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The Spectralis HRA+OCT is a non-contact ophthalmic diagnostic imaging device. It is intended for viewing the posterior segment of the eye, including two- and three-dimensional imaging, cross-sectional imaging, fundus photography, and fluorescence imaging (fluorescein, indocyanine green and autofluorescence), and to perform measurements of ocular anatomy and ocular lesions. The device is indicated as an aid in the detection and management of various ocular diseases including: age-related macular degeneration, macular edema, diabetic retinopathy, retinal and choroidal vascular diseases, glaucoma, and for viewing geographic atrophy as well as changes in the eye that result from neurodegenerative diseases. The Spectralis HRA+OCT includes a retinal nerve fiber layer thickness normative database, which is used to quantitatively compare the retinal nerve fiber layer in the human retina to a database of Caucasian normal subjects; the classification result is valid only for Caucasian subjects.

The Spectralis HRA+OCT is a real-time imaging system of the posterior segment of the human eye and for aiding in the assessment and management of various diseases of the posteriorsegment, such as age-related macular degeneration, diabetic retinopathy, and glaucoma. The device is a combination of optical coherence tomography (OCT) with confocal scanning laser ophthalmoscopy (cSLO). OCT imaging includes high-resolution cross-sectional imaging of ocular structures (e.g., retina, macula, optic nerve head); cSLO imaging includes high-resolution and dynamic infrared reflectance, blue reflectance, fluorescein angiography, indocvanine green angiography, and autofluorescence imaging. OCT images and cSLO images are acquired simultaneously and are viewed side-by-side on the computer screen. Images are acquired and stored using Spectralis operation software, which runs on a standard personal computer. Spectralis components include a laser scanning camera mount with headrest, operation panel, power supply box, operation software, and host computer. A MultiColor option has been added to provide additional green reflectance imaging and a "composite color" image, which provides a different view of the features of the eye. This composite color image is not the same as fundus color photo.

The provided 510(k) summary for the Spectralis HRA+OCT device primarily focuses on demonstrating substantial equivalence to a predicate device and safety/performance through bench testing and a normative database study. It does not contain a typical acceptance criteria table with corresponding reported performance for a specific algorithm or AI model, nor does it describe a study specifically designed to prove a device meets such criteria in the context of AI performance.

Instead, the clinical evaluation section describes studies related to the device's measurement accuracy, repeatability, reproducibility, and the establishment of a normative database for Retinal Nerve Fiber Layer (RNFL) thickness, which is then used for classification.

Here's an attempt to extract and present the information based on the provided text, acknowledging the limitations in scope for AI-specific acceptance criteria and studies:

1. Table of "Acceptance Criteria" and Reported Device Performance

As this is a 510(k) for an imaging device with a normative database feature, the "acceptance criteria" are not framed in terms of AI performance metrics (like sensitivity, specificity, AUC). Instead, the performance is demonstrated through the characteristics of the normative database and the agreement with a predicate device.

• Age adjustment: Linear regression of RNFL thickness vs. age was performed for various sectors; negative slopes showed decrease with age and were adjusted; insignificant positive slopes were not adjusted. |
  | Agreement with Predicate Device (Stratus) | A good linear correlation between RNFL thickness measurements with Spectralis and Stratus devices was found for healthy and glaucoma subjects across all measurement regions. Slopes and intercepts of regression lines were in the neighborhood of 1 and 0, respectively, though with some variation (indicating they should not be used interchangeably, which is noted as in agreement with published literature). |
  | Performance in Disease (Qualitative) | Case Reports and Case Series of eyes with various pathologies showed no artifacts, no unexpected RNFL thickness measurement results, and no unexpected classification results. RNFL thickness was found to be predictably decreased in glaucoma subjects. (This is a qualitative statement of expected behavior rather than a strict quantitative criterion). |
  | Safety and Electrical Compliance | Tested according to IEC 60601-1, IEC 60601-1-2, IEC 60601-1-4, and IEC 60825-1, meeting all requirements. Classified as a Class 1 laser product per 21 CFR §1040.10. |

2. Sample size used for the test set and the data provenance

• Reproducibility and Repeatability: "human volunteers" - specific number not provided.
• Normative Database (Acts as a reference/test for classification): 201 subjects of Caucasian origin.
  • Provenance: Enrolled in a patient registry (implied prospective data collection for the purpose of the database). All subjects were described as "normal" based on specific criteria.
  • Limitations noted: Sample size (201), particularly small for extreme age groups (70 years), Caucasian ethnicity only, and inclusion of refractive errors from +5 to -7 diopters.
• Agreement Study with Predicate:
  • Healthy subjects: n=101
  • Glaucoma patients: n=183
  • Provenance: Not explicitly stated as retrospective or prospective, but implies direct examination for the study.

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

• Normative Database: "Screening for entry into the study included patient history and physical examination to determine if eyes were 'normal' by two ophthalmologists." No specific years of experience are provided, but "ophthalmologist" implies a qualified medical doctor specializing in ophthalmology.
• Agreement Study with Predicate: No explicit mention of experts establishing ground truth; the study compared measurements between two devices. The classification of "glaucoma patients" would imply diagnosis by medical professionals.

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

• Normative Database: For determining "normality," two ophthalmologists made the determination. It's unclear if there was an adjudication process if their opinions differed ("2+0" if they agreed, no explicit mention of dispute resolution).
• Other studies: Adjudication methods are not mentioned.

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 MRMC study of human readers with vs. without AI assistance was reported. This device predates the widespread use of AI in medical imaging interpretation as we know it today. The "normative database" itself is a classification tool provided by the device, not an AI interpreting images for a human. It provides a color-coded classification based on measured RNFL thickness compared to the database.

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

• Yes, in spirit, the normative database is a standalone "algorithm" for classification. The device automatically measures RNFL thickness and then classifies it against the stored normative database (e.g., as within or outside the 1st or 5th percentile). The performance of this classification is implicitly demonstrated by the database's construction, reproducibility of measurements, and the statement that RNFL thickness was "predictably decreased in glaucoma subjects." However, it is not presented with traditional standalone diagnostic performance metrics (e.g., sensitivity, specificity, AUC).

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

• Normative Database: Ground truth for "normality" was established by expert consensus/clinical examination by two ophthalmologists.
• Agreement Study with Predicate: The "ground truth" for comparison was the measurements from an established predicate device (Stratus OCT). For classifying subjects as "healthy" or "glaucoma," the ground truth would have been clinical diagnosis.

8. The sample size for the training set

• The document describes the normative database as being built from 201 subjects. This serves as the reference data against which subsequent patient measurements are compared for classification. In a machine learning context, this database acts effectively as the "training" or "reference" set for the classification rules (i.e., percentiles for normality).

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

• For the normative database (which serves as the "training set" for the classification logic), the ground truth for "normality" was established by two ophthalmologists. Subjects were included if they had "no history of glaucoma, normal intraocular pressure, normal visual field, normal appearance of optic disc, etc." based on patient history and physical examination.

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The OIS EyeScan Portable Modular Imaging System is a portable monocular camera intended for imaging of both the posterior segment (including structures of the retina, vitreous and choroid) and anterior segment (including structures of the orbit, lids, cornea, iris and lens) of the eye. The device is suitable for documentation of findings in a clinical setting.

OIS EyeScan is a portable, modular imaging device, which is designed to perform retinal imaging (including color, FA, FAF, Red-free) and corneal imaging (including tear film analysis, corneal fluorescences, slit). OIS EyeScan Portable Modular Imaging System, consistent with the predicate imaging devices previously listed, uses light photography to obtain clinical information. OIS EyeScan captures images using light sources (LEDs of different colors), functionally optimized lenses and filters, and digital camera sensors. OIS EyeScan uses OIS WinStation software for image capture, review and analysis. The device comprises a base unit, and interchangeable imaging modules and optional chin rest. Images may be stored on industry standard storage media.

The provided text describes the OIS EyeScan Portable Modular Imaging System, a portable monocular camera intended for imaging both the posterior and anterior segments of the eye. It is suitable for documenting findings in a clinical setting.

However, the document is a 510(k) summary, which focuses on demonstrating substantial equivalence to pre-existing predicate devices rather than proving a new device's efficacy through specific clinical performance studies with acceptance criteria, sample sizes, and ground truth methodologies.

Therefore, the requested information cannot be fully extracted as such studies are not present in this 510(k) submission.

Here's an breakdown of what can be extracted and what cannot:

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

• Acceptance Criteria: Not explicitly stated in terms of quantitative performance metrics for disease detection or diagnostic accuracy. The acceptance criteria for this 510(k) submission revolve around demonstrating substantial equivalence to predicate devices. This means the device should have similar technological characteristics, intended use, and be as safe and effective as existing legally marketed devices.
• Reported Device Performance: The document only states that the device was subjected to "extensive performance testing and validation" and that "EMC and safety tests currently underway will ensure the device complies with industry and safety standards." No specific performance metrics (e.g., sensitivity, specificity, accuracy) are reported for clinical tasks.

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

• Not applicable. This submission focuses on demonstrating substantial equivalence, not on a clinical performance study with a test set of patient data to assess diagnostic accuracy. Therefore, sample sizes and data provenance for such a test set are not mentioned.

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. As there is no clinical performance study with a "test set" of patient data to establish ground truth on, this information is not provided.

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

• Not applicable. No clinical performance study 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 document describes a new imaging system, not an AI-powered diagnostic aide designed to assist human readers. Therefore, an MRMC study comparing human readers with and without AI assistance was not performed or reported.

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

• Not applicable. This device is an imaging system, not a standalone diagnostic algorithm.

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

• Not applicable. As a clinical performance study for diagnostic accuracy is not outlined, the concept of "ground truth" for disease states is not relevant in this 510(k) summary. The "ground truth" for this submission would be adherence to functional specifications, safety standards, and equivalence to predicate devices.

8. The sample size for the training set:

• Not applicable. This device is an imaging system, not a machine learning algorithm that requires a training set of data.

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

• Not applicable. As there is no training set mentioned, this information is irrelevant.

Summary of Device Evaluation in the 510(k) Submission:

The OIS EyeScan Portable Modular Imaging System demonstrated substantial equivalence by:

• Comparative Analysis to Predicate Devices: The submission extensively compares the OIS EyeScan to several predicate devices (Topcon TRC-50EX and TRC-50IX, WinStation Digital Imaging System, IRI Integrated Retinal Imager, BX900, Tearscope Plus).
• Similarities Highlighted: The document emphasizes that the OIS EyeScan employs similar principles (light photography), light sources (LEDs instead of halogen/xenon lamps), optical specifications, image processing, and storage capabilities as the predicate devices.
• Performance Testing: While specific results are not provided, the document states the device underwent "extensive performance testing and validation" and "software validation tests" to ensure it met "all its functional specifications." This functional specification fulfillment is the closest equivalent to "acceptance criteria" in this context.
• Safety and EMC Compliance: The submission notes that EMC and safety tests were underway or completed to ensure compliance with industry and safety standards, another critical aspect of demonstrating equivalence.

Conclusion Drawn:

The conclusion drawn from the performance testing (functional and safety) and the comparison to predicate devices is that the OIS EyeScan Portable Modular Imaging System is substantially equivalent in safety and effectiveness to the listed predicate devices. This substantial equivalence is the primary "proof" provided in a 510(k) process for devices of this nature, rather than a clinical trial demonstrating specific diagnostic performance metrics against a defined ground truth.

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