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IDx-DR is indicated for use by healthcare providers to automatically detect more than mild diabetic retimopathy (mtmDR) in adults diagnosed with diabetes who have not been previously diagnosed with diabetic retinopathy. IDx-DR is indicated for use with the Topcon NW400.

The IDx-DR device is an autonomous, artificial intelligence (AI)-based system for the automated detection of more than mild diabetic retinopathy (mtmDR). It consists of several component parts: IDx-DR Analysis, IDx-DR Client, and IDx-DR Service. The IDx-DR Analysis software analyzes patient images and determines exam quality and the presence/absence of mtmDR. The IDx-DR Client is a software application running on a computer connected to the fundus camera, allowing users to transfer images and receive results. The IDx-DR Service comprises a general exam analysis service delivery software package with a webserver front-end, database, and logging system, and is responsible for device cybersecurity. The system workflow involves image acquisition using the Topcon NW400, transfer to IDx-DR Service, analysis by IDx-DR Analysis System, and display of results on the IDx-DR Client.

The provided text describes a 510(k) submission for IDx-DR v2.3, a diabetic retinopathy detection device. The submission aims to demonstrate substantial equivalence to a predicate device (IDx-DR v2.0).

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

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

The document implicitly uses the performance of the predicate device (IDx-DR v2.0) as the acceptance criteria for the new version (IDx-DR v2.3). The study's goal is to show that IDx-DR v2.3 performs comparably to or better than IDx-DR v2.0. The primary endpoints are sensitivity, specificity, and "diagnosability." Secondary endpoints are positive prophetic value (PPV) and negative predictive value (NPV).

Here's a table comparing the performance of the subject device (IDx-DR v2.3) and the predicate device (IDx-DR v2.0) based on "final submission" images, which are the most relevant for diagnostic performance. The document presents ranges for performance, but for the sake of clarity, I've used the point estimates presented in the tables for both the subject and predicate devices. The values in parentheses are the 95% Confidence Intervals.

The document concludes that "The results of the clinical study support a determination of substantial equivalence between IDx-DR v2.3 and IDx-DR v2.0." This implies that the observed performance of IDx-DR v2.3 falls within an acceptable range, demonstrating non-inferiority or similar performance to the predicate device. Specific numerical acceptance thresholds (e.g., "must be at least X%") are not explicitly stated, but the comparison to the existing cleared device acts as the benchmark.

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

• Sample Size for Test Set: Data from 892 participants from the pivotal study of the predicate device were used. Of these, images from 850 participants were available for analysis and were diagnosable by the clinical reference standard, making them evaluable for performance.
• Data Provenance: The data was retrospectively collected from the pivotal study of the predicate device ("IDx-DR v2.0"; Abràmoff et al. Digital Medicine 2018;1:39). The country of origin is not explicitly stated in the provided text.

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

The document refers to a "clinical reference standard" and states that IDx-DR has "the ability to perform analysis on the specific disease features that are important to a retina specialist for diagnostic screening of diabetic retinopathy." However, the exact number of experts, their specific qualifications (e.g., number of years of experience, board certification), and their role in establishing the ground truth for the test set are not explicitly detailed in the provided text. It mentions an article by Abràmoff et al. (2018), which likely describes the ground truth establishment for the original pivotal study.

4. Adjudication method for the test set

The adjudication method used to establish the clinical reference standard for the test set is not explicitly stated in the provided text. It mentions a "clinical reference standard" but does not detail how it was established (e.g., 2+1, 3+1, etc.).

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted. The study evaluated the standalone performance of the algorithm (IDx-DR v2.3) by comparing it against the clinical reference standard, and then comparing its performance to the predicate algorithm (IDx-DR v2.0). There is no mention of human readers assisting the AI, nor is there any data on human reader improvement with AI assistance.

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

Yes, a standalone (algorithm only) performance study was conducted. The study assesses the ability of IDx-DR v2.3 to automatically detect more than mild diabetic retinopathy (mtmDR) and compares its sensitivity, specificity, and diagnosability to the predicate device's standalone performance.

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

The ground truth used for the test set is referred to as a "clinical reference standard" by which participants were "diagnosable." This strongly implies expert consensus by retina specialists, as suggested by the mention of the algorithm identifying "specific disease features that are important to a retina specialist." However, the exact methodology is not detailed within this document.

8. The sample size for the training set

The document does not provide information regarding the sample size of the training set used for IDx-DR v2.3. The provided study is a retrospective validation of the modified algorithm using a pre-existing dataset.

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

The document does not provide information on how the ground truth for the training set was established. It focuses solely on the clinical performance testing (validation) of the device using a pre-existing test set.

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IDx-DR is indicated for use by healthcare providers to automatically detect more than mild diabetic retinopathy in adults diagnosed with diabetes who have not been previously diagnosed with diabetic retinopathy. IDx-DR is indicated for use with the Topcon NW400.

The IDx-DR device consists of several component parts. A camera is attached to a computer, where IDx-DR client is installed. Guided by the Client, users acquire two fundus images per eye to be dispatched to IDx-Service. IDx-Service is installed on a server hosted at a secure datacenter. From IDx-Service, images are transferred to IDx-DR Analysis. No information other than the fundus images is required to perform the analysis. IDx-DR Analysis, which runs on dedicated servers hosted in the same secure datacenter as IDx-Service, processes the fundus images and returns information on the exam quality and the presence or absence of mtmDR to IDx-Service. IDx-Service then transports the results to the IDx-DR Client that displays them to the user.

Here's an analysis of the acceptance criteria and study information for the IDx-DR device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary (K203629) states that the device modifications do not affect clinical performance and refers to the predicate device (DEN180001) for clinical trial details. Therefore, the acceptance criteria and reported device performance are identical to the predicate device. To provide complete information, one would need to refer to the DEN180001 submission. However, based solely on the provided document K203629, the table would look like this:

Important Note: To get the actual numerical acceptance criteria (e.g., sensitivity, specificity thresholds) and the reported performance values, the DEN180001 submission would need to be reviewed. This document explicitly avoids providing those details for the current submission.

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

Since the current submission (K203629) states that "The determination of substantial equivalence is not based on an assessment of clinical performance data" and refers to DEN180001 for clinical trial details, this information is not available in the provided text.

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

This information is not provided in the K203629 document. It would be found in the clinical trial details for the predicate device (DEN180001).

4. Adjudication Method for the Test Set

This information is not provided in the K203629 document. It would be found in the clinical trial details for the predicate device (DEN180001).

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance is not mentioned in the furnished K203629 document. The document explicitly states that the substantial equivalence determination is not based on new clinical performance data and refers to the predicate device's clinical trial.

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

The K203629 document describes the IDx-DR Analysis component as "Software that analyzes the patient's images and determines exam quality and the presence/absence of diabetic retinopathy." This implies a standalone algorithmic assessment. However, the performance metrics of this specific version of the standalone algorithm are not presented in this document, as it relies on the predicate device's clinical performance. The "Outputs" section of Table 1 supports the standalone nature of the output, as it directly states the detection of DR and referral decisions.

7. The Type of Ground Truth Used

This information is not provided in the K203629 document. It would be found in the clinical trial details for the predicate device (DEN180001). Typically, for diabetic retinopathy, ground truth is established by a panel of expert ophthalmologists or retina specialists through consensus reading of images, potentially correlated with other clinical findings.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set. It mentions "Future algorithm improvements will be made under a consistent medically relevant framework" and "A protocol was provided to mitigate the risk of algorithm changes," but no details on training data for the current or previous versions are given.

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

The document does not provide details on how the ground truth for the training set was established.

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IDx-DR is indicated for use by health care providers to automatically detect more than mild diabetic retinopathy (mtmDR) in adults diagnosed with diabetes who have not been previously diagnosed with diabetic retinopathy. IDx-DR is indicated for use with the Topcon NW400.

The IDx-DR consists of several components. A fundus camera is attached to a computer, where the IDx-DR Client is installed. The Client allows the user to interact with the server-based analysis software over a secure internet connection. Using the Client, users identify two fundus images per eye to be dispatched to IDx-Service is installed on a server hosted at a secure datacenter. IDx-DR Analysis, which runs inside IDx-Service, processes the fundus images and returns information on the image quality and the presence or absence of mtmDR to IDx-Service. IDx- Service then returns the results to the IDx-DR Client.

Acceptance Criteria and Device Performance for IDx-DR

This document details the acceptance criteria for the IDx-DR device and summarizes the study conducted to demonstrate its performance.

1. Acceptance Criteria and Reported Device Performance

The primary outcomes for the IDx-DR study were sensitivity and specificity for detecting more than mild diabetic retinopathy (mtmDR). Pre-defined performance thresholds were established, and the study results demonstrate the device met these criteria.

Note: The reported performance also includes enrichment-corrected sensitivity and specificity, which were also high and met the thresholds.

2. Sample Size and Data Provenance for Test Set

• Sample Size (Test Set): 819 participants were fully analyzable in the pivotal clinical study.
• Data Provenance: The data was collected prospectively from 10 primary care sites across the United States. The target population was adults diagnosed with diabetes who had not been previously diagnosed with diabetic retinopathy. The study population was enriched by targeting enrollment of subjects with elevated Hemoglobin A1c (HbA1C) levels.

3. Number and Qualifications of Experts for Ground Truth (Test Set)

• Number of Experts: Three experienced and validated readers.
• Qualifications of Experts: The readers were certified by the Fundus Photography Reading Center (FPRC) and had expertise in evaluating the severity of retinopathy and diabetic macular edema (DME) according to the Early Treatment for Diabetic Retinopathy Study (ETDRS) scale and Diabetic Retinopathy Clinical Research Network (DRCR) grading paradigm.

4. Adjudication Method for the Test Set

The adjudication method used for establishing the ground truth from the FPRC readers was a majority voting paradigm for the four widefield stereo image pairs.

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

No explicit MRMC comparative effectiveness study involving human readers' improvement with AI vs. without AI assistance was reported. The study focused on the standalone performance of the IDx-DR device against an expert-derived reference standard.

6. Standalone (Algorithm Only) Performance

Yes, a standalone performance study was conducted. The reported sensitivity, specificity, PPV, and NPV values are for the IDx-DR algorithm operating autonomously, without human-in-the-loop assistance during the diagnostic process.

7. Type of Ground Truth Used (Test Set)

The ground truth used was expert consensus based on comprehensive ophthalmic imaging (dilated four widefield stereo color fundus photography and macular optical coherence tomography (OCT) imaging) read by three experienced and validated readers at the Fundus Photography Reading Center (FPRC). The severity of retinopathy and DME was determined according to the ETDRS scale and DRCR grading paradigm, using a majority voting paradigm.

8. Sample Size for the Training Set

The document does not explicitly state the sample size used for the training set. It describes the clinical study as a pivotal clinical study with 900 enrolled patients, which formed the basis for evaluating the device's performance, but it does not specify what portion (if any) of this dataset was used for training or validation during the development phase. The language focuses on the "analyzable fraction" of participants for the primary outcomes, implying this was the test set.

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

The document does not provide details on how the ground truth was established for the training set. It primarily describes the methodology for establishing the ground truth for the test set used in the pivotal clinical study. It mentions that IDx has provided a full characterization of the technical parameters of the software, including a description of the algorithms, and that IDx will make future algorithm improvements under a consistent medically relevant framework. However, the details of training data ground truth establishment are not discussed.

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