Search Results

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

Pattern Scanning Laser Trabeculoplasty (PSLT) is a software option for the currently marketed PASCAL Streamline (K171488). 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. 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.

The manufacturer, Topcon Medical Laser Systems, Inc., has leveraged a previous clinical study (K171488) conducted on the predicate device, PSLT for PASCAL Streamline, to support the substantial equivalence of the new devices, PSLT with PASCAL Synthesis and PSLT with PASCAL Synthesis TwinStar. This submission does not present a new study with specific acceptance criteria for a new AI/software algorithm performance assessment, but rather asserts that the performance of the new devices is similar to the predicate based on hardware similarities and shared software.

Therefore, many of the requested details about acceptance criteria and a new study proving the device meets those criteria are not explicitly provided or applicable in the context of this 510(k) summary, as it relies on equivalence to a previously cleared device. The summary emphasizes that the PSLT software feature's specifications and performance remain unchanged, and the laser modalities have similar safety and efficacy profiles.

However, I can extract information related to the previous clinical study for the predicate device (K171488), which serves as the basis for the current device's claimed safety and effectiveness.

Here's a breakdown of the requested information based on the provided text, indicating where information is not present or not directly applicable to a new performance study for the current submission:

1. A table of acceptance criteria and the reported device performance

• Acceptance Criteria: The K171488 study defined clinical endpoints as its primary and secondary measures of success.
  • Primary Endpoint: Change in Intraocular Pressure (IOP) from baseline to 1-month post-surgery, as measured by tonometry.
  • Secondary Endpoint: Success at 1-month post-surgery as measured by tonometry and CLS (likely "clinical") derived parameters.
• Reported Device Performance:
  • The document states: "Results of the clinical investigation support the indications for use of the PSLT with PASCAL Streamline for procedures in trabeculoplasty in patients with open angle glaucoma by achieving clinical endpoints. Therefore, the clinical study confirms that the device is safe and effective as used according to the instructions for use."
  • Specific numerical results or thresholds for "achieving clinical endpoints" are NOT provided in this summary. For example, it doesn't state "IOP reduction of X mmHg was considered successful" or list the average IOP reduction observed.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Sample Size: A sample size of 58 eyes was used.
• Data Provenance: The study was a "prospective, randomized, controlled study." The country of origin of the data is not specified in the provided text.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• This is not applicable to this study. The ground truth for this clinical study was based on physiological measurements (IOP, clinical parameters) and diagnosis of open angle glaucoma, not expert interpretation of images or other data requiring multiple readers for adjudication.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

• This is not applicable as the ground truth was based on objective physiological measurements and predefined diagnostic criteria, not subjective interpretations requiring adjudication.

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

• No, an MRMC study was NOT done. This was a clinical effectiveness study assessing a laser treatment device, not an AI assistance tool for human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• This is not applicable. The PSLT is a software option that guides a laser treatment. Its "performance" is in guiding the laser to achieve a clinical outcome in a patient, not in making a diagnostic prediction independently. The clinical study evaluated the device (laser with PSLT software) as a whole, not just the software in isolation.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• The ground truth was based on clinical outcomes data (change in Intraocular Pressure (IOP) and success measured by tonometry and CLS derived parameters) in patients with a diagnosis of open angle glaucoma.

8. The sample size for the training set

• This information is not provided and is likely not applicable in the context of this 510(k) submission which relies on clinical equivalence rather than a new machine learning algorithm that requires a distinct training and test set. The PSLT software applies "a fixed pattern of locations" - suggesting a rule-based or predefined algorithm rather than one that "learns" from a training set in the typical machine learning sense.

9. How the ground truth for the training set was established

• This is not applicable for the reasons stated in point 8.

Ask a specific question about this device

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.

Ask a specific question about this device

PASCAL® Synthesis™ TwinStar™ Ophthalmic Scanning Laser System is intended for use to perform single-spot photocoagulation in the posterior segment (retina, choroid) and in the anterior segment (iris, trabecular meshwork) as well as pattern-scanning photocoagulation in the non-macular retina of the eye. Single-spot delivery may be performed using a slit lamp biomicroscope or an indirect ophthalmoscope. Pattern delivery may be performed using a slit lamp biomicroscope.

PASCAL® Synthesis™ TwinStar™ is an ophthalmic scanning laser system. The system can perform single shot photocoagulation as is performed conventionally. At the physician's discretion, the system provides for a single laser spot treatment or the option of using a selectable pattern of delivering multiple laser spots simultaneously. Pattern scanning allows the physician to enhanced efficiency and reduced patient discomfort.

The PASCAL Synthesis™ Laser System (parent product to the PASCAL Synthesis Twinstar™) included 532 nm/577 nm wavelengths and was cleared via K123542. The subject device for this submission is a line extension named PASCAL Synthesis Twinstar and will include 577 nm/638 nm laser wavelengths.

The provided document is a 510(k) summary for the PASCAL® Synthesis™ TwinStar™ Ophthalmic Scanning Laser System. It focuses on demonstrating substantial equivalence to predicate devices, rather than establishing acceptance criteria and proving performance against them in the way an AI/ML device submission would.

Therefore, many of the requested points, especially those related to acceptance criteria, specific performance metrics (like sensitivity, specificity, F1 score), sample sizes for test/training sets with ground truth, expert adjudication, or MRMC studies for AI devices, are not present in this document.

This device is a physical laser system, not an AI/ML diagnostic tool. The "performance" discussed relates to its physical characteristics and biological effects on animal tissue, not its ability to interpret medical data or assist human readers.

However, I can extract information relevant to the study that was done to support the device and map it to the closest possible aspects of your request.

1. A table of acceptance criteria and the reported device performance

No explicit acceptance criteria in the form of numerical performance targets (e.g., sensitivity > X%) are stated. The study's conclusion of "similarity" is the implicit acceptance.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Animal Study (Test Set): Not explicitly stated as a number of animals or eyes, but the study refers to "rabbit retinae" and "single-eye comparative cases." The common practice for such studies usually involves a small number of animals (e.g., 3-5 rabbits).
• Data Provenance: The study was "in vivo" (live animal) and "prior to euthanasia," implying a prospective experimental design. Country of origin is not specified, but the sponsor is based in Livermore, CA, USA.

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)

• The ground truth (lesion morphology and size) was established by "ophthalmoscopic evaluation, fundus photos, and SD-OCT measurements."
• The number and qualifications of experts interpreting these evaluations are not specified in the document. It is implied these were performed by qualified personnel involved in the animal study.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• No explicit adjudication method is described. The assessment likely involved expert interpretation of the ophthalmoscopic, fundus photo, and SD-OCT data, but details on how consensus or adjudication was achieved are not provided.

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

• No, an MRMC comparative effectiveness study was not done. This device is a laser surgical instrument, not an AI diagnostic tool that assists human readers.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

• Not applicable. This is a laser device, not an algorithm. The "standalone" performance here refers to the device's ability to create comparable lesions to the predicate, irrespective of human interpretation beyond setting parameters and performing the procedure.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• The ground truth was based on in-vivo assessments using:
  • Ophthalmoscopic evaluation
  • Fundus photos
  • SD-OCT measurements
• It primarily focused on lesion morphology and size immediately post-treatment in animal models.

8. The sample size for the training set

• Not applicable. This device is a laser system and does not involve AI/ML requiring a training set. The "study" described is an animal performance test, not an AI model training or validation.

9. How the ground truth for the training set was established

• Not applicable, as there is no training set for an AI/ML model for this device.

Ask a specific question about this device

The PASCAL® Laser Indirect Ophthalmoscope is intended for use with the PASCAL Streamline 577 laser or the PASCAL Streamline 532 laser when either of those lasers is used in the treatment of ocular pathology in the posterior segment; retinal photocoagulation, panretinal photocoagulation, focal photocoagulation and grid photocoagulation for vascular and structural abnormalities of the retina and choroid including:

532nm

• . macular edema
• age-related macular degeneration .
• . lattice degeneration
• . retinal tears and detachments

577nm

• proliferative and nonproliferative diabetic retinopathy .
• macular edema .
• choroidal neovascularization .
• . branch and central retinal vein occlusion
• . age-related macular degeneration
• . lattice degeneration
• retinal tears and detachments .
• retinopathy of prematurity .

Intended for use in the treatment of ocular pathology in the anterior segment including:

577nm

• iridotomv .
• trabeculoplasty .

The PASCAL Laser Indirect Ophthalmoscope (LIO) with eye safety filter is a non-sterile, multiple use, delivery device that is worn on the physician's head and is used in conjunction with a compatible hand held ophthalmoscopic examination lens to view and treat the patient's retina. LIOs are used to treat patients in a supine position or who could not otherwise be treated using a slit lamp delivery system.

This document is a 510(k) Pre-market Notification for a medical device called the PASCAL® Laser Indirect Ophthalmoscope. It primarily focuses on demonstrating substantial equivalence to predicate devices rather than proving performance against specific acceptance criteria through clinical studies in the typical sense for AI/software devices.

Therefore, many of the requested sections regarding acceptance criteria and performance against those criteria, especially those pertaining to AI/software performance assessment like MRMC studies, standalone performance, and ground truth establishment, are not applicable to this type of submission.

Here's a breakdown based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The concept of "acceptance criteria" and "reported device performance" in this 510(k) is framed around substantial equivalence to predicate devices rather than quantitative performance metrics for a novel functionality. The primary acceptance criteria for a 510(k) submission are that the new device has the same intended use and technological characteristics as a legally marketed device (predicate device) and does not raise new questions of safety and effectiveness.

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

Not applicable. This is a hardware device submission demonstrating substantial equivalence, not a performance study on a test set of data. There is no "test set" of patient data in the context of evaluating an algorithm.

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

Not applicable. There is no test set or ground truth established by experts for algorithmic performance in this submission.

4. Adjudication Method for the Test Set

Not applicable. There is no test set or adjudication method described for an algorithm.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study was not done. This submission is for a medical device (a laser indirect ophthalmoscope), not an AI/software algorithm intended to aid human readers in diagnostic tasks.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done

No, a standalone algorithm performance study was not done. This device is a delivery system for laser treatment, used by a physician (human-in-the-loop is inherent in its intended use), and does not involve AI or algorithms running independently.

7. The Type of Ground Truth Used

Not applicable. "Ground truth" in the context of expert consensus, pathology, or outcomes data is relevant for evaluating diagnostic or predictive algorithms. This submission is for a laser delivery device, where the "ground truth" relates to its physical and functional specifications meeting pre-defined engineering and safety standards, and its equivalence to devices already on the market.

8. The Sample Size for the Training Set

Not applicable. There is no "training set" in the context of an algorithm for this device.

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

Not applicable. There is no training set or ground truth for algorithmic development in this submission.

Ask a specific question about this device