(338 days)
Coreleader Hemo-Pad is a dressing indicated for topical wound management and for the external topical temporary control of moderate to severe bleeding. The dressing is indicated for the following wounds: abrasions, lacerations, skin surface puncture sites for vascular procedures (arteries and veins)
The Coreleader Hemo-Fiber wound dressing is made from poly-D-glucosamine and poly-N-acetylglucosamine derived from chitosan. The Coreleader Hemo-Fiber is a soft, non-woven topical pad for hemostasis and wound care. The natural biological property of this material carries cation (positively charged ion) that helps to stop external hemorrhage, and the Coreleader Hemo-Fiber wound dressing absorbs the wound exudates to form a hydrogel while providing protection layer layer for an optimal wound-healing environment. Coreleader Hemo-Pad is a sterile topical hemostasis pad, packed in a foil pouch and sterilized by gamma-ray radiation to a 10to SAL.
This is a 510(k) summary for a medical device called "Coreleader Hemo-Pad." 510(k) submissions, particularly older ones, often focus on substantial equivalence to a predicate device rather than presenting detailed standalone performance studies with specific acceptance criteria in the manner one might find for a novel AI/software medical device.
Based on the provided document, here's an attempt to answer your questions, with the caveat that detailed performance study methodology and acceptance criteria as you've outlined for AI/software devices are largely absent from this type of regulatory submission for a physical wound dressing:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state quantitative acceptance criteria or detailed reported device performance in terms of metrics like sensitivity, specificity, accuracy, etc., which are common for AI/software-based devices. Its focus is on establishing substantial equivalence to predicate devices (CLO-SURPLUS P.A.D., ChitoSeal, T-PAD) based on intended use, technological characteristics, and safety/effectiveness.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document does not mention any specific "test set" in the context of a performance study like those for AI/software devices. There is no information provided regarding the sample size of any study, data provenance (country of origin), or whether any data used was retrospective or prospective.
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)
No information is provided regarding the use of experts to establish a "ground truth" for a test set. This type of review is not typically part of a 510(k) for a physical wound dressing.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
No information is provided regarding an adjudication method, as no "test set" and ground truth establishment process are 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 such study was done or is referenced. This type of study (MRMC, human readers, AI assistance) is completely irrelevant to a physical wound dressing like the Coreleader Hemo-Pad.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
A standalone performance study for an algorithm is not applicable, as this is a physical medical device (a wound dressing), not an algorithm or AI.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The concept of "ground truth" as relevant to AI/software performance evaluation is not applicable here. The safety and effectiveness of the device are implied by its substantial equivalence to legally marketed predicate devices, which would have undergone their own prior evaluations (e.g., biocompatibility testing, perhaps some animal or limited human trials demonstrating hemostasis, but not in the detailed statistical performance metrics of current AI studies). The document refers to "safety and use of chitosan [being] published by researchers over a period of decades," implying reliance on existing scientific literature rather than a new ground truth establishment for this specific device.
8. The sample size for the training set
No "training set" is mentioned or applicable, as this is not an AI/machine learning device.
9. How the ground truth for the training set was established
Not applicable as there is no training set and no AI/machine learning component.
Summary regarding the 510(k) submission and your questions:
The provided document is a 510(k) summary for a physical medical device (wound dressing) and is focused on demonstrating substantial equivalence to existing predicate devices. The framework you've provided for evaluating acceptance criteria and study designs is highly relevant to AI/Machine Learning-based medical devices and software, which require rigorous statistical performance analysis and often human reader studies.
For a physical device like the Coreleader Hemo-Pad, the "proof" often lies in:
- Bench testing: Demonstrating material properties, absorption, sterility, biocompatibility.
- Performance testing: Potentially in-vitro or in-vivo (animal) models to show hemostatic capabilities (e.g., time to hemostasis, blood loss reduction), but these are usually not detailed with specific "acceptance criteria" in the same way as an AI's diagnostic performance.
- Comparisons to predicate devices: Showing that the new device has similar technological characteristics and performs comparably to devices already on the market that have been deemed safe and effective.
- Scientific literature: Referencing established knowledge about the materials used (chitosan in this case) to support claims of biocompatibility, biodegradability, hemostatic, and anti-infectional activity.
The 510(k) summary provided does not contain the level of detail regarding performance studies, sample sizes, expert ground truth, or adjudication methods that would be expected for an AI/software device.
N/A