(146 days)
IDENTIFY is indicated for adult patients undergoing radiotherapy treatment simulation and/or delivery. IDENTIFY is indicated for positioning of patients, and for montoring patient motion including respiratory patterns. It allows for data output to radiotherapy devices to synchronize image acquisition or treatment delivery with the acquired motion information.
IDENTIFY is a system for motion monitoring during radiotherapy treatment simulation and delivery. It incorporates patient safety, quality, and workflow efficiency. Its high precision SGRT cameras support proper patient positioning and enable to monitor the patient's respiratory motion and to detect intra-fraction patient position changes during the treatment.
This FDA 510(k) summary for Varian Medical Systems Inc.'s IDENTIFY device mentions non-clinical testing for hardware and software verification and validation, electrical safety, and electromagnetic compatibility. However, it does not include a study specifically demonstrating device performance against pre-defined acceptance criteria for its intended use (patient positioning and motion monitoring, including respiratory patterns).
The document focuses on conformance to regulatory standards and a conclusion that the product conformed to defined user needs and intended uses, and is considered safe and effective. It explicitly states: "The Verification and Validation demonstrates that the device is as safe and effective as the predicate." This implies that the validation activities were largely focused on ensuring the new device performs at least as well as its predicate and meets safety standards, rather than proving performance against specific quantitative metrics for its core functionality in a study format.
Therefore, many of the requested sections regarding a specific performance study (e.g., sample size, ground truth establishment, MRMC study, standalone performance) cannot be answered from the provided text.
Here's a breakdown of what can and cannot be extracted from the provided text:
1. A table of acceptance criteria and the reported device performance
The document does not provide a table of acceptance criteria for device performance (e.g., accuracy of positioning, respiratory motion tracking accuracy, beam-hold trigger accuracy) nor does it report specific quantitative performance metrics for these functions. It states that "Test results showed conformance to applicable requirements specifications and assured hazard safeguards functioned properly," but does not detail what these requirements or results were in measurable terms related to the device's clinical indications.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Not provided in the document.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Not provided in the document.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not provided in the document.
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 provided in the document. The device is not described as an AI-assisted diagnostic tool that would typically involve human readers. Its primary function is motion monitoring and synchronization with treatment delivery.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
A standalone performance study against specific clinical metrics for motion tracking or positioning accuracy is not described in the provided text. The non-clinical testing mentions "hardware and software verification and validation testing," which would imply testing the algorithm, but the specifics of such testing in terms of clinical performance metrics are absent.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not provided in the document.
8. The sample size for the training set
Not applicable, as this document does not describe a machine learning model's training phase for a diagnostic or predictive task. The device is for motion monitoring and synchronization, not typically modeled through a "training set" in the sense of supervised learning for image interpretation.
9. How the ground truth for the training set was established
Not applicable, as a training set for machine learning is not discussed.
§ 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.