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The ENDOCOLLECT Specimen Retrieval Bag is indicated for use in laparoscopic procedures to capture organs or tissue to be removed from the body cavity.

The ENDOCOLLECT Specimen Retrieval Bag ("ENDOCOLLECT") is a disposable device used as a receptacle for the collection and extraction of tissue specimens during laparoscopic surgery and is intended to be used with an endoscopic trocar. ENDOCOLLECT is comprised of a flexible plastic bag with a large, easily accessible opening, a spring finger, deployment shaft, shaft handle, and an insertion tube. In the fully deployed condition, the specimen bag opening is maintained during the retrieval of a specimen. When the specimen is placed in the bag, the bag is closed with the cinch cord and the device may be removed from the body.

This FDA 510(k) summary does not describe a study involving AI or human readers, therefore, much of the requested information is not applicable. The device in question is a medical tool, the ENDOCOLLECT Specimen Retrieval Bag, used in laparoscopic procedures to capture organs or tissue. The 510(k) submission focuses on demonstrating substantial equivalence to a predicate device through non-clinical bench testing, not on AI performance or human reader studies.

Here's a breakdown of the applicable information:

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

The document does not provide a specific table of acceptance criteria with numerical performance metrics for an AI device. Instead, it lists the types of non-clinical bench testing conducted to demonstrate that the device meets established specifications and consistently performs for its intended use. The "reported device performance" is a general statement that "The collective results of the nonclinical testing demonstrate that the materials chosen, the manufacturing processes, and design of the ENDOCOLLECT Specimen Retrieval Bag meet the established specifications necessary for consistent performance during its intended use."

The categories of testing performed are:

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

This information is not applicable as the document describes non-clinical bench testing of a physical medical device, not a study involving a "test set" of data for an AI algorithm.

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)

This information is not applicable as the document describes non-clinical bench testing of a physical medical device, not an AI algorithm requiring expert-established ground truth.

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

This information is not applicable as the document describes non-clinical bench testing of a physical medical device, not an AI algorithm requiring adjudication of a test set.

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

There was no MRMC comparative effectiveness study done. The device is a specimen retrieval bag, not an AI-assisted diagnostic tool. No comparison to human readers with or without AI assistance is relevant or reported.

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

This information is not applicable as the document is about a physical medical device, not a standalone AI algorithm.

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

This information is not applicable as the document describes non-clinical bench testing of a physical medical device. The "ground truth" for such testing is typically based on pre-defined engineering specifications and industry standards rather than medical consensus or pathology.

8. The sample size for the training set

This information is not applicable as there is no training set mentioned in the context of this device.

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

This information is not applicable as there is no training set mentioned in the context of this device.

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The ENDOCOLLECT Specimen Retrieval Bag is indicated for use in laparoscopic procedures to capture organs or tissue to be removed from the body cavity.

The ENDOCOLLECT Specimen Retrieval Bag ("ENDOCOLLECT") is a disposable device used as a receptacle for the collection and extraction of tissue specimens during laparoscopic surgery and is intended to be used with an endoscopic trocar. ENDOCOLLECT is comprised of a flexible plastic bag with a large, easily accessible opening, a spring finger, deployment shaft, shaft handle, and an insertion tube. In the fully deployed condition, the specimen bag opening is maintained during the retrieval of a specimen. When the specimen is placed in the bag, the bag is closed with the cinch cord and the device may be removed from the body.

This document is an FDA 510(k) clearance letter for a medical device called the "ENDOCOLLECT Specimen Retrieval Bag." It details the regulatory process and the determination of substantial equivalence to a predicate device.

Based on the provided text, the device in question is a physical medical device (specimen retrieval bag) and not an AI/software-based medical device. Therefore, many of the requested points related to AI/MRMC studies, ground truth establishment, and training/test sets are not applicable to this submission.

The document primarily focuses on the nonclinical performance testing to demonstrate the device's safety and effectiveness compared to a legally marketed predicate device.

Here's an analysis of the provided information in the context of the requested points, noting where information is not applicable due to the nature of the device:

Device Type: ENDOCOLLECT Specimen Retrieval Bag (a physical, disposable device for laparoscopic tissue retrieval). This is NOT an AI/software device.

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

The document does not provide a direct table of specific numerical acceptance criteria and reported performance values. Instead, it offers a summary of the types of nonclinical bench testing conducted:

Overall Conclusion: "The collective results of the nonclinical testing demonstrate that the materials chosen, the manufacturing processes, and design of the ENDOCOLLECT Specimen Retrieval Bag meet the established specifications necessary for consistent performance during its intended use."

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

• Sample Size: Not explicitly stated for each test. The testing is described as "Nonclinical Bench Testing," implying laboratory-based tests of physical specimens of the device.
• Data Provenance: Not specified, but generally, bench testing would be conducted in a controlled lab environment. Given the applicant's address (Providence, RI, USA), it's likely conducted in the USA.
• Retrospective/Prospective: Not applicable. These are bench tests, not clinical studies involving patient data.

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)

Not applicable. The "ground truth" for a physical device like this is established through engineering and material science testing against predefined specifications, not human expert consensus on interpretations of images or data. Usability validation might involve human evaluators, but they wouldn't be establishing "ground truth" in the diagnostic sense.

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

Not applicable. This concept is relevant for studies involving human interpretation or consensus, such as clinical trials or image labeling. Bench testing relies on objective measurements against engineering specifications.

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

Not applicable. This is a physical device, not an AI/software device, and no MRMC study was conducted or required. The document explicitly states: "Clinical testing was not required to demonstrate substantial equivalence to the predicate device."

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

Not applicable. This is a physical device, not an algorithm.

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

For this physical device, the "ground truth" is defined by engineering specifications and validated testing methods. For example:

• Force and Volume Testing: The "ground truth" is the measured force or volume achieved by the device, compared against a target specification.
• Puncture Testing: The "ground truth" is whether the device withstands a specified puncture force without failure.
• Biocompatibility Testing: The "ground truth" is established by standard biological safety tests demonstrating the materials are non-toxic and biocompatible according to recognized standards (e.g., ISO 10993).

8. The sample size for the training set

Not applicable. This is a physical device, not a machine learning model that requires a training set.

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

Not applicable. See point 8.

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