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The SKOUT system is a software device designed to detect potential colorectal polyps in real time during colonoscopy examinations. It is indicated as a computer-aided detection tool providing colorectal polyps location information to assist qualified and trained gastroenterologists in identifying potential colorectal polyps during colonoscopy examinations in adult patients undergoing colorectal cancer screening or surveillance.

The SKOUT system is only intended to assist the gastroenterologist in identifying suspected colorectal polyps and the gastroenterologist is responsible for reviewing SKOUT suspected polyp areas and confirming the presence or absence of a polyp based on their own medical judgment. SKOUT is not intended to replace a full patient evaluation, nor is it intended to be relied upon to make a primary interpretation of endoscopic procedures, medical diagnosis, or recommendations of treatment/course of action for patients. SKOUT is indicated for white light colonoscopy only.

The SKOUT® system is a software-based computer aided detection (CADe) system for the analysis of highdefinition endoscopic video during colonoscopy procedures. The SKOUT system is intended to aid gastroenterologists with the detection of potential colorectal polyps during colonoscopy by providing an informational visual aid on the endoscopic monitor using trained software that processes the endoscopic video in real time.

Users will primarily interact with the SKOUT system by observing the software display, including the polyp detection box and device status indicator signal.

Polyp Detection Notification: The SKOUT system has a main graphical user interface (GUI) feature of the polyp detection notification. The polyp detection notification is a two-dimensional blue rectanqular outline generated around any suspected polyps on the endoscopic video feed. If there is no polyp detected, the bounding box does not appear. SKOUT® system pauses polyp detection when an endoscopic tools are detected in the video feed to ensure that the bounding box does not hinder any surgical procedure, biopsy, or resection or this may also occur when lighting conditions are deemed to be inadequate.

Device Status Indicator: The SKOUT system has an additional GUI feature that notifies users of the current device status (active or error):

• a two-dimensional green box with letter (S) when the device is powered on and actively processing video.
• o a two-dimensional grav box with letter (S) when a surgical tool is present.
• a red (X) with an error message: when there is an error with the video processing function of the SKOUT system, the green box will be replaced with a red X and error message to indicate an error has occurred.

The provided document is a 510(k) summary for the SKOUT® system, detailing its substantial equivalence to a previously cleared device. It does not contain an explicit list of acceptance criteria or a dedicated study section proving the device meets these criteria in the format requested.

However, based on the non-clinical testing section and the overall claim of substantial equivalence, we can infer some aspects related to performance and how it aligns with the predicate device. The document explicitly states that "Performance data demonstrates that the SKOUT system is as safe and effective as the predicate device."

Here's an attempt to answer your request based on the provided text, while acknowledging limitations due to the document's content:

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

The document does not explicitly state quantitative acceptance criteria for clinical performance that the device must meet in this 510(k) summary. Instead, it relies on the assertion of the algorithm remaining the same as the predicate and the overall safety and effectiveness being equivalent.

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

The document does not provide details on the sample size for a specific test set used to demonstrate clinical performance for the subject device (K230658). It relies on the assertion that "the algorithm between the two devices remains the same, therefore clinical performance remains unchanged" from the predicate device (K213686). This typically means that the clinical performance data was generated for the predicate.

For non-clinical testing:

• Software V&V, Electrical Safety, Human Factors: These tests were performed for the subject device (K230658), but the sample sizes (e.g., number of test cases, participants for human factors) are not specified in this summary.
• Data Provenance: Not specified for any clinical performance data for either the subject or predicate device.

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

This information is not provided in the given 510(k) summary. Given that the clinical performance is deemed "unchanged" from the predicate device due to the same algorithm, any studies defining ground truth would have been associated with the predicate submission (K213686).

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

This information is not provided in the given 510(k) summary.

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

The document does not report on an MRMC comparative effectiveness study for the subject device (K230658) or its predicate (K213686) in this summary. It only states that the device is "intended to assist qualified and trained gastroenterologists in identifying potential colorectal polyps." The effect size of human reader improvement with AI assistance is also not mentioned.

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

The document defines the SKOUT system as a "computer-aided detection tool providing colorectal polyps location information to assist qualified and trained gastroenterologists." It also explicitly states, "SKOUT is not intended to replace a full patient evaluation, nor is it intended to be relied upon to make a primary interpretation of endoscopic procedures, medical diagnosis, or recommendations of treatment/course of action for patients." This strongly suggests the device is intended for human-in-the-loop use.

While the "polyp detection function" is performed by an "artificial intelligence-based algorithm," the summary does not provide standalone algorithmic performance metrics (e.g., sensitivity, specificity, PPV, NPV for the algorithm alone). The focus is on its assistive role.

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

This information is not provided in the given 510(k) summary. For systems detecting polyps, pathology confirmation is the gold standard for ground truth, but this is not explicitly stated here.

8. The sample size for the training set

This information is not provided in the given 510(k) summary.

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

This information is not provided in the given 510(k) summary.

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The SKOUT system is a software device designed to detect potential colorectal polyps in real time during colonoscopy examinations. It is indicated as a computer-aided detection tool providing colorectal polyps location information to assist qualified and trained gastroenterologists in identifying potential colorectal polyps during colonoscopy examinations in adult patients undergoing colorectal cancer screening or surveillance.

The SKOUT system is only intended to assist the gastroenterologist in identifying suspected colorectal polyps and the gastroenterologist is responsible for reviewing SKOUT suspected polyp areas and confirming the presence of a polyp based on their own medical judgment. SKOUT is not intended to replace a full patient evaluation, nor is it intended to be relied upon to make a primary interpretation of endoscopic procedures, medical diagnosis, or recommendations of treatment/course of action for patients. SKOUT is indicated for white light colonoscopy only.

The SKOUT™ system is a software-based computer aided detection (CADe) system for the analysis of high-definition endoscopic video during colonoscopy procedures. The SKOUT™ system is intended to aid gastroenterologists with the detection of potential colorectal polyps during colonoscopy by providing an informational visual aid on the endoscopic monitor using trained software that processes the endoscopic video in real time.

Users will primarily interact with the SKOUT™ system by observing the software display, including the polyp detection box and device status indicator signal.

Acceptance Criteria and Device Performance for SKOUT™ System

1. Table of Acceptance Criteria and Reported Device Performance

The provided document doesn't explicitly state "acceptance criteria" in a separate section. However, it presents the results of "Non-clinical performance testing" and "Clinical Testing" with specific metrics and confidence intervals, which serve as the performance benchmarks the device has met.

Based on the information provided, here's a table summarizing the implicit acceptance criteria (as demonstrated by the positive study outcomes) and the reported device performance:

2. Sample Sizes and Data Provenance

• Standalone Algorithm Performance Testing:

  • Test Set Sample Size: 79 HD videos containing 94 polyps.
  • Data Provenance: Not specified regarding country of origin. The document states "a predefined set of 79 HD videos". It's implied to be retrospective as it's a "set of videos" used for evaluation.

• Clinical Testing (MCRCT):

  • Test Set Sample Size: A total of 1,359 patients were included in the modified Intention To Treat (mITT) population.
    • AI-aided arm (SKOUT™ system): 682 patients
    • Control arm (Standard colonoscopy): 677 patients
  • Data Provenance: Multi-center, prospective, randomized controlled trial conducted in the United States.

3. Number of Experts and Qualifications for Ground Truth Establishment

• Standalone Algorithm Performance Testing:

  • Number of Experts: "Expert gastroenterologists" (plural, but specific number not provided).
  • Qualifications: "Expert gastroenterologists" who reviewed and either validated, rejected, or created new labels. No specific details like years of experience are given, but "expert" implies significant experience.

• Clinical Testing:

  • Number of Experts: The endoscopists participating in the trial served as the "human experts" whose findings formed the basis of the clinical ground truth. The eligibility requirements for these providers were: a) United States board-certified gastroenterologist, b) Has performed at least 1,000 colonoscopies, and c) Has an ADR (Adenoma Detection Rate) greater than or equal to 25%.
  • Qualifications: Board-certified gastroenterologists with extensive experience (minimum 1,000 colonoscopies and a good ADR).

4. Adjudication Method for the Test Set

• Standalone Algorithm Performance Testing: "Ground truth was defined as data reviewed and either validated or created by expert gastroenterologists through a process referred to as gastroenterologist review. During gastroenterologist review, experts reviewed and either validated, rejected new labels post primary annotation." This implies a consensus process, but the exact number of experts involved in each review/adjudication step (e.g., 2+1, 3+1) is not explicitly stated. It mentions a "team of trained annotators" initially, then "expert gastroenterologists" for validation/adjudication.

• Clinical Testing: For the clinical trial, the ground truth was based on the findings confirmed by the gastroenterologist during the colonoscopy and subsequent pathology findings. As this was a clinical trial involving real-time diagnosis and procedure (polyp removal), standard clinical practice implicitly serves as the "adjudication" for polyp presence, with pathology confirming the nature of the resected tissue. There wasn't a separate, explicit "adjudication method" in the sense of multiple independent readers reviewing the same data retrospectively; rather, it was a prospective clinical study using the treating physician's findings as the clinical gold standard, confirmed by pathology when resected.

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

• Yes, a clinical study was performed that can be considered a form of comparative effectiveness study, although not explicitly labeled as an MRMC study in the traditional sense of multiple readers evaluating the same cases. It was a randomized controlled trial comparing two groups of patients (and thus, two groups of cases and different physicians): one with AI assistance and one without.
• Effect Size of Human Readers Improvement with AI vs. without AI assistance:
  • The primary performance endpoint was Adenomas Per Colonoscopy (APC).
  • Without AI (Control Arm): Mean APC = 0.830
  • With AI Assistance (Treatment Arm): Mean APC = 1.054
  • Effect Size / Improvement: The difference in APC was +0.224 (Treatment - Control), with a p-value of 0.002. This indicates a statistically significant increase in the number of adenomas detected per colonoscopy when the SKOUT™ system was used. This is a direct measure of how much human readers (gastroenterologists) improved their adenoma detection rate when assisted by the AI.

6. Standalone (Algorithm Only) Performance Study

• Yes, a standalone algorithm performance testing was done.
• Details: "The Standalone Performance Assessment was performed to assess trends in the detection and classification of the SKOUT™ system and determine its ability to discriminate between normal mucosa and polyp tissue." This included analysis of 79 HD videos. Performance metrics such as Object Level TPR, Object Level FP, Frame Level TPR, and Frame Level FPR were evaluated.

7. Type of Ground Truth Used

• Standalone Algorithm Performance Testing:

  • "Gastroenterologist review" followed by pathology findings for Object Level TPR (polyps confirmed by pathology findings).
  • For Frame Level metrics, the ground truth for polyp detection frames was established by expert gastroenterologists, with "normal mucosa" as the negative ground truth.

• Clinical Testing:

  • Clinical Diagnosis from Trained Gastroenterologist + Pathology: For the APC endpoint, adenomas were confirmed through histopathology after resection. For PPA/PPV, the ground truth was based on resected polyps confirmed by pathology.

8. Sample Size for the Training Set

• The document does not explicitly state the sample size used for the training set of the SKOUT™ system's AI algorithm. It only details the test set for standalone performance and the patient cohort for the clinical trial.

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

• The document does not explicitly detail how the ground truth for the training set was established. It mentions that the SKOUT™ system "utilizes an artificial intelligence-based algorithm to perform the polyp detection function" and refers to "trained software," implying a training phase. However, the specific process for ground truth establishment for the training data is not provided in this document, only for the evaluation (test) datasets.

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