(161 days)
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)
| Metric | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Primary Endpoints: | ||
| Sensitivity (for Condition X) | ≥ 90% (lower 95% CI > 85%) | 92.5% (95% CI: 90.1% - 94.4%) |
| Specificity (for Condition X) | ≥ 80% (lower 95% CI > 75%) | 83.2% (95% CI: 80.5% - 85.7%) |
| Secondary Endpoints: | ||
| Area Under ROC Curve (AUC) | ≥ 0.90 | 0.93 |
| Positive Predictive Value (PPV) | ≥ 75% | 78.1% |
| Negative Predictive Value (NPV) | ≥ 95% | 96.3% |
| Human Reader Performance Gain (MRMC) | Mean sensitivity improvement ≥ 5% with AI assistance | Mean sensitivity improvement of 7.2% |
| Time-to-diagnosis | Reduction of 20% | 25% reduction in mean diagnostic reading time |
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
§ 878.4040 Surgical apparel.
(a)
Identification. Surgical apparel 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. Examples include surgical caps, hoods, masks, gowns, operating room shoes and shoe covers, and isolation masks and gowns. Surgical suits and dresses, commonly known as scrub suits, are excluded.(b)
Classification. (1) Class II (special controls) for surgical gowns and surgical masks. A surgical N95 respirator or N95 filtering facepiece respirator is not exempt if it is intended to prevent specific diseases or infections, or it is labeled or otherwise represented as filtering surgical smoke or plumes, filtering specific amounts of viruses or bacteria, reducing the amount of and/or killing viruses, bacteria, or fungi, or affecting allergenicity, or it contains coating technologies unrelated to filtration (e.g., to reduce and or kill microorganisms). Surgical N95 respirators and N95 filtering facepiece respirators are exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to § 878.9, and the following conditions for exemption:(i) The user contacting components of the device must be demonstrated to be biocompatible.
(ii) Analysis and nonclinical testing must:
(A) Characterize flammability and be demonstrated to be appropriate for the intended environment of use; and
(B) Demonstrate the ability of the device to resist penetration by fluids, such as blood and body fluids, at a velocity consistent with the intended use of the device.
(iii) NIOSH approved under its regulation.
(2) Class I (general controls) for surgical apparel other than surgical gowns and surgical masks. The class I device is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to § 878.9.