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510(k) Data Aggregation
(172 days)
HANGZHOU ATEK MEDICAL & TEXTILE CO., LTD
Disposable gowns are worn by operating room personnel during surgical procedures to protect both the surgical patient and OR personnel from the transfer of body fluids and particulate material.
Gowns provided as sterile and non-sterile.
Non-sterile surgical gowns are to be sold to OEMs, which require EtO sterilization according to the ISO 11135 standard. Sterile surgical gowns are to be sold directly to users and must be sterilized by an EtO cycle validated to ISO 11135 standard.
Disposable Gowns manufactured from non-woven fabric. Various sizes and materials.
The provided document is a 510(k) Summary for a medical device, specifically surgical gowns. It outlines the device's classification, intended use, and comparison to predicate devices, along with a summary of performance data. However, this document does not describe a study involving an AI/Machine Learning (ML) device or its acceptance criteria.
The "device" in question is a physical medical device (surgical gowns), not an AI/ML algorithm. The performance data listed (flammability, lint, tensile strength, water resistance, etc.) are standard physical and material property tests for surgical gowns, not metrics typically associated with AI/ML performance like sensitivity, specificity, or AUC.
Therefore, I cannot provide the requested information about acceptance criteria for an AI/ML device, the study proving its adherence to those criteria, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, or standalone performance, because the submitted document pertains to a traditional medical device and not an AI/ML device.
The document details the following for the surgical gowns:
1. Table of Acceptance Criteria and Reported Device Performance:
The document doesn't explicitly present "acceptance criteria" in a pass/fail format within a single table. Instead, it lists various "Performance Testing" and "Performance Standards Used" which imply the criteria that the gowns must meet. The "Data Generated" column for each standard indicates the type of performance evaluated. The implicit acceptance criterion for this type of medical device (surgical gowns) is that they meet the specified performance standards for properties like water resistance, tensile strength, flammability, and biocompatibility, demonstrating substantial equivalence to predicate devices.
| Standard or Guidance Document | Data Generated |
|---|---|
| AAMI/ANSI/ISO 11135-1 2007 (Ethylene Oxide Sterilization) | EtO Sterilization Parameters |
| AAMI / ANSI / ISO 10993-7:1995 (EO Residual Determination) | EO Residuals |
| AAMI / ANSI / ISO 10993-1:2003(E) (Biological evaluation of medical devices) | Biocompatibility Testing Evaluation |
| AAMI / ANSI / ISO 10993-5: (1999 and 2009) (In Vitro cytotoxicity) | Cytotoxicity |
| AAMI / ANSI / ISO 10993-10:2002/Amd. 1:2006 (Irritation and sensitization) | Skin Irritation, intra-cutaneous reactivity & sensitization |
| AATCC Test Method 127-2008 (Water Resistance: Hydrostatic Pressure Test) | Hydrostatic Pressure - Water Resistance |
| AATCC Test Method 42-2007 (Water Resistance: Spray Impact Penetration Test) | Impact Penetration - Water Resistance |
| ASTM - D5034-2008 (Breaking Strength and Elongation of Textile Fabrics) | Tensile Strength |
| ASTM -- D5734-1995(2001) (Tearing Strength of Nonwoven Fabrics by Falling-Pendulum (Elmendorf) Apparatus) | Elmendorf Tear |
| ISO 9073-10:2003 (Textiles -- Test methods for nonwovens -- Part 10: Lint and other particles generation in the dry state) | Gelbo Flex - Lint |
| CPSC 16 CFR 1610-2004 (Standard for Flammability of Clothing Textiles) | Flammability |
Points 2-9 are not applicable as the document describes a physical medical device (surgical gowns), not an AI/ML device.
The document states, "The information in the Premarket Notification on safety and effectiveness supports a finding of substantial equivalence to devices already in commercial distribution. Equivalence is demonstrated through intended use, materials, design and testing methods." This indicates that the study involved performing tests according to the listed standards and comparing the results to those expected for predicate devices to establish substantial equivalence.
This submission is a 510(k) premarket notification for traditional medical devices (surgical gowns), which does not involve AI or machine learning. Therefore, the requested information related to AI/ML device performance, such as sample sizes for test/training sets, expert qualifications for ground truth establishment, adjudication methods, or MRMC studies, is not present or relevant in this document.
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