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Pattern Scanning Laser Trabeculoplasty (PSLT) software is intended for use with the PASCAL Streamline for procedures in trabeculoplasty in open angle glaucoma.

Pattern Scanning Laser Trabeculoplasty (PSLT) is a software option for the currently marketed PASCAL Streamline (K100019 and K111108). It represents an enhancement intended to simplify the laser trabeculoplasty procedure by applying a fixed pattern of locations to the trabecular meshwork instead of a single spot. Additionally, this modification offers greater multi-functionality for the management of glaucoma. PSLT provides rapid, defined, uniform and minimally traumatic (sub-visible) computer-guided treatment with exact abutment of the patterns readily align to the trabecular meshwork, allowing more rapid and easier applications. Once the software is enabled, the PSLT feature is always available.

Here's a breakdown of the acceptance criteria and study information for the PSLT for PASCAL Streamline device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document generally states that the device met the clinical endpoints and had similar safety and efficacy profiles to the comparator, but it does not provide specific numerical values for the achieved IOP reduction or success rate for the PSLT device itself. It only mentions the target difference for the sample size calculation (an inter-group IOP difference of 3 mmHg).

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size (Test Set): 58 eyes.
• Data Provenance: The study was a "prospective, randomized, controlled study" of patients with open-angle glaucoma. The text does not explicitly state the country of origin, but it implies a single investigational site ("standard criteria of the investigational site"). The study design describes patients requiring bilateral laser trabeculoplasty, suggesting each patient received both PSLT and a comparator treatment (likely standard SLT, though not explicitly named as "SLT" in the study design section, it's mentioned in inclusion/exclusion for prior procedures).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

The document does not provide details on the number of experts or their qualifications specifically used to establish ground truth for the test set beyond the general term "observational site" and patients' diagnosis of open angle glaucoma. Clinical endpoints were measured by tonometry and CLS derived parameters. It is implicitly assumed that ophthalmologists or trained clinical staff performed these measurements.

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or described. The clinical study was a patient-level comparative effectiveness study comparing two laser modalities (PSLT and an implied standard SLT) on clinical outcomes (IOP reduction and success rates), not a diagnostic study evaluating reader performance with or without AI assistance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

No, the PSLT software is an enhancement to the PASCAL Streamline laser system, simplifying the laser application procedure. It assists the clinician in applying the laser, rather than performing an autonomous diagnostic or treatment decision. Therefore, a standalone performance study in the sense of an "algorithm-only" diagnostic AI was not applicable or performed.

7. Type of Ground Truth Used

The ground truth for the clinical study was based on clinical outcomes data measured directly from patients:

• Change in Intraocular Pressure (IOP) from baseline, measured by tonometry.
• "Success" at 1-month post-surgery, measured by tonometry and CLS derived parameters.

8. Sample Size for the Training Set

The provided text does not mention a training set because the PSLT is a software option for a laser surgical instrument, simplifying treatment delivery. It's not a machine learning model that requires a distinct training and test set in the traditional sense of AI diagnostics. The software capabilities were validated through a clinical performance study.

9. How the Ground Truth for the Training Set Was Established

As no training set is mentioned or implied for an AI/ML model, the establishment of ground truth for a training set is not applicable here. The software's functionality was validated through engineering verification and validation, as well as the clinical study described.

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