(214 days)
The Bausch + Lomb Stellaris Elite Vision Enhancement System is intended for the emulsification and removal of cataracts, anterior and posterior segment vitrectomy. The system is designed for use in both anterior and posterior segment surgeries. It provides capabilities for phacoemulsification, coaxial and bimanual irrigation/aspiration, bipolar coagulation, vitrectomy, viscous fluid injection/removal and air/fluid exchange operations. The Bausch + Lomb Stellaris Elite Vision Enhancement System configured with the laser module is additionally intended for retinal photocoagulation and laser trabeculoplasty.
The Bausch + Lomb Stellaris Elite Vision Enhancement System is comprised of an integrated ophthalmic microsurgical system designed for use in anterior and posterior segment surgery including phacofragmentation and vitreous aspirating and cutting as well as endoillumination. Additionally, the Stellaris Elite Vision Enhancement System may be configured with a 532 nm laser module for photocoagulation. The system is based on the technology and the performance of the existing Stellaris and Stellaris PC Vision Enhancement Systems. This traditional 510(k) incorporates software and hardware revisions to support the introduction of new features including Adaptive Fluidics, high speed vitrectomy, and supporting accessories. The Stellaris Elite Vision Enhancement System is available in various configurations.
This document is a 510(k) premarket notification for the "Stellaris Elite Vision Enhancement System" and primarily focuses on demonstrating substantial equivalence to predicate devices, rather than providing a detailed study proving performance against specific acceptance criteria in the manner one might expect for an AI/ML-based device.
Therefore, many of the requested sections (including specific acceptance criteria, sample sizes for test sets, expert consensus details, MRMC studies, standalone performance, and details on training sets) are not provided in this document. The document describes a traditional medical device (a surgical system that enhances vision), not an AI/ML product.
Here's a breakdown based on the information provided in the document:
1. Table of acceptance criteria and the reported device performance:
The document does not present a table of specific quantitative acceptance criteria (e.g., accuracy, sensitivity, specificity thresholds) for the device's clinical performance. Instead, it demonstrates compliance with recognized electrical and medical device safety standards and verifies functional performance to support substantial equivalence.
| Acceptance Criteria Category (Implied) | Reported Device Performance |
|---|---|
| Electrical Safety Standards | Complies with: |
- IEC 60601-1:2005 + C1(2006) + C2(2007) + AM1(2012) or IEC 60601-1:2012 (General requirements for basic safety and essential performance)
- IEC 60601-1-2 ed3.0 (2007) (Electromagnetic compatibility)
- IEC 60601-1-6:2010 (Usability)
- IEC 60601-2-2:2009 (High frequency surgical equipment) |
| Laser Safety Standards | Complies with: - IEC 60601-2-22:2007 (Diagnostic and therapeutic laser equipment) |
| Device-Specific Standards | Complies with: - IEC 80601-2-58:2008 (Lens removal and vitrectomy devices) |
| Functional Performance | Successful test results for functional, simulated use, biocompatibility, shelf life, and transport testing. |
| Software Quality | Software changes verified and validated in accordance with Bausch + Lomb software quality procedures, complying with EN ISO IEC 62304:2006 (Medical device software life cycle processes). |
| Clinical Equivalence | Demonstrated substantial equivalence in indications for use, design features, and functional features to predicate devices (K133486 Stellaris PC Vision Enhancement System, K133242 Stellaris Vision Enhancement System). |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
The document does not provide details on specific sample sizes for test sets in a clinical performance study involving patient data. The "test sets" mentioned would refer to units of the device or its components for engineering and performance validation rather than patient data for AI model evaluation.
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 applicable. There is no mention of expert-established ground truth for a test set of medical cases. The assessment is based on compliance with standards and functional testing by engineers/testers.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
Not applicable. No clinical test set requiring adjudication is described.
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 applicable. This is not an AI-assisted diagnostic or therapeutic device that would typically undergo an MRMC study.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Not applicable. The device is a surgical system operated by a human surgeon. It is not an algorithm performing a task autonomously.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
For this type of device (a surgical system), the "ground truth" for its performance is typically defined by:
- Compliance with validated international and national standards (e.g., IEC standards for electrical safety, electromagnetic compatibility, usability, and device-specific requirements).
- Successful completion of functional and simulated use testing to ensure the device operates as intended (e.g., proper phacoemulsification, vitrectomy, fluidics).
- Biocompatibility testing of materials.
- Shelf-life and transport testing to ensure product integrity.
8. The sample size for the training set:
Not applicable. This device is not an AI/ML model that undergoes "training" on a dataset. The software development follows traditional software life cycle processes (EN ISO IEC 62304:2006).
9. How the ground truth for the training set was established:
Not applicable, as there is no "training set" in the context of an AI/ML model. Software validation involves verification against requirements and pre-defined test cases, not ground truth labeling for machine learning.
§ 886.4670 Phacofragmentation system.
(a)
Identification. A phacofragmentation system is an AC-powered device with a fragmenting needle intended for use in cataract surgery to disrupt a cataract with ultrasound and extract the cataract.(b)
Classification. Class II.