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The DiLumen Endolumenal Interventional Scissors ("DiLumen Is™") is a disposable monopolar electrosurgical device intended to be used for cutting, and cauterizing tissue within the digestive tract during endoscopic procedures.

The DiLumen I , is a sterile, single patient use, disposable instrument that consists of a pistol style handle, a flexible shaft with an articulating section at the distal end, and scissor blades. The handle incorporates controls that enable one-handed operation of the device, including rotating the shaft; opening, closing and rotating the blades independently from the shaft; articulating the distal end of the shaft in a specific plane; and locking the articulation in a fixed position. This allows the clinician maximum flexibility to cut, dissect and cauterize tissue during endoscopic interventions in the digestive tract. The clinician can insert the DiLumen Is into any endoscopic tool channel with a diameter of at least 6mm. With the DiLumen Is under direct visualization, the clinician can position the scissor blades as appropriate for the particular procedure using blade rotation, blade articulation, and shaft rotation as necessary. The clinician can then cut gastrointestinal tissue either with or without energizing the blades, depending on the specific need during the procedure.

In addition, the DiLumen Is has a plug at the bottom of the handle. If desired by the clinician, this plug can be used to connect the device to an electrosurgical generator via a dedicated cord in order to apply monopolar electrical current through the blades to the target site in the gastrointestinal tract. When the device is connected to the generator, monopolar current can be applied through the blades to cut or cauterize tissue electrosurgically.

The DiLumen I , can also be used in conjunction with other endoscopic devices such as graspers, knives, etc. to perform endolumenal interventions.

The provided text describes the 510(k) premarket notification for the DiLumen Endolumenal Interventional Scissors (DiLumen Is™). This document focuses on demonstrating substantial equivalence to a predicate device, rather than proving that the device meets specific performance criteria through a study involving AI performance or human reader improvement, as typically found in AI/ML medical device submissions.

Therefore, many of the requested details about acceptance criteria, study design for AI models (sample size, data provenance, number of experts for ground truth, MRMC studies, standalone performance, training sets), and adjudication methods cannot be extracted from the provided text.

The document details performance testing that demonstrates the device meets specifications and is as safe and effective as the predicate. These tests are primarily engineering and mechanical evaluations, rather than clinical efficacy studies in the context of AI diagnostic or assistive devices.

Here's what can be extracted based on the provided text:

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

The document lists performance tests but does not provide specific acceptance criteria or quantitative results for these tests. It states qualitatively that "In all instances, the device functioned as intended and the results observed were as expected."

Regarding the other points, the information is not available in the provided text:

• 2. Sample sized used for the test set and the data provenance: Not mentioned. The "performance data" refers to engineering and bench testing, not clinical data for an AI model.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable/not mentioned. "Ground truth" in this context would refer to device specifications and performance metrics, not expert interpretations of medical images for AI.
• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable/not mentioned.
• 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, an MRMC study was not done as this is not an AI/ML device.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No, this is not an AI/ML device.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not explicitly stated in terms of common AI model ground truth. The "ground truth" for this device's performance would be engineering specifications and functional requirements.
• 8. The sample size for the training set: Not applicable/not mentioned. This device does not have a "training set" in the context of AI/ML.
• 9. How the ground truth for the training set was established: Not applicable/not mentioned.

In summary, the provided document describes a traditional medical device submission (510(k)) that focuses on demonstrating substantial equivalence through a comparison of technological characteristics and performance testing against engineering specifications, rather than clinical studies involving AI or machine learning.

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SB Soft Hood is intended for use with SB Knife for endoscopic submucosal dissection within the gastrointestinal tract and for use in keeping the suitable depth of endoscopic field of view.

SB Soft Hood™ is a transparent distal attachment applied to the tip of an endoscope. It is used in endoscopic submucosal dissection (hereinatter ESD) procedures. The subject device is used for maintaining visualization during the dissection phase of the procedure. The subject device is disposable and in a variety of diameters that correspond to different endoscope sizes. Each model of the subject device contains a drainage side hole that allows egress of water and blood.

This document is a 510(k) Summary for the SB Soft Hood™ device. It details the device's description, indications for use, comparison to a predicate device, and non-clinical testing performed to demonstrate substantial equivalence.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics in the format typically seen for algorithm performance (e.g., sensitivity, specificity, AUC). Instead, the "acceptance criteria" are implied by the non-clinical tests performed and the conclusion of "no new concern" or meeting "in-house standard which was determined based on expected worst-case."

Here's an interpretation of the implied acceptance criteria and reported performance:

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This device is a physical medical device, not an AI/software device that processes data or images. Therefore, the concepts of "test set sample size" and "data provenance" (country of origin, retrospective/prospective) stemming from AI/machine learning studies are not applicable to this submission. The tests performed are non-clinical, laboratory-based tests on the device itself.

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)

Again, the device is a physical medical accessory for endoscopy, not an algorithm requiring expert-established ground truth from images or data. Therefore, this information is not applicable. The "ground truth" for the non-clinical tests is based on established engineering and biological standards (e.g., ISO, USP, ASTM).

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

Since this is not an AI/imaging study with subjective interpretation, an adjudication method for a "test set" is not applicable. The test results are objective measurements against established standards.

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 done. This type of study is relevant for evaluating the impact of AI systems on human interpretation, which is not applicable to a physical endoscopic accessory. The document explicitly states: "Clinical testing was not performed for SB Soft Hood™."

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

No, a standalone performance study (algorithm only) was not done because this is a physical medical device, not an algorithm.

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

The concept of "ground truth" as typically used in AI/diagnostic studies (e.g., pathology, clinical outcomes) is not applicable here. For the non-clinical tests performed, the "truth" or reference is defined by:

• Established international and national standards (e.g., ISO 10993-5, ISO 10993-10, ISO 10993-11, USPC 151, ISO 11135-1:2007, ASTM F1929, ASTM F88/F88M, ISO10993-7:2008, USP).
• "In-house standard which was determined based on expected worst-case" for performance testing.

8. The sample size for the training set

This is not applicable as the device is not an AI/machine learning product and does not involve training data.

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

This is not applicable as the device is not an AI/machine learning product and does not involve training data or ground truth establishment for a training set.

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