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510(k) Data Aggregation
(100 days)
INTEGRITY(TM)
Integrity™ is the interface and control software for the Elekta range of medical digital linear accelerators and is intended to assist a licensed practitioner in the delivery of radiation to defined target volumes (e.g. lesions, arterio-venous malformations, malignant and benign tumours), whilst sparing surrounding normal tissue and critical organs from excess radiation. It is intended to be used for single or multiple fractions, delivered as static and/or dynamic beams of radiation, in all areas of the body where such treatment is indicated
This Traditional 510(k) describes enhancements to the integral software performing the interface and machine control functions for the Elekta Limited range of medical digital linear accelerators. These modifications provide improvements to beam delivery, compatibility with a wider range of Elekta products and the provision of diagnostic tools to assist service engineers
The provided 510(k) summary for Integrity™ R1.1 discusses a software enhancement to an existing medical linear accelerator control system. This submission does not describe a study in the traditional sense with acceptance criteria, sample sizes, expert ground truth, or an MRMC study related to diagnostic accuracy. Instead, it focuses on verifying the software's functionality and safety and establishing substantial equivalence to a predicate device.
The "acceptance criteria" here are essentially the successful completion of various engineering and validation tests, ensuring the new software features and changes do not negatively impact safety or effectiveness.
Here's a breakdown of the requested information based on the provided text, recognizing that many categories are not applicable to this type of submission which focuses on software changes to a control system, rather than a diagnostic AI device.
1. Table of Acceptance Criteria and Reported Device Performance
Given the nature of this 510(k) (software update for a control system), the "acceptance criteria" are implied to be the successful execution and verification of functional and safety requirements, and demonstrating substantial equivalence to the predicate device. The "reported device performance" refers to the system meeting these established criteria.
| Acceptance Criteria Category | Specific Criteria (Implied) | Reported Device Performance |
|---|---|---|
| Functional Verification | All new and existing features perform as specified in the requirement specification. | "Testing in the form of module, integration and system level verification was performed to evaluate the performance and functionality of the new and existing features against the requirement specification." "Regression testing has been performed successfully to verify the integrity of any changes." |
| Safety and Effectiveness | The device maintains safety and effectiveness equivalent to the predicate device and complies with relevant safety standards. | "Results from verification and validation testing demonstrate that conformance to applicable technical design specification and assured safety & effectiveness have been met." "The system is subject to compliance testing to voluntary consensus safety standards. Details of the standards employed in the design are specified in the Standard Data Report in section 9 which includes but not limited to IEC 60601-1, IEC 60601-2-1, IEC 60601-1-4, IEC 62304, IEC 60601-1-6, IEC 62366 and ISO 14971." |
| Substantial Equivalence | Demonstrated equivalence in intended use, principles of operation, technological characteristics, and labeling to the predicate device (Integrity™ R1.0, K102200). | "The functionality for Integrity™ is substantially equivalent to its predicate device, Integrity™ R1.0 (K102200) in safety and effectiveness. The intended use, principles of operation, technological characteristics and labelling are the same or equivalent to the predicate device." |
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify a "test set" in terms of patient data or clinical images, as this is a software update for a control system. Testing was performed on "production equivalent systems at Elekta and at customer sites." There is no mention of data provenance (e.g., country of origin, retrospective/prospective) because the testing is related to the functionality of the control system software, not analysis of patient data.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
N/A. This is a software control system update, not a diagnostic device requiring expert interpretation for ground truth establishment.
4. Adjudication Method for the Test Set
N/A. Not applicable for this type of submission.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No. An MRMC study is not relevant for this type of device (a linear accelerator control system software update). The device's primary function is to control radiation delivery, not to interpret diagnostic images.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
The device itself is a standalone algorithm (a control system software). Its performance was evaluated through verification and validation testing, as described in the "Summary of non clinical performance testing." This is effectively "standalone" performance testing for the software's functionality.
7. The Type of Ground Truth Used
N/A. For a control system, "ground truth" is defined by the technical specifications and desired operational parameters. Successful operation according to these specifications (e.g., accurate beam delivery, proper system responses) constitutes meeting the "ground truth." There is no patient-level ground truth (like pathology or outcomes data) involved for this type of submission.
8. The Sample Size for the Training Set
N/A. This 510(k) is for a software update to a control system. It does not involve machine learning or AI in the sense of being "trained" on a dataset like an image analysis algorithm would be.
9. How the Ground Truth for the Training Set Was Established
N/A. As there is no "training set" in the context of an AI/ML algorithm that learns from data, this question is not applicable.
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