Search Results

The Topcon 3D OCT-1000 is a non-contact ophthalmic imaging system for the viewing and axial cross sectional imaging of posterior ocular structures. It is used for in vivo imaging of the retina, retinal nerve fiber laver and optic disc. It is intended for use as a diagnostic device to aid in the detection and management of ocular diseases, including but not limited to macular edema and central serous retinopathy. The device is indicated for assessing the area, location, and measurement of retinal thickness, including in patients with retinal pathologies. In addition, the device is indicated to detect separation between identified retinal layers and surfaces, i.e., retinal tissue layers.

The Topcon 3D OCT-1000 is a non-contact, high resolution tomographic and biomicroscopic imaging device. It is indicated for in vivo viewing, axial, cross-sectional and three-dimensional imaging and measurements of posterior ocular structures, including retina, retinal nerve fiber layer, macula, and optic disc. It is intended for use as a diagnostic device to aid in the detection and management of ocular diseases affecting the posterior segment of the eye.

The Topcon 3D OCT-1000 for Measurement of Retinal Thickness uses optical coherence tomography, which relies upon interferometry of superluminescent diode light reflected from the fundus of the eye to obtain cross-sectional images of the retina. The Topcon 3D OCT-1000 for Measurement of Retinal Thickness is identical to the FDA-cleared Topcon 3D OCT-1000 (K063388), with the exception of the addition of a new software module allowing for the measurement of retinal thickness.

{
  "1. A table of acceptance criteria and the reported device performance": {
    "Acceptance Criteria": "The device provides accurate measurements that correlate with manually identified retinal boundaries in both healthy and diseased retinas.",
    "Reported Device Performance": "Topcon conducted performance testing demonstrating that from an analytical perspective, the device provides accurate measurements. Additional performance testing demonstrated that the accuracy of the measurements reported from the device correlate with measurements calculated using manually identified retinal boundaries in a population with healthy retinas and a population with diseased retinas."
  },
  "2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)": "The sample size for the test set is not specified. The study included 'a population with healthy retinas and a population with diseased retinas'. Data provenance (country of origin, retrospective/prospective) is not specified.",
  "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 document states that measurements were correlated with 'manually identified retinal boundaries'. The number and qualifications of experts involved in this manual identification are not specified.",
  "4. Adjudication method (e.g. 2+1, 3+1, none) for the test set": "Adjudication method is not specified.",
  "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": "A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned. The study focused on the correlation between device measurements and manually identified boundaries, not on human reader performance with or without AI assistance.",
  "6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done": "Yes, a standalone performance study was conducted. The document states, 'Topcon conducted performance testing demonstrating that from an analytical perspective, the device provides accurate measurements.' and 'the accuracy of the measurements reported from the device correlate with measurements calculated using manually identified retinal boundaries'. This indicates an evaluation of the algorithm's direct measurement capabilities.",
  "7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)": "The ground truth used was based on 'manually identified retinal boundaries'. This implicitly suggests expert human assessment as the basis for comparison.",
  "8. The sample size for the training set": "The sample size for the training set is not specified.",
  "9. How the ground truth for the training set was established": "How the ground truth for the training set was established is not specified in the provided document."
}

Ask a specific question about this device

The Topcon 3D OCT-1000 is a non-contact ophthalmic imaging system for the viewing and axial cross sectional imaging of posterior ocular structures. It is used for in vivo imaging of the retinal nerve fiber layer and optic disc. It is intended for use as a diagnostic device to aid in the detection and management of ocular diseases, including but not limited to macular edema and central serous retinopathy.

The components of the Topcon 3D OCT-1000 include a Main Unit, which houses three optical systems for observing and photographing the retina, a Power Supply Unit, a chin rest, a Spectroscope, and the ability to connect a personal computer for image viewing and analysis.

Here's a breakdown of the acceptance criteria and study details for the Topcon 3D OCT-1000 Optical Coherence Tomography System, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

2. Sample Size and Data Provenance

• Test Set Sample Size: 55 images from 31 eyes. This included at least seven pairs each from subjects with normal eyes, macular holes, cystoid macular edema (CME), epiretinal membrane (ERM), and both dry and wet form age-related macular degeneration.
• Data Provenance: Not explicitly stated in terms of country of origin. The study appears to be a prospective collection of images specifically for this comparison, as it states "Topcon collected 55 images from 31 eyes... obtained using both the 3D OCT-1000 device and a predicate device." This suggests a controlled collection rather than a retrospective analysis of existing data.

3. Number and Qualifications of Experts for Ground Truth

• Number of Experts: Two graders.
• Qualifications: "Predefined diagnostic image criteria" were used, implying the graders were qualified to interpret these images against those criteria, but their specific qualifications (e.g., ophthalmologists, optometrists, years of experience) are not explicitly stated.

4. Adjudication Method for the Test Set

• The text states: "Paired images were independently graded by two graders using predefined diagnostic image criteria. In all of the 55 pairs of images, the graders (with the consensus score) demonstrated..." This indicates an agreement/consensus method, likely a 2-reader consensus where their independent gradings were reconciled to arrive at a "consensus score." The exact method of achieving consensus (e.g., discussion, third reader) is not detailed beyond "consensus score."

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

• No, a MRMC comparative effectiveness study was not explicitly conducted in the typical sense of measuring human reader improvement with and without AI assistance. This study was a direct comparison of image quality between two devices (the new device and a predicate) as graded by human readers, not an assessment of AI's augmentative effect on human performance.
• Effect Size of Human Readers with vs. Without AI: Not applicable, as this was not the design of the study.

6. Standalone Performance (Algorithm Only)

• Yes, a standalone performance study was implicitly done for the device itself. The study's primary goal was to demonstrate that the device (Topcon 3D OCT-1000), in isolation, produced images of comparable or superior diagnostic quality to the predicate device. The grading by human experts was used to evaluate the device's output, not the performance of an AI algorithm within the device. The device's performance was assessed based on the quality of the images it generated.

7. Type of Ground Truth Used

• Expert Consensus / Predefined Diagnostic Image Criteria: The ground truth for evaluating image quality was established by "predefined diagnostic image criteria" applied by two independent graders, whose scores were then subject to a "consensus score." This is a form of expert consensus based on established criteria.

8. Sample Size for the Training Set

• Not applicable/Not provided. The provided text describes a device comparison study for regulatory submission, not the development or validation of an AI algorithm. Therefore, there is no mention of a training set for an AI model.

9. How Ground Truth for the Training Set Was Established

• Not applicable/Not provided (see point 8).

Ask a specific question about this device