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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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The device is indicated for use as a receptacle for the collection of tissue, organs and calculi during general and laparoscopic procedures.

The proposed device, Specimen Retrieval Bag, is comprised of retracted bag, steel sheet, plug, inner sleeve, outer sleeve, pull ring, outer introducer handle, joint, tightening line, pick rod and inner introducer handle. The device is available in a series of models: RJRB-50, RJRB-100, RJRB-120, RJRB-150, RJRN-200, RJRB-250, RJRB-300, RJRB-320, RJRB-420. The difference between each model is the retracted bag size and volume. The device is provided sterile and single use

This document is a 510(k) premarket notification for a "Disposable Specimen Retrieval Bag" and does not describe an AI/ML medical device. Therefore, it does not contain information about acceptance criteria for an AI device, a study proving an AI device meets acceptance criteria, sample sizes for test sets, data provenance, number of experts, adjudication methods, MRMC studies, standalone performance, type of ground truth, or training set details.

The document is for a physical medical device and is primarily focused on demonstrating "Substantial Equivalence" to a previously marketed predicate device based on non-clinical performance and biocompatibility tests.

The device is a physical medical device, not an AI medical device. Therefore, the requested information regarding AI device acceptance criteria, studies, and related data is not applicable and not present in the provided text.

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The device is indicated for use as a receptacle for the collection of tissue, organs and calculi during general and laparoscopic procedures.

The proposed device, Specimen Retrieval Bag, is comprised of retracted bag, opening support, outer introducer shaft, outer introducer handle, inner introducer handle, string and pull loop. The device is available in a series of models. The difference between each model is the retracted bag size and volume. The device is provided sterile and single use.

The provided text describes a 510(k) premarket notification for a Specimen Retrieval Bag. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study proving the device meets specific performance acceptance criteria for a novel medical device.

Therefore, many of the requested categories for a study proving acceptance criteria cannot be extracted because such a study was not presented in this document.

Here's what can be extracted and what cannot:

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

The document provides a comparison of the proposed device against a predicate device, focusing on technological characteristics and compliance with general standards. It does not explicitly state "acceptance criteria" for a study to prove performance but rather lists the tests performed to demonstrate substantial equivalence to the predicate.

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

The document does not provide details on the sample sizes used for the non-clinical tests or the data provenance. It only states that "performance tests were performed on both proposed device and predicate device."

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 and not provided. The study is a non-clinical, bench-testing evaluation designed to demonstrate substantial equivalence, not a clinical study involving human readers or expert ground truth for interpretation.

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

This information is not applicable and not provided, as there was no clinical study or expert adjudication described.

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 MRMC or clinical study was done. The document explicitly states: "No clinical study is included in this submission."

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

This concept is not applicable as the device is a physical specimen retrieval bag, not an AI algorithm. The tests performed are non-clinical, bench tests.

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

For the non-clinical performance tests, the "ground truth" would be the established scientific and engineering principles and the performance of the predicate device, as well as compliance with recognized standards (e.g., ISO, ASTM). It's not a clinical "ground truth."

8. The sample size for the training set

This information is not applicable as there is no mention of a "training set." The device is a physical medical device, not an AI or machine learning model that requires training data.

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

Not applicable as there is no training set mentioned.

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The specimen retrieval bag has application for safe, convenient removal of tissue specimens such as the appendix, an ectopic pregnancy, gallbladder, gallstones, lymph nodes, ovaries, small sections of bowel and other tissue structures.

The ReliaCatch™ 10mm Specimen Retrieval Bag and ReliaCatch™ 12mm Specimen Retrieval Bag consist of a long cylindrical tube and a rip-stop nylon bag that encapsulates and contains specimens for removal from the body cavity. The device contains a handle used to retract and deploy the bag. The specimen bag is designed for introduction and use through all appropriately sized trocar sleeves.

The provided text describes a 510(k) premarket notification for the ReliaCatch Specimen Retrieval Bag, comparing it to a predicate device. However, it does not contain the detailed acceptance criteria for the device's performance, nor does it present a study proving the device meets specific acceptance criteria in a structured manner.

The document primarily focuses on establishing substantial equivalence to a predicate device based on device description, intended use, and a general summary of performance data. It lists types of performance tests conducted but does not provide the quantitative acceptance criteria or the specific results.

Therefore, I cannot fulfill the request to provide a table of acceptance criteria and reported device performance, or details about sample size, ground truth, expert qualifications, adjudication methods, MRMC studies, or training set information. This information is not present in the provided document.

The document states:

• "Summary of testing includes description of test methods, sample size, acceptance criteria, and pass/fail results. Objective acceptance criteria were defined for each test."
• "Performance testing included: Leak Rate Evaluation, Trocar Insertion Force, Trocar Removal Force, Pouch Deployment Force, Pouch Retraction Force, Pouch Cinch Force, Pouch Burst Pressure, Human Factors/Usability"

This indicates that such information exists but is not detailed within this FDA letter or the included 510(k) summary.

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

The GENICON Single-Use Specimen Retrieval Bag is comprised of a flexible plastic bag with a large, easily accessible opening, a push-pull rod with thumb ring handle, finger rings, string and an introducer shaft. In the fully deployed condition, the bag opening is maintained in a fully-open position by a metallic rim. A string with a closure suture facilitates closure of the specimen bag after the specimen has been collected. This device is packaged and sterlized for single use only. Do not re-use, reprocess, or re-sterilize. Discard after use.

The provided document is a 510(k) summary for the GENICON Specimen Retrieval Bag. It details the device's description, indications for use, and a statement about nonclinical tests supporting substantial equivalence to predicate devices. However, it does not contain the specific acceptance criteria or a detailed study report that proves the device meets those criteria, as typically found in a clinical trial or performance study report. The document mentions "Bench Testing and Clinical Evaluation performed (ref. TR-12022-A and CLEV-032-A)," but the details of these tests are not provided in this regulatory submission.

Therefore, for aspects requiring detailed study information, the answer will reflect that the information is "Not Available in the provided text."

Here's an attempt to answer based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The provided document does not explicitly list quantitative acceptance criteria or detailed device performance metrics from a study. It only states that "Successful results support a determination of substantial equivalence" for general operation, seal strength peel, and puncture resistance. Without access to TR-12022-A and CLEV-032-A, specific criteria and performance values cannot be extracted.

2. Sample Size for the Test Set and Data Provenance

• Sample size used for the test set: Not available in the provided text. The document mentions "Bench Testing and Clinical Evaluation," but no sample sizes are given for either.
• Data provenance (e.g., country of origin of the data, retrospective or prospective): Not available in the provided text.

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

• Number of experts: Not available in the provided text.
• Qualifications of those experts: Not available in the provided text.

4. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set

• Adjudication method: Not available in the provided text.

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

• MRMC comparative effectiveness study: Not applicable. This device is a physical specimen retrieval bag, not an AI-assisted diagnostic or therapeutic tool for human readers. Therefore, an MRMC study comparing human reader performance with and without AI assistance is not relevant to this device.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Standalone performance study: Not applicable. This device is a physical medical instrument, not a software algorithm.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• Type of ground truth: Not available in the provided text. For bench testing, ground truth would likely be established by objective physical measurements according to specified test methods. For clinical evaluation, it would typically relate to the device's ability to safely and effectively retrieve specimens in a surgical setting, which could involve surgical observations, successful specimen retrieval without complications, and potentially pathology results of retrieved specimens. However, the document does not specify.

8. The Sample Size for the Training Set

• Sample size for the training set: Not applicable/Not available. This device is a physical specimen retrieval bag, and the concept of a "training set" is usually associated with machine learning algorithms, which is not applicable here. Even if a design verification/validation process involved iterative testing, the term "training set" as used in AI contexts does not apply.

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

• How the ground truth for the training set was established: Not applicable/Not available, as the concept of a "training set" for a physical device is not relevant in the context of this summary.

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ENDOPOUCH PRO Specimen Retrieval Bag is intended for use during general laparoscopic surgical procedures as well as the collection and extraction of tissue specimens such as the appendix, gall bladder, ovaries, fibroid tumors, other tissues and calculi.

ENDOPOUCH PRO Specimen Retrieval Bag is a disposable device used as a receptacle for the collection and extraction of tissue specimens such as the appendix, gall bladder, ovaries, fibroid tumors, other tissues and calculi during laparoscopic surgical procedures.

The ENDOPOUCH PRO device is comprised of a flexible plastic bag with a large, easily accessible opening, a push rod handle and an introducer tube. The push rod and handle allow for single hand deployment.

The provided text describes a 510(k) summary for a medical device called the ENDOPOUCH PRO Specimen Retrieval Bag. It outlines its intended use, technological characteristics, and a brief summary of performance data. However, the document does NOT contain the detailed information required to answer all the questions in your request, particularly those related to specific acceptance criteria, sample sizes, ground truth establishment, or multi-reader multi-case studies, as this device is not an AI/ML algorithm.

Here's an analysis of what can and cannot be answered based on the provided text:

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

• Explanation: The document states that "Pre-clinical in vivo as well as bench top testing has been performed to verify that the product meets the performance requirements described." However, it does not explicitly list the acceptance criteria (e.g., "bag strength must be X Newtons") nor provides quantitative reported device performance other than the qualitative statement about greater bag strength.

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

• Information Not Provided: The document mentions "Pre-clinical in vivo as well as bench top testing," but it does not specify the sample size for these tests, the type of data (e.g., if it involved human subjects or materials), or the provenance of any 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)

• Information Not Provided: This question is more relevant to studies involving interpretation by human experts, such as medical image analysis. For a specimen retrieval bag, the "ground truth" would likely relate to objective physical properties or functional performance, not expert consensus on interpretations.

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

• Information Not Provided: Adjudication methods are typically associated with resolving disagreements in expert interpretations, which is not applicable to the type of testing described for this device.

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.
• Explanation: An MRMC study is relevant for AI/ML-based diagnostic devices that assist human readers (e.g., radiologists interpreting images). The ENDOPOUCH PRO is a surgical instrument, not an AI/ML diagnostic aid. The document does not describe any such study.

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

• No, a standalone algorithm performance study was not done.
• Explanation: This question also pertains to AI/ML algorithms. The ENDOPOUCH PRO is a physical medical device, not an algorithm.

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

• Implied Ground Truth: For the "bag strength" testing, the ground truth would likely be established through objective physical measurements (e.g., tensile strength testing) rather than expert consensus, pathology, or outcomes data, as it's a mechanical property. The document doesn't specify the exact metrics or methods for establishing this.

8. The sample size for the training set

• Information Not Applicable/Provided: As this device is not an AI/ML algorithm, there is no "training set."

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

• Information Not Applicable/Provided: As this device is not an AI/ML algorithm, there is no "training set" and thus no ground truth established for one.

Summary of Study (as described for the ENDOPOUCH PRO Specimen Retrieval Bag):

The study referenced is "Pre-clinical in vivo as well as bench top testing." The purpose of this testing was to "verify that the product meets the performance requirements described." The key finding reported is that the device "has greater bag strength than either predicate device."

This documentation focuses on demonstrating substantial equivalence to predicate devices based on technological characteristics and functional performance testing, which is typical for a 510(k) submission of a conventional medical device. It does not involve AI/ML components or associated study methodologies like MRMC, standalone algorithm performance, or extensive details on ground truth establishment relevant to AI.

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The Specimen Retrieval Bag is indicated for use as a receptacle for the collection and extraction of tissue, organs and calculi during general and laparoscopic surgical procedures.

The Applied Medical Specimen Retrieval Bag consists of a stylet, an introducer sheath with a locking pneumoperitoneal seal at the proximal end and a retrieval bag sealed radially on the distal end. The locking seal serves as a mechanism for maintaining positive pressure in the peritoneal cavity during laparoscopic procedures. The sheath contains a dual lumen, where the larger of the two lumens acts as the working channel for the insertion and removal of grasping instruments while the second and smaller lumen contains the bag activation wire for positioning, opening and closing of the bag. The dual lumen design allows the use of a single trocar for placement and retrieval of tissue, organs and calculi.

Here's an analysis of the provided text in the context of device acceptance criteria and studies, as requested:

Device: Applied Medical Specimen Retrieval Bag

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: The document does not specify the sample size for any tests. It only states that "Mechanical and biocompatibility tests were performed."
• Data Provenance: The document does not provide information on data provenance (e.g., country of origin, retrospective/prospective). It describes laboratory-based mechanical and biocompatibility tests, not clinical data involving human subjects.

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

• This information is not applicable to this submission. The device is a physical medical device (specimen retrieval bag) and its performance is evaluated through engineering and biocompatibility testing, not through expert review of data like imaging or clinical assessments. Ground truth in this context would be defined by engineering specifications and biocompatibility standards.

4. Adjudication Method for the Test Set

• This information is not applicable. Adjudication methods are typically used in studies involving human interpretation or clinical endpoints. This submission describes physical and material property testing.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was Done

• No, an MRMC comparative effectiveness study was not done. This type of study assesses human reader performance, often with and without AI assistance, which is not relevant to a physical specimen retrieval bag.

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

• No, a standalone study in the context of an algorithm's performance was not done. This device is a physical tool, not an algorithm. Its "standalone" performance would be its inherent functional and structural integrity, which was tested.

7. The Type of Ground Truth Used

• The ground truth for this device's evaluation would be based on:
  • Engineering Specifications: Defined parameters for strength, sealing capability, lumen dimensions, etc.
  • Biocompatibility Standards: Established international standards (e.g., ISO 10993 series) for material safety testing and acceptable biological responses.
  • Predicate Device Performance: The functional and safety characteristics of the legally marketed predicate device (Endopouch™ Specimen Retrieval Bag) served as a benchmark for "comparability."

8. The Sample Size for the Training Set

• This concept is not applicable. A "training set" is relevant for machine learning algorithms. This submission describes testing of a physical medical device.

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

• This concept is not applicable for the same reason as point 8.

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