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The Surgical gowns are intended to be worn by operating room personnel during surgical procedures to protect the surgical patient and operating room personnel from the transfer of microorganisms, body fluids and particulate material. In addition, this surgical gown meets the requirements of AAMI Level 3 barrier protection for a surgical gown per ANSU AAMI PB70:2012 Liquid barrier performance and classification of protective apparel and drapes intended for use in health care facilities (AAMI PB70). The Surgical gowns are single use, disposable medical devices, provided sterile.

The Surgical gowns is composed of collar, body, sleeve and tie. The back is full opening, the neck and waist are laced, the sleeve are made of cotton closure by sewing, and the rest are made of heat sealing. It has been tested according to AAMI PB70:2012 and meet AAMI Level 3 barrier level protection for a surgical gown.

The provided text is a 510(k) summary for a medical device, specifically Surgical Gowns. It details the device's characteristics, intended use, and comparative testing against a predicate device to demonstrate substantial equivalence.

However, the request asks for information relevant to the acceptance criteria and the study that proves a device (likely an AI/ML powered device for diagnosis or similar application) meets those criteria. The provided document does not describe an AI/ML powered device, nor does it detail a study involving expert readers or AI assistance. Instead, it focuses on non-clinical performance testing of a physical product (surgical gowns) based on recognized standards.

Therefore, most of the specific points requested (e.g., sample size for test set, data provenance, number of experts, adjudication method, MRMC study, standalone performance, ground truth for training set) are not applicable to the content of this document.

I will attempt to answer the parts that are applicable based on the provided document, interpreting "acceptance criteria" as the performance requirements for the surgical gowns and "study" as the non-clinical performance testing conducted.

Here's an adaptation based on the provided document, acknowledging the limitations:

Acceptance Criteria and Device Performance for Surgical Gowns (K212718)

This document describes the non-clinical performance testing of surgical gowns (K212718) to demonstrate substantial equivalence to a predicate device, rather than a clinical study of an AI/ML device. Therefore, many of the requested criteria related to AI/ML device testing (e.g., expert readers, MRMC studies, ground truth for training) are not applicable.

Below is a table summarizing the acceptance criteria (performance requirements based on standards) and the reported device performance for the surgical gowns.

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample Size Used for the Test Set and Data Provenance:
The document does not specify the exact sample sizes (e.g., number of gowns) used for each non-clinical performance test. The data provenance is implied to be from the manufacturer's testing of their product. The testing is non-clinical performance testing, not a human study, and therefore terms like "retrospective or prospective" are not directly applicable in the typical sense for clinical trials.

3. Number of Experts Used to Establish Ground Truth and Qualifications:
Not applicable. This device is a physical product (surgical gowns) and its performance is evaluated through standardized laboratory tests, not by human expert assessment of diagnostic outputs.

4. Adjudication Method for the Test Set:
Not applicable. Performance is determined by objective measurements according to recognized standards (e.g., AATCC 42, AATCC 127, ASTM D5034, ASTM D5733, 16 CFR Part 1610, AAMI PB70, ISO 10993).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:
Not applicable. This is not an AI-assisted diagnostic device.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Not applicable. This is a physical product, not an algorithm. The reported performance relates to the gown's material properties and barrier protection.

7. The Type of Ground Truth Used:
The "ground truth" for the surgical gowns' performance is established by the results of standardized laboratory testing (e.g., measurements of impact penetration, hydrostatic resistance, tensile strength, tear resistance, flame spread, and biocompatibility assays). These tests provide objective values that are compared against predefined criteria from recognized industry standards (e.g., AAMI PB70, ASTM, AATCC, ISO standards).

8. The Sample Size for the Training Set:
Not applicable. This is a manufactured product, not an AI/ML model that requires a training set.

9. How the Ground Truth for the Training Set Was Established:
Not applicable. No training set is involved for this type of device.

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Surgical gown are devices that are intended to be worn by operating room personnel during surgical procedures to protect both the surgical patient and the operating room personnel from transfer of microorganisms, body fluids, and particulate material.

The surgical gown is intended to be worn by operating room personnel during surgical procedures to protect both the surgical patient and the operating room personnel from transfer of microorganisms, body fluids, and particulate material. It is made of soft, air permeable SMS non-woven fabric. The Jianerkang Surgical Gown is made of a laminate with adhesive taped seams and have a hook and loop closure at the back of the neck and a waist tie feature to secure the gown to the body of the user. The sleeves of the gown have knit cuffs sewn onto the end of the sleeve at the user's wrists to keep the sleeves in place on the wearer. The entire gown including the gown sleeves are made of the same material and utilize the same manufacturing processes.

The provided text is a 510(k) summary for a medical device called "Surgical Gown." It details the product, its intended use, and its comparison to a predicate device to demonstrate substantial equivalence for FDA clearance.

However, the questions you've asked are typically relevant to the performance evaluation of AI/ML-enabled medical devices, specifically regarding their accuracy and how they improve human performance. The document describes a traditional medical device (a surgical gown) and focuses on its physical properties, material performance, and biocompatibility, not on AI/ML algorithm performance.

Therefore, I cannot provide answers to your specific questions based on the provided text, as it does not contain information about:

• Acceptance criteria for an AI/ML algorithm's performance
• Study data for an AI/ML algorithm
• Sample sizes for test or training sets for AI/ML
• Ground truth establishment by experts for AI/ML
• MRMC studies for AI/ML
• Standalone AI algorithm performance

The document states that "Performance testing was conducted on the Surgical Gown. All of the tested parameters met the predefined acceptance criteria," and then lists those criteria in a table for properties like water resistance, tearing strength, tensile strength, and seam strength. This is a physical product, not a diagnostic or prognostic AI/ML device.

To answer your request, if this were an AI/ML device, the structure of the answer below demonstrates what would be needed, but the actual values and details cannot be extracted from the provided text.

Assuming the Request Pertains to a Hypothetical AI/ML-enabled Medical Device based on the Questions Provided:

Given the nature of the provided document, which describes a surgical gown (a physical medical device), it does not contain the kind of information requested regarding acceptance criteria and studies for an AI/ML-enabled software device. The acceptance criteria and performance studies described in the document relate to the physical properties and barrier performance of the gown itself, not to an algorithm's diagnostic or analytical capabilities.

Therefore, for the type of device described in the document, none of the following information is applicable or available:

• A table of acceptance criteria and reported device performance for an AI/ML algorithm
• Sample sizes used for AI/ML test sets or training sets
• Data provenance for AI/ML
• Number of experts used to establish ground truth for AI/ML
• Adjudication method for AI/ML
• MRMC comparative effectiveness study results for AI/ML
• Standalone AI algorithm performance
• Type of ground truth used for AI/ML (e.g., expert consensus, pathology, outcomes data)
• How ground truth for AI/ML training was established

However, if we were to reinterpret your request as an example of what such a description would entail for an AI/ML device, even though not present in this document, here's a hypothetical structure that addresses your points:

Hypothetical Description for an AI/ML Medical Device (Not Applicable to the Provided Document)

This section describes the hypothetical acceptance criteria and the study conducted to prove the AI-enabled device meets these criteria, as if the "Surgical Gown" were an AI/ML diagnostic tool.

1. Table of Acceptance Criteria and Reported Device Performance (Hypothetical AI/ML Device)

2. Sample Sizes and Data Provenance (Hypothetical AI/ML Device)

• Test Set Sample Size: N = 1,000 cases (e.g., medical images, patient records).
• Data Provenance: Retrospective and prospective data collected from multiple sites across the United States, Europe (e.g., Germany, UK), and Asia (e.g., Japan, South Korea). Data was collected over a period of 5 years (2018-2023).

3. Number of Experts and Qualifications for Ground Truth (Hypothetical AI/ML Device)

• Number of Experts: A panel of 5 board-certified medical specialists (e.g., Radiologists, Pathologists) with varying levels of experience.
• Qualifications:
  • 3 Senior Experts: Each with >10 years of experience in the relevant subspecialty (e.g., thoracic radiology, dermatopathology).
  • 2 Junior Experts: Each with 3-5 years of experience in the relevant subspecialty.
  • All experts were blinded to the device's output during ground truth establishment.

4. Adjudication Method for the Test Set (Hypothetical AI/ML Device)

• Method: A "3+1" consensus method was used.
  • Each case was independently reviewed by three out of the five experts.
  • If at least two out of the three experts agreed on a label, that was considered the preliminary ground truth.
  • In cases of disagreement (i.e., no two out of three experts agreed, or a 1-1-1 split), a fourth senior expert (the "plus 1") was brought in to review the case and resolve the discrepancy, establishing the final ground truth.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study (Hypothetical AI/ML Device)

• Was an MRMC study done? Yes.
• Effect Size of Human Reader Improvement: The MRMC study demonstrated a statistically significant improvement in human reader performance when assisted by the AI device.
  • Effect Size: Human readers improved their mean sensitivity by 7.2% (absolute change) and mean specificity by 4.5% (absolute change) when using the AI assistance compared to reading without AI assistance.
  • This translated to a statistically significant increase in AUC for human readers with AI assistance (e.g., AUC increased from 0.85 to 0.91, p

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