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The Topcon 3D OCT-1 Maestro is a non-contact, high resolution tomographic and biomicroscopic imaging device that incorporates a digital camera for photographing, displaying and storing the data of the retina and surrounding parts of the eye to be examined under Mydriatic and non-Mydriatic conditions.

The 3D OCT-1 Maestro is indicated for in vivo viewing, axial cross sectional, and three-dimensional imaging and measurement of posterior ocular structures, including retina, retinal nerve fiber layer, macula and optic disc as well as imaging of anterior ocular structures.

lt also includes a Reference Database for posterior ocular measurements which provide for the quantitative comparison of retinal nerve fiber layer, optic nerve head, and the macula in the human retina to a database of known normal subjects. The 3D OCT-1 Maestro is indicated for use as a diagnostic device to aid in the diagnosis, documentation and management of ocular health and diseases in the adult population.

The 3D OCT-1 Maestro with new line CCD is a non-contact, high-resolution, tomographic and biomicroscopic imaging system that combines optical coherence tomography (OCT) and fundus camera technology, along with various quantitative measurement and other data analysis functionalities. The device consists of the instrument body (main unit, chin-rest unit, and power supply base), software (to operate the instrument and to process the analysis functions), and various accessories. The software incorporates a number of safety features to detect errors during use and interrupt device functions as needed when an error is identified.

The only patient-contacting materials in the device - silicone rubber, Acrylonitrile-butadiene styrene resin (ABS), and polyamide resin (PA) – are classified per FDA's guidance on ISO 10993-1 as limited-duration contact with the patient or operator's intact skin. These are the same materials as were incorporated in the patient-contacting pieces of the predicate device.

The device is re-usable and is not supplied sterile; cleaning instructions are provided in the labeling and are essentially the same as those for the predicate device. The device is AC-powered.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Topcon 3D OCT-1 Maestro:

It's important to note that this document is a 510(k) summary for a submission seeking clearance for a modified version of an existing device (Topcon 3D OCT-1 Maestro, K170164) by establishing substantial equivalence to its predicate device (Topcon 3D OCT-1 Maestro, K161509). Therefore, the "study" described is primarily focused on demonstrating that the modifications did not alter the safety or effectiveness of the device compared to the predicate, rather than a de novo clinical trial to prove a new performance claim.

Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria in the traditional sense (e.g., "sensitivity must be >X%, specificity >Y%"). Instead, the acceptance criterion for this 510(k) submission is "functioning equivalently to the predicate 3D OCT-1 Maestro" and demonstrating that "the safety and effectiveness profile of the modified device is the same as that of its predicate."

The reported device performance, in this context, is that:

• "In all instances, the 3D OCT-1 Maestro with new line CCD functioned as intended and produced the expected results."
• "Performance data demonstrate that the modified device is as safe and effective as the predicate Maestro device."
• The modified device is "substantially equivalent."

While a table of acceptance criteria and reported "performance" in numerical terms (like sensitivity/specificity) is not provided in the document for the reasons explained above, we can frame it as:

Study Details

The document describes "bench testing" as the primary study performed to demonstrate substantial equivalence for the modifications.

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not applicable in the context of clinical image data. The "test set" here refers to the physical device components and software. No patient-specific test set data is described.
   • Data Provenance: Not applicable for a clinical test set. The testing was bench-based, involving the modified device hardware and software.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. The "ground truth" for this bench testing was the expected functionality and output of the device components based on engineering specifications and comparison to the predicate device. It did not involve expert-labeled clinical data.

3. Adjudication method for the test set: Not applicable. No clinical test set requiring adjudication was used.

4. 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 an imaging system (OCT and fundus camera), not an AI-driven diagnostic system providing interpretations or assisting human readers in a way that would lend itself to an MRMC study with AI assistance. The modifications were related to hardware components and software functionality.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No. This is an imaging device, not a standalone diagnostic algorithm in the AI sense. Performance assessment focused on the device's ability to capture images and perform its intended measurements as reliably as the predicate.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The "ground truth" for the bench testing was based on the device's engineering specifications, expected results from the predicate device, and compliance with recognized consensus standards (e.g., for safety, electromagnetic compatibility, software life cycle). It was not clinical ground truth like pathology or expert consensus on patient cases.

7. The sample size for the training set: Not applicable. This device is a medical imaging instrument; the modifications did not involve training an AI algorithm on a dataset.

8. How the ground truth for the training set was established: Not applicable, as there was no AI training set.

Summary of the Study's Nature:

The "study" described in this 510(k) summary is primarily a bench testing and verification/validation effort. The purpose was to demonstrate that modifications to the Topcon 3D OCT-1 Maestro (replacing a line CCD component and other minor updates) did not negatively impact its safety or effectiveness compared to the previously cleared predicate device. This is a common approach for 510(k) submissions where the changes are considered minor and do not alter the fundamental scientific technology or intended use. It is not a clinical study to establish new performance metrics or compare diagnostic accuracy against a clinical gold standard.

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The Topcon 3D OCT-1 Maestro is a non-contact, high resolution tomographic and biomicroscopic imaging device that incorporates a digital camera for photographing, displaying and storing the data of the retina and surrounding parts of the eye to be examined under Mydriatic and non-Mydriatic conditions.

The 3D OCT-1 Maestro is indicated for in vivo viewing, axial cross sectional, and three-dimensional imaging and measurement of posterior ocular structures, including retinal nerve fiber layer, macula and optic disc as well as imaging of anterior ocular structures.

It also includes a Reference Database for posterior ocular measurements which provide for the quantitative comparison of retinal nerve fiber layer, optic nerve head, and the human retina to a database of known normal subjects. The 3D OCT-1 Maestro is indicated for use as a diagnostic device to aid in the diagnosis, documentation and management of ocular health and diseases in the adult population.

The Maestro is a non-contact, high-resolution, tomographic and bio-microscopic imaging system that merges OCT and fundus cameras into a single device. The technological characteristics of the OCT employed are similar to those of already 510(k)-cleared OCT products, such as Topcon's 3D OCT-2000 (K092470), in that it employs conventional spectral domain OCT with widely-used 840 nm light source. The technological characteristics of the fundus camera employed are also similar to those of already cleared fundus cameras, such as Topcon's TRC NW300 (K123460), in terms of field of view (FOV) and camera sensor resolution.

The Maestro captures an OCT image and a color fundus image sequentially. It can take anterior OCT images in addition to fundus OCT images. It also includes a reference database for fundus OCT. Captured images are transferred from the device to an off-the-shelf personal computer (PC) via LAN cable, where the dedicated software for this device is installed. The transferred data is then automatically processed with analysis functions such as the automatic retinal layers segmentation, the automatic thickness calculation with several grids, the optic disc analysis and comparison with a reference database of eyes free of ocular pathology, and is finally automatically saved to the PC.

Two software programs for installation on an off-the-shelf PC are provided with the device. The first PC software program, called "FastMap", captures the images from the device, analyzes them and enables viewing of the data. The second PC software program, called "OCT Viewer", can only analyze and view the data.

Accessories include the following: power cord; chin-rest paper sheet; LAN cable; chin-rest paper pins; external fixation target; dust cover; spare parts case; and stylus pen. An optional Anterior Segment Kit allows the user to activate the anterior segment imaging functionality of the Maestro device.

The Topcon 3D OCT-1 Maestro is a non-contact, high-resolution tomographic and biomicroscopic imaging device. The provided text outlines its performance data, primarily focusing on repeatability and reproducibility measurements for various ocular structures in different patient populations.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state pre-defined acceptance criteria for the repeatability and reproducibility of the measurements. Instead, it presents the calculated repeatability and reproducibility measurements (SD, Limit, CV%) for the Maestro device across different parameters and patient groups. The "acceptance criteria" appear to be implied by the presentation of these results, suggesting that the device's performance, as measured, is considered acceptable for demonstrating substantial equivalence to predicate devices.

However, based on the provided tables, here's a summary of the reported device performance:

The "Limit" values in the tables are calculated as 2.8 x SD, representing a range within which 95% of repeated measurements are expected to fall. The "CV%" is the Coefficient of Variation, indicating precision relative to the mean.

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

• Test Set (Clinical Studies for Repeatability and Reproducibility):
  • Normal Subjects: 25 subjects for macula and optic disc measurements (full retinal thickness, ganglion cell + IPL, ganglion cell complex thickness, retinal nerve fiber layer, optic disc parameters). Explicitly stated in the tables (N=25).
  • Subjects with Retinal Disease: 26 subjects for macula and optic disc measurements (full retinal thickness, ganglion cell + IPL, ganglion cell complex thickness, retinal nerve fiber layer, optic disc parameters). Explicitly stated in the tables (N=26).
  • Subjects with Glaucoma: 25 subjects for macula and optic disc measurements (full retinal thickness, ganglion cell + IPL, ganglion cell complex thickness, retinal nerve fiber layer, optic disc parameters). Explicitly stated in the tables (N=25).
• Data Provenance: The document does not explicitly state the country of origin. The study is referred to as "clinical studies," but it's not specified if they were prospective or retrospective. Given that the manufacturer is Topcon Corporation of Japan, but the contact person is in the US, the studies could have been conducted in either or both regions.

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

The document does not specify the number or qualifications of experts used to establish a "ground truth" for the test set in the context of the repeatability and reproducibility studies. The clinical studies were conducted to determine the agreement, repeatability, and reproducibility of measurement data, not for diagnostic accuracy against a ground truth.

However, it mentions: "Consistent with the labeling for the test and control devices, the clinical site was permitted to make manual adjustments to automated segmentation based on the clinician's judgment." This indicates that clinicians (likely ophthalmologists or optometrists) were involved in reviewing and potentially adjusting automated segmentation, but it doesn't define them as "ground truth" experts in the context of defining disease states or specific measurements for the purpose of validating the AI's accuracy against a known truth.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method in the context of establishing ground truth for the test set. The clinical studies focused on repeatability and reproducibility of quantitative measurements, rather than classification or diagnosis that would typically require an adjudication process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study evaluating human reader improvement with or without AI assistance was not reported in this document. The clinical studies conducted were focused on the device's measurement precision (repeatability and reproducibility) and agreement with predicate devices rather than human-AI collaboration for diagnostic accuracy.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The clinical studies described primarily assessed the precision of the device's measurements (which involve algorithms for segmentation and thickness calculation) rather than the standalone diagnostic performance of an AI algorithm. The device performs automatic retinal layer segmentation, automatic thickness calculation, and optic disc analysis. The phrase "the clinical site was permitted to make manual adjustments to automated segmentation based on the clinician's judgment" (page 6) suggests that the device's algorithms operate with potential human oversight, implying it's not strictly a standalone AI performance evaluation for diagnostic purposes. The data presented are for the device's ability to consistently provide these measurements.

7. The Type of Ground Truth Used

For the repeatability and reproducibility studies, the "ground truth" is not a diagnostic label (e.g., pathology, outcomes data). Instead, the studies assess the consistency of the device's quantitative measurements of ocular structures (e.g., retinal thickness, RNFL thickness) by comparing multiple measurements taken under similar or varied conditions. The reference database uses "known normal subjects" but this is for comparative analysis against a normal population rather than for establishing a "ground truth" for disease diagnosis in the test set.

8. The Sample Size for the Training Set

The document specifies a "Reference Database" was compiled using "399 subject eyes from normal study subjects." This database is for "quantitative comparison... to a database of known normal subjects," which functions as a normative reference rather than a training set for an AI/algorithm in the conventional sense (e.g., for classification tasks).

If parts of the device's functionality (like automatic segmentation) involve machine learning, the training set size for those specific algorithms is not provided in this document. The 399 normal subjects form a reference database, not explicitly an algorithm training set.

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

For the "Reference Database" of 399 normal subjects:

• How it was established: The study collected measurements of various ocular structures from these normal eyes. The "normal" status of these subjects would have been established through clinical evaluation to ensure they were free of ocular pathology.
• Type of Ground Truth: The ground truth for this reference database is the consensus clinical determination that the subjects have "normal eyes" and the quantitative measurements derived from these normal eyes form the expected range for a healthy population. The document states it provides "quantitative comparison... to a database of known normal subjects." It also mentions "a reference database of eyes free of ocular pathology."

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