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EXALT Model D Single Use Duodenoscope: The EXALT Model D Single-Use Duodenoscope is intended for use with a Boston Scientific endoscopic video imaging system, for endoscopy and endoscopic surgery within the duodenum. EXALT Controller: The EXALT Controller is intended for use with a Boston Scientific endoscope for endoscopic diagnosis, treatment, and video observation.

The EXALT Model D Single-Use Duodenoscope is a sterile, single-use, flexible duodenoscope used to examine the duodenum and perform various procedures within the duodenum including endoscopy and endoscopic surgery. When connected to an EXALT Controller, the EXALT Model D Single-Use Duodenoscope provides imaging and illumination, 4-way steerable navigation, lens wash, insufflation, suction, facilitates image capture initiation, and allows the delivery of various ancillary devices. The EXALT Controller is an endoscopic video imaging system that receives video signals from the EXALT Model D Single-Use Duodenoscope, processes the video signals, outputs video images to a video monitor, and outputs electrical signals that interface with external image capture systems. The EXALT Controller also controls the light transmitted by the tip of the EXALT Model D Single-Use Duodenoscope to illuminate the area of interest within the anatomy.

This document does not contain the specific acceptance criteria or details of a study structured to prove a device meets those criteria in the manner one would find for an AI/ML medical device. Instead, it is an FDA 510(k) clearance letter for a hardware (medical device) product, specifically an endoscope and its controller.

Therefore, many of the requested points, such as sample size for test sets, number of experts for ground truth, adjudication methods, MRMC studies, standalone algorithm performance, and sample size/ground truth for training sets, are not applicable to the information provided. These elements are typically part of a submission for software as a medical device (SaMD) or AI/ML-driven devices.

However, based on the provided text, I can extract and infer information relevant to the device's performance demonstration for its 510(k) clearance, framed in the context of substantial equivalence to predicate devices:

1. Table of Acceptance Criteria and Reported Device Performance

For medical devices seeking 510(k) clearance, the "acceptance criteria" are generally demonstrating substantial equivalence to a legally marketed predicate device across various characteristics. The "reported device performance" refers to how the new device matches or performs comparably to the predicate device in those characteristics.

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

• Sample Size: The document mentions "clinical testing" for procedural performance but does not specify the sample size (i.e., number of patients or procedures) used for this clinical study.
• Data Provenance: The document does not explicitly state the country of origin or whether the clinical data was retrospective or prospective. It only states "Clinical testing was performed to confirm procedural performance...".

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

This point is not applicable. For hardware devices like endoscopes, "ground truth" as conceptualized for AI/ML performance (e.g., expert consensus on image interpretation) is not typically established in the same way. The procedural performance evaluation would likely involve assessments by practicing physicians/endoscopists, but their role is to assess the device's functionality and efficacy during the procedure, not to create an image-based "ground truth" dataset for an algorithm.

4. Adjudication Method for the Test Set

This point is not applicable. As there's no specific "test set" in the AI/ML sense (e.g., images requiring interpretation), there's no mention of an adjudication method.

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

This point is not applicable. MRMC studies are typically for evaluating the impact of AI on human reader performance, which is not relevant for this hardware device.

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

This point is not applicable. The EXALT Model D Duodenoscope and EXALT Controller are hardware devices, not standalone algorithms.

7. The Type of Ground Truth Used

The concept of "ground truth" (expert consensus, pathology, outcomes data) as applied to AI/ML image analysis is not relevant here. For a medical device like an endoscope, the "truth" is its functional performance during procedures, its ability to provide clear imaging, enable tool delivery, and adhere to safety standards (biocompatibility, electrical safety). The clinical testing assessed "procedural performance," which means observing the device's operation in real use cases, rather than establishing a diagnostic ground truth from images.

8. The Sample Size for the Training Set

This point is not applicable. Hardware devices like endoscopes do not have "training sets" in the context of machine learning.

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

This point is not applicable, as there is no training set for a machine learning algorithm.

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