical testing" summary is referenced but not provided.
7. The type of ground truth used:
- Not explicitly stated, but inferred to be a comparison against the original "steep, narrow, and unmachinable areas" and the requirement for a "machinable surface." For this type of device, ground truth would likely involve geometric accuracy, smoothness criteria, and mechanical machinability assessments. However, the document does not specify how this ground truth was established for study purposes.
8. The sample size for the training set:
- Not present. As this is from 2006 and the device is described as a "software translator" to smooth designs, it's less likely to be a deep learning model requiring a large training set in the modern sense. It's more likely rule-based or algorithmic. Regardless, no training set size is mentioned.
9. How the ground truth for the training set was established:
- Not present.
In summary, the provided document K061440 primarily focuses on the regulatory submission for substantial equivalence. It points to validation and clinical testing summaries (Sections 11 and 12) which are not included in the provided text. Therefore, the specific details regarding acceptance criteria and study methodologies, as requested, cannot be extracted from this document alone.
§ 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.