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Deep Capsule® is an artificial intelligence (AI) assisted reading tool designed to aid small bowel capsule endoscopy reviewers in decreasing the time to review capsule endoscopy images for adult patients in whom the capsule endoscopy images were obtained for suspected small bowel bleeding. The clinician is responsible for conducting their own assessment of the findings of the AI-assisted reading through review of the entire video, as clinically appropriate. This tool is not intended to replace clinical decision-making.

Deep Capsule® is an artificial intelligence (AI) assisted reading tool designed to aid small bowel capsule endoscopy reviewers (SBCapER) in decreasing the time to review capsule endoscopy images for adult patients in whom the capsule endoscopy images were obtained for suspected small bowel bleeding.

Deep Capsule® is capable of detecting small bowel lesions, without differentiating them. The detection of lesions by Deep Capsule® is insufficient to achieve a direct diagnosis, which is dependent on the clinical integration of the different findings. Deep Capsule® should be integrated into this multifactorial and complex context, both in the diagnostic workup or in the patient follow-up.

In summary, although the Deep Capsule® detects small bowel lesions, it acts only as a support to the clinical decision. The ultimate diagnosis will always be given by the small bowel capsule endoscopy reviewers ("human in the loop").

It is important to note that Deep Capsule® also provides in the findings count as a non-AI software output, and user inputs that are inserted and edited by SBCapERs, including CapE device model, patient exam priority category, procedure date, responsible physician, clinical indication and medical notes.

Deep Capsule® software includes a main algorithm as illustrated in Figure 1:

• Small bowel lesion detection, which includes the small bowel lesion image analysis algorithm designed to automatically identify and localize potential small bowel lesions in capsule endoscopy images. Suspected findings are selected automatically from the video frames as illustrated in Figure 1 below, with the active video displayed on the left side of the user interface. On the right side, AI-selected frames are presented as a structured gallery of thumbnails, allowing the reviewer to efficiently navigate through suspected findings. The interface also provides exam details, frame indexing, clinical indication, findings count, and medical notes to support structured review.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter for Deep Capsule®:

1. Table of Acceptance Criteria and Reported Device Performance

The FDA clearance letter does not explicitly define "acceptance criteria" for the device's performance in a table format. However, it presents the results of its performance studies against benchmarks like "Expert Reading" (standalone algorithm testing) and "Expert Board (Ground Truth)" (clinical validation).

Based on the provided data, we can infer the performance metrics deemed acceptable:

2. Sample Size for the Test Set and Data Provenance

• Standalone Algorithm Testing Test Set:

  • Patients: 272 patients (from a total dataset of 1,133 patients).
  • Images: 101,802 images (from a total dataset of 321,357 images).
  • Data Provenance: Retrospectively collected from two clinical institutions in Portugal (Centro Hospitalar Universitário São João, Porto, Portugal; and ManopH – Laboratório de Endoscopia e Motilidade Digestiva, Lda., Vila Nova de Gaia, Portugal).

• Clinical Validation Study Test Set:

  • Patients: 330 patients.
  • Data Provenance: Retrospectively collected between January 2021 and April 2024 from seven independent clinical centers across four countries: Portugal, Spain, Brazil, and the United States. No overlap existed between training and clinical validation datasets, and clinical validation sites were independent from the institutions used for model development.

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

• Standalone Algorithm Testing (Internal Test Set): The document states that images were "expert-labeled images." It does not specify the exact number of experts, their qualifications, or the adjudication method for this initial labeling of the training/internal test dataset.
• Clinical Validation Study (Ground Truth):
  • Adjudicators: 5 independent expert adjudicators.
  • Qualifications: "board-certified gastroenterologists with extensive capsule endoscopy experience."
  • Clinical Readers: 15 board-certified gastroenterologists.
  • Qualifications mentioned for Clinical Readers: "certified specialists in gastroenterology and/or digestive endoscopy with a minimum of 5 years of post-fellowship clinical experience." Readers were located across the United States, Portugal, Spain, and Brazil. There was no overlap between clinical readers and adjudicators.
  • Image-level ground truth: "established by expert reviewers blinded to AI outputs." (Implies the 5 expert adjudicators).

4. Adjudication Method for the Test Set

• Standalone Algorithm Testing (for initial expert-labeled images - training/internal test): Not explicitly stated, but implies expert consensus or single expert labeling for the "expert-labeled images."
• Clinical Validation Study (for Expert Board Ground Truth): "Ground truth was established through independent manual review of full capsule endoscopy videos by two experienced gastroenterologists, with adjudication by an expert board in cases of disagreement." This is a 2+1 adjudication model.

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

Yes, an MRMC comparative effectiveness study was done.

• Effect Size of Human Readers Improving with AI vs. without AI assistance:
  • Diagnostic Yield: AI-aided CapE showed a Diagnostic Yield (DY) of 0.961 compared to Standard-of-Care (SoC) at 0.761. The difference in DY between AI-aided and SoC was 0.200 (0.149 - 0.251), with non-inferiority established (p < 0.001). This indicates a substantial improvement in diagnostic yield when readers are assisted by AI.
  • Sensitivity (Per-Patient): AI-aided CapE had a sensitivity of 0.972 compared to SoC at 0.762. The improvement in sensitivity for AI-aided reading over unaided (SoC) was 21.0% (13.9 - 27.6%).
  • Specificity (Per-Patient): AI-aided CapE had a specificity of 0.125 compared to SoC at 0.250. The difference in specificity was -12.5% (-34.7 - 11.9%). It decreased with AI assistance (due to the AI prioritizing sensitivity and detecting more potential lesions, leading to more "false positives" that clinicians must then review for true positivity).
  • Mean Reading Time: "mean reading time was significantly reduced with AI+Physician reading, as compared to standard reading." (Specific quantification of reduction not provided in the excerpt).

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone algorithm performance study was done. This is referred to as "Standalone Algorithm Testing" or "PERFORMANCE TESTING – BENCH – STANDALONE PERFORMANCE."

• Patient-Level Performance: Sensitivity 95.8%, Specificity 75%.
• Image-Level Performance: Sensitivity 92.1%, Specificity 88.0%.

7. Type of Ground Truth Used

• Standalone Algorithm Testing: "Expert-labeled images."
• Clinical Validation Study: Expert board reference standard involving "independent manual review of full capsule endoscopy videos by two experienced gastroenterologists, with adjudication by an expert board in cases of disagreement."

8. The Sample Size for the Training Set

• Patients: 861 patients (from a total of 1,133 patients).
• Images: 219,555 images (from a total of 321,357 images).

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

The document states: "Images included in the dataset [which includes the training set] were labeled by expert readers following a structured annotation process." It does not provide further details on the number of experts, their qualifications, or the exact adjudication method during the training data labeling phase.

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