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The CooperSurgical Advincula Delineator™ Uterine Manipulator is indicated to provide delineation of the vaginal fornices and maintain pneumoperitoneum as a uterine manipulator during Total Laparoscopic Hysterectomy, Laparoscopic Assisted Vaginal Hysterectomy and/or Laparoscopic Supra-Cervical Hysterectomy.

The Advincula Delineator Uterine Manipulator is intended for use during total laparoscopic hysterectomy (TLH), laparoscopic assisted vaginal hysterectomy (LAVH), and/or laparoscopic supracervical hysterectomy (LSH) procedures. It is single-use, disposable, and provided sterile. The device has a distal balloon built into the arched shaft, and a sliding colpotomy cup, called a Koh-Cup, that locks into place with a position lock. The handle at the proximal end allows the user to hold the device and to manipulate the uterus. When properly positioned, the outer rim of the Koh-Cup delineates the vaginal fornices and provides an anatomical landmark to facilitate uterine resection. An occluder balloon is used to maintain pneumoperitoneum during the procedure.

The Advincula Delineator Uterine Manipulator is available with Koh-Cups sized 2.5cm or 4.0cm in diameter. The Koh-Cup is made of Hytrel and the manipulator is made of stainless steel, silicone, various plastics, adhesives and inks. The subject device is intended to be used in hospitals.

This document describes the premarket notification (510(k)) for the Advincula Delineator™ Uterine Manipulator, which is a medical device and not an AI/ML powered device. As such, it does not involve the type of acceptance criteria and study designs typically associated with AI/ML device performance (e.g., sensitivity, specificity, MRMC studies, ground truth established by experts).

The acceptance criteria presented here are for the substantial equivalence of a modified medical device to a predicate device, focusing on material changes and packaging, rather than an AI algorithm's diagnostic or predictive performance.

Therefore, the requested information, specifically regarding AI/ML device performance, acceptance criteria, test set details, expert involvement, and ground truth methodologies, cannot be extracted from this document as it is not relevant to the described device and its review process.

Instead, the document focuses on:

• Acceptance Criteria (for device modification equivalence): The "acceptance criteria" here refer to demonstrating that changes made to the device (specifically, the Koh-Cup material and packaging) do not raise new questions of safety or effectiveness compared to the predicate device.

  • The criteria are met by passing a series of non-clinical performance tests.

• Study Proving Acceptance Criteria Met: The study undertaken was a series of non-clinical performance tests on the modified device.

Here's a breakdown of the relevant information provided in the document:

1. Table of Acceptance Criteria and Reported Device Performance

Regarding the AI/ML specific questions (2-9), this document does not contain that information as the device is not an AI/ML product.

• 2. Sample size used for the test set and the data provenance: Not applicable. Testing was performed on physical device samples, not data sets.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI/ML model is not relevant here. Device performance was assessed against engineering and biocompatibility standards.
• 4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.
• 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 not an AI-assisted device.
• 6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable. "Ground truth" here refers to the pre-established engineering and biocompatibility specifications and standards the physical device needed to meet.
• 8. The sample size for the training set: Not applicable. There is no training data set for this physical medical device.
• 9. How the ground truth for the training set was established: Not applicable.

In summary, the document details the testing performed to demonstrate that a physical medical device with material and packaging changes is substantially equivalent to a previously cleared predicate device, rather than the performance of an AI/ML algorithm.

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The CooperSurgical Advincula Delineator Uterine Manipulator is indicated to provide delineation of the vaginal fornices and maintain pneumoperitoneum as a uterine manipulator during Total Laparoscopic Hysterectomy (TLH), Laparoscopic Assisted Vaginal Hysterectomy (LAVH) and/or Laparoscopic Supracervical Hysterectomy (LSH).

The Advincula Delineator is a uterine manipulator that is intended for use during total laparoscopic hysterectomy (TLH), laparoscopic assisted vaginal hysterectomy (LAVH), and/or laparoscopic supracervical hysterectomy (LSH) procedures. It is single-use, disposable, and provided sterile. The device has a distal balloon built into the arched shaft, and a sliding colpotomy cup, called a Koh-Cup, that locks into place with a position lock. The handle at the proximal end allows the user to hold the device and to manipulate the uterus. When properly positioned, the outer rim of the Koh-Cup delineates the vaginal fornices and provides an anatomical landmark to facilitate uterine resection. An occluder balloon is used to maintain pneumoperitoneum during the procedure.

The Advincula Delineator is available with Koh-Cups sized 2.5cm, 3.5cm or 4.0cm in diameter. There are flexible plastic and Ultem plastic Koh-Cups available for use in electrocautery procedures, and a version with a stainless-steel outer rim for use in harmonic scalpel or laser procedures.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text.

Device: Advincula Delineator™ Uterine Manipulator (K180429)

Type of Device: Uterine Manipulator, Class II

This device is not an AI/ML powered device, so all the acceptance criteria and study information is related to mechanical and material performance, not algorithmic performance. Therefore, sections pertaining to expert review, MRMC studies, standalone performance, and training/test set details for AI/ML will be marked as "Not Applicable (N/A)".

1. Table of Acceptance Criteria and Reported Device Performance

This device is a physical medical device, not a software or AI/ML product. The acceptance criteria focus on mechanical performance and biocompatibility.

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

The document does not explicitly state the numerical sample size for each test (e.g., number of devices tested in the pull-off or compression tests). However, it implies testing was performed on a sufficient number of devices to demonstrate the device meets acceptance criteria.

• Data Provenance: The tests are explicitly non-clinical performance and design validation tests on the physical device itself. The "cadaveric model" implies testing was performed using human cadavers.
  • Country of Origin of the Data: Not specified, but likely performed by or for CooperSurgical, Inc. in the US.
  • Retrospective or Prospective: These are prospective tests performed specifically for regulatory submission.

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

N/A. This is a physical device, not an AI/ML system requiring expert interpretation or ground truth establishment in a diagnostic context. The "ground truth" here is the physical performance of the device against engineering specifications.

4. Adjudication Method for the Test Set

N/A. Adjudication methods like 2+1 or 3+1 are relevant for subjective interpretations (e.g., in medical image reading). For objective engineering tests (pull-off, compression, biocompatibility), the results are typically quantitative and directly measured against a specification.

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

No. An MRMC study is not applicable to a uterine manipulator. These studies are typically performed to assess the impact of AI on human reader performance for diagnostic tasks.

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

No. This a physical medical device, not an algorithm.

7. The Type of Ground Truth Used

The "ground truth" for this device's performance is based on engineering specifications, material science principles, and established biocompatibility standards (e.g., ISO 10993). For the design validation, the "ground truth" was the device "performs as intended and meets user needs" in a cadaveric model, which would be an outcome-based assessment in a simulated use environment.

8. The Sample Size for the Training Set

N/A. This term is applicable to AI/ML models. For a physical device, the concept of a "training set" is not relevant in the same way. The design and manufacturing processes are iterative, but there's no defined "training set" as in machine learning.

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

N/A. As above, this concept doesn't apply to this kind of device.

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The CooperSurgical Advincula Delineator™ Uterine Manipulator is indicated to provide delineation of the vaginal fornices and maintain pneumoperitoneum as a uterine manipulator during Total Laparoscopic Hysterectomy (TLH), Laparoscopic Assisted Vaginal Hysterectomy (LAVH) and/or Laparoscopic Supra-Cervical Hysterectomy (LSH).

The CooperSurgical Advincula Delineator™ Uterine Manipulator is a single use device, supplied sterile to the end user. It includes a cup shaped sliding colpo-pneumo occluder used to delineate the vaginal fornices, providing an anatomical marker to facilitate resection of the uterus, and a balloon to occlude the vaginal vault to maintain pneumoperitoneum during uterine resection.

The provided document is a 510(k) summary for the CooperSurgical Advincula Delineator™ Uterine Manipulator. It focuses on demonstrating substantial equivalence to a predicate device, rather than providing detailed acceptance criteria and a study to prove a device meets those criteria from an AI/algorithm performance perspective.

Therefore, many of the requested categories (e.g., sample size for test set, number of experts for ground truth, MRMC study, sample size for training set, how ground truth for training set was established) are not applicable to this type of medical device submission. This document describes a physical medical device, not an AI or algorithmic device.

However, I can extract the relevant performance data and conclusions presented in the document based on the provided text.

Here's a breakdown of the information that is available in the document, formatted as requested where possible:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in a quantitative manner as one would for an AI algorithm (e.g., "sensitivity must be > 90%"). Instead, performance assessments were conducted to ensure the device performs comparably to its predicate and is safe and effective. The "acceptance" is implied by demonstrating substantial equivalence.

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 specified in the 510(k) summary. These would typically be engineering bench tests involving a certain number of manufactured units, not patient data.
• Data Provenance: Not applicable as this relates to bench testing of a physical device, not patient data or geographical origin of data.
• Retrospective/Prospective: Not applicable.

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. This is a physical device, not an AI or image-based diagnostic tool requiring expert ground truth for performance evaluation of an algorithm. "Ground truth" for this device would be established by engineering specifications and physical measurements.

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

• Not Applicable. This relates to clinical interpretations, not bench testing of a physical 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

• Not Applicable. This is not an AI-assisted device or diagnostic tool.

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

• Not Applicable. This is not an AI or algorithmic device.

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

• Not Applicable in the traditional sense. The "ground truth" for this device's performance would be derived from engineering specifications, material properties testing, and comparison to the predicate device's established performance through bench testing (e.g., measuring actual burst volume, tensile strength, confirming no leakage).

8. The sample size for the training set

• Not Applicable. This is not an AI or machine learning device that requires a training set.

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

• Not Applicable. This is not an AI or machine learning device.

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