Search Results

ABCcolla® Collagen ADM Scaffold is intended to be used for management of wounds. including venous ulcers, pressure ulcers, chronic vascular ulcers, diabetic ulcers, tunneled undermined wounds (donor site/ grafts, post-Moh's surgery, post-laser surgery, podiatric, wound dehiscence), trauma wounds (abrasions, and skin tears), first and second-degree burns, draining wounds.

ABCcolla® Collagen ADM Scaffold is a decellularized porcine collagen biomaterial from porcine dermis. When applied on a wound, this product helps absorb wound exudates and maintain a moist wound environment.

This document is a 510(k) premarket notification for the ABCcolla® Collagen ADM Scaffold, a medical device for wound management. The core of the submission is to demonstrate the substantial equivalence of the new device to a legally marketed predicate device, ABCcolla® Collagen Matrix (K162348), and a reference device, Cook® ECM Powder (K152033).

Based on the provided text, the "acceptance criteria" and the "study that proves the device meets the acceptance criteria" are not related to an AI/ML-driven device's performance in terms of diagnostic accuracy or a clinical study in humans with a traditional statistical endpoint and acceptance criteria. Instead, the "acceptance criteria" for this specific device (a collagen scaffold for wound management) are primarily focused on benchmarking against a predicate device to demonstrate "substantial equivalence" as required by the FDA 510(k) pathway. The "study" here refers to the pre-clinical testing and characterization that demonstrates the new device has similar technological characteristics and performance to the predicate device, and any differences do not raise new questions of safety or effectiveness.

Here's an interpretation of the requested information based on the provided document:

1. A table of acceptance criteria and the reported device performance

For a 510(k) submission, "acceptance criteria" are not typically framed as specific performance metrics and thresholds like sensitivity/specificity for an AI device. Instead, the acceptance is based on demonstrating that the new device is "substantially equivalent" to a legally marketed predicate device. This is achieved by comparing various characteristics.

Here's a table based on the "Substantial Equivalence Comparison Table" in the document, interpreting "acceptance criteria" as demonstrating "sameness" or "differences that do not raise new questions of safety/effectiveness" compared to the predicate/reference devices:

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document mentions one performance study: "A study was performed where the ABCcolla® Collagen ADM Performance Data Scaffold is performed in full thickness skin defect rat model."

• Sample size: Not specified. It only mentions "rat model," not the number of rats.
• Data provenance: Not specified regarding country of origin. This is a pre-clinical animal study, not human data.
• Retrospective or prospective: This would be a prospective animal study as it describes an experiment being "performed."

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)

This section is not applicable as the provided document does not describe a clinical study in humans or an AI/ML device relying on expert-adjudicated ground truth. The "study" mentioned is an animal model for safety/performance, not human diagnostic interpretation.

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

This is not applicable. There is no human expert adjudication involved for the "full thickness skin defect rat model."

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

This is not applicable. The device is a collagen scaffold for wound management, not an AI/ML-driven diagnostic or assistive technology for human readers. No MRMC study was conducted or mentioned.

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

This is not applicable. The device is a physical medical device (collagen scaffold), not an algorithm or software. It does not have a "standalone" performance in the context of AI/ML.

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

For the mentioned performance data (rat model):

• The "ground truth" would likely be based on histopathology and/or gross observation/measurements of wound healing in the rat model, as is typical for such pre-clinical studies. The document states "The results of the study show that device is safe in its application," implying direct observation and measurement of tissue response.

8. The sample size for the training set

This is not applicable. There is no "training set" as this is not an AI/ML device.

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

This is not applicable. There is no "training set."

In summary: The provided document is an FDA 510(k) clearance letter for a biological medical device (collagen scaffold), not an AI/ML device. Therefore, many of the requested points related to AI/ML evaluation (like expert ground truth, MRMC studies, training/test sets, etc.) are not relevant to this type of submission. The "acceptance criteria" are demonstrating substantial equivalence primarily through comparison of materials, design, intended use, and pre-clinical biocompatibility and safety testing against a predicate device.

Ask a specific question about this device

ABCcolla® Bone Matrix is intended to fill bony voids or gaps of skeletal system (i.e., extremities and pelvis). These osseous defects are surgically created or the result of traumatic injury to the bone and are not intrinsic to the stability of bone structure. ABCcolla® Bone Matrix provides resorbs and is replaced by bone during the healing process.

ABCcolla® Bone Matrix is a bone mineral matrix of porcine origin. ABCcolla® Bone Matrx is physically and chemically comparable to the mineralized matrix of human bone.

This document (K212156) is a 510(k) premarket notification for a medical device called "ABCcolla® Bone Matrix." It concerns a resorbable calcium salt bone void filler.

Crucially, this document does not contain information about acceptance criteria or a study proving the device meets those criteria in the context of an AI/ML medical device. The document is for a traditional medical device (bone matrix), not an AI/ML driven device.

Therefore, I cannot fulfill your request for information regarding acceptance criteria and study data for an AI/ML device based on the provided text. The document focuses on demonstrating substantial equivalence to a predicate device based on material characteristics, manufacturing processes, and physical specifications.

To answer your request, I would need a document related to an AI/ML medical device and its validation study.

Ask a specific question about this device

ABCcolla® Bone Graft is an implant intended to fill bony voids or gaps of the skeletal system (i.e., extremities and pelvis). These osseous defects are surgically created or the result of traumatic injury to the bone and are not intrinsic to the stability of the bony structure. ABCcolla® Bone Graft resorbs and is replaced with bone during the healing process.

ABCcolla® Bone Graft is a bone mineral matrix of porcine origin. ABCcolla® Bone Graft is physically and chemically comparable to the mineralized matrix of human bone.

The provided text describes a 510(k) premarket notification for the "ABCcolla® Bone Graft" device, which is a resorbable calcium salt bone void filler. Here's an analysis of the acceptance criteria and study information, focusing on the lack of specific details regarding a "study that proves the device meets acceptance criteria" as would be expected for an AI/ML device:

Important Note: The provided document is a 510(k) summary for a bone graft medical device, not an AI/ML powered device. As such, concepts like "test set," "human readers," "AI assistance," "ground truth establishment for training set," and "adjudication method" are not applicable in their typical AI/ML context. The document describes traditional medical device testing for substantial equivalence, focusing on biocompatibility, physical properties, and pre-clinical animal performance.

Therefore, many of the requested fields will be marked as "Not Applicable" or "Not Provided" in the context of an AI/ML device. I will extract the relevant information from the document as it pertains to a traditional medical device's testing and acceptance.

Acceptance Criteria and Reported Device Performance

The acceptance criteria for the ABCcolla® Bone Graft are implicitly set by demonstrating "substantial equivalence" to predicate devices and meeting established medical device standards. The "performance" in this context refers to its physical, chemical, and biological properties, as well as its ability to facilitate bone formation in animal models.

Study Details (Interpreted for a non-AI/ML medical device)

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size:
     • For chemical, physical, and in vitro biological tests: Not explicitly stated in terms of sample count (e.g., number of batches, units). These would typically involve multiple samples to establish consistency.
     • For pre-clinical animal performance testing: A "critical-size bone defect model" was performed. The number of animals used is not specified.
   • Data Provenance: The studies were performed by ACRO Biomedical Co., Ltd. (Taiwan). The document does not specify if the studies were retrospective or prospective, but animal studies are typically prospective by nature.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not Applicable / Not Provided. For a bone graft device, "ground truth" is established through standardized laboratory analyses (e.g., chemical assays, physical measurements) and histological examination of animal tissue, performed by qualified scientists and pathologists, rather than expert clinicians rendering a diagnostic opinion. The number of such specialists is not specified.

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

   • Not Applicable. Adjudication methods like 2+1 are relevant for interpreting ambiguous diagnostic images or clinical scenarios, typically in studies involving human readers or AI. For the described medical device testing, results are derived from objective measurements, biochemical analyses, and histological assessments in animal models.

4. 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 device is a bone graft, not an AI-powered diagnostic tool. Therefore, no MRMC study or AI assistance comparison would be performed.

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

   • Not Applicable. This is a physical medical implant, not an algorithm.

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

   • For chemical and physical properties: Analytical measurements against established standards (e.g., hydroxyapatite content, porosity).
   • For biocompatibility: Adherence to ISO 10993 series standards, which involves evaluating cellular responses, systemic toxicity, etc.
   • For pre-clinical animal performance: Histological evidence of new bone formation and healing in the critical-size bone defect model, likely compared to control groups or expected healing patterns. This would involve pathology and quantitative analysis of bone growth.

7. The sample size for the training set:

   • Not Applicable. This is a physical medical device, not an AI/ML model, so there is no "training set."

8. How the ground truth for the training set was established:

   • Not Applicable. As there is no training set for an AI/ML model, this question is irrelevant for this device.

Ask a specific question about this device