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NanOss Bone Void Filler is intended only for bony voids or gaps that are not intrinsic to the stability of the bony structure. The product is indicated to be gently packed into bony voids or gaps of the skeletal system (i.e., the extremities, spine and pelvis). These defects may be surgically created osseous defects or osseous defects created from traumatic injury to the bone. The product provides a bone void filler that resorbs and is replaced by the growth of new bone during the healing process.

NanOss Bone Void Filler is an osteoconductive, resorbable, calcium phosphate implant for use as a bone graft substitute or bone void filler. The device consists of prefabricated cylindrical calcium phosphate pellets. The device is radiopaque and is presterilized for single use. The nanocrystalline processing of the material results in sintered hydroxyapatite pellets that are translucent and uniform in density and strength.

This 510(k) premarket notification for the Angstrom Medica NanOss™ Bone Void Filler does not contain the detailed study information typically required to describe acceptance criteria and device performance in the context of AI/ML or diagnostic devices. Instead, it is a submission for a bone void filler product, which is a medical device, but not one that involves software algorithms, AI, or diagnostic performance metrics like sensitivity, specificity, or accuracy.

Therefore, many of the requested categories (e.g., sample size for test set, number of experts, adjudication method, MRMC studies, standalone performance, training set details) are not applicable to this type of device submission as they pertain to performance evaluation of diagnostic or AI-driven systems.

However, based on the provided document, I can extract information relevant to the device's equivalence to marketed products, which serves as the "proof" for this type of device, rather than a performance study against acceptance criteria in the AI/ML sense.

Here's a breakdown based on the information available:

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

For this type of device, "acceptance criteria" are not reported as specific performance metrics and thresholds (like sensitivity > X%). Instead, the acceptance criteria are met by demonstrating substantial equivalence to predicate devices. The "reported device performance" is essentially that it is chemically, functionally, and indication-wise similar to already cleared devices.

2. Sample sized used for the test set and the data provenance

• Not Applicable. This submission relies on demonstrating equivalence to predicate devices, not on a "test set" in the context of AI/ML performance evaluation studies. Chemical and physical properties of the NanOss pellets would have been tested, but these are not clinical "test sets" of data as understood in AI/ML.
• Data Provenance: The document doesn't specify an external "data provenance" for performance testing, as the "proof" is largely based on internal material characterization and comparison to established predicate properties.

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

• Not Applicable. No ground truth in the AI/ML sense (e.g., expert consensus on images or pathology results) was established for a "test set." The basis for clearance is material science and clinical equivalence to existing products.

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

• Not Applicable. No adjudication method for a "test set" was used as described for AI/ML performance studies.

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

• Not Applicable. This is not a diagnostic device involving human readers or AI assistance.

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

• Not Applicable. This is a physical bone void filler, not an algorithm.

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

• For material properties and claims of equivalence: The "ground truth" is established based on standard material characterization techniques (e.g., chemical analysis, density measurements, dissolution studies) and comparison to the known properties and regulatory clearances of the predicate devices.
• For clinical benefit: The "ground truth" is that similar calcium phosphate/sulfate bone void fillers are already cleared and used safely and effectively in clinical practice, and this new device functions equivalently.

8. The sample size for the training set

• Not Applicable. No training set for an AI/ML algorithm was used.

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

• Not Applicable. No training set for an AI/ML algorithm was used.

Summary of the "Study" that Proves Acceptance Criteria are Met:

The "study" in this context is the demonstration of substantial equivalence to legally marketed predicate devices. The manufacturer, Angstrom Medica, Inc., submitted evidence to the FDA showing that their NanOss™ Bone Void Filler shares the same intended use, design principles, and functional characteristics as several already cleared bone void fillers.

Key elements of this "study" (as implied by the document):

• Comparison to Predicate Devices: NanOss was compared to:
  • Biomet Calcium Phosphate Granular Bone Void Filler (K011531)
  • NovaBone Resorbable Bone Graft Substitute (K021336)
  • Wright Medical Technology Osteoset Resorbable Mini Bead Kit (K010532)
  • Orthovita Vitoss Scaffold (K994337)
  • Interpore Pro Osteon 500R (K990131)
• Material Characterization: Evidence was presented that NanOss pellets are:
  • Chemically similar to calcium phosphate predicates.
  • Exhibit expected phase purity and calcium/phosphorus stoichiometry.
  • Have trace element levels below ASTM F1185 limits.
  • Possess a density approaching theoretical density for ceramic hydroxyapatite.
  • Have a bulk density in the expected range for small cylindrical pellets.
  • Demonstrate dissolution behavior within the range of predicate devices.

The FDA's letter (K050025) confirms that, based on this submission, the device was determined to be "substantially equivalent" to the predicate devices for the stated indications for use, thereby meeting the regulatory acceptance criteria for market clearance.

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MeDiCa Indirect Immunofluorescence (IIF) Anti- Endomysial Antibody Test Kit is used for the qualitative and semi- quantitative detection of IgA class endomysial autoantibodies (AEmA) in human serum as an aid in the diagnosis of Celiac Disease and other related disorders such as Dermatitis Herpetiformis.

MeDiCa Indirect Immunofluorescence (IIF) Anti- Endomysial Antibody Test Kit is used for the qualitative and semiquantitative detection of IgA class endomysial autoantibodies (AEmA) in human serum as an aid in the diagnosis of Celiac Disease and other related disorders such as Dermatitis Herpetiformis.

In general, MeDiCa and Scimedx Anti- Endomysial Antibody Test Kits have the following similarities:

• a) intended use
• b) methodology- indirect immunofluorescence method
• c) test kit composition
• d) type of substrate

MeDiCa and Scimedx test kits differ in the following features:
a) volume of conjugate and controls provided by MeDiCa is . about 50-65% less than Scimedx test kit.
b) presentation of substrate on slide wells. Both MeDiCa and Scimedx provide the same type of substrate. The only difference is that MeDiCa monkey esophaqus distal substrate sections are cut sequentially from one and the same block. They are deposited individually one after another and oriented the same way in all wells of each slide. The sections for Scimedx are cut from four different blocks at the same time, resulting in different orientation of sections from well to well.

The provided text describes a 510(k) pre-market notification for a medical device called the "MeDiCa Indirect Immunofluorescence (IIF) Anti-Endomysial Antibody (AEmA) Test Kit." The purpose of this submission is to demonstrate substantial equivalence to a predicate device, the "Scimedx Anti-Endomysial IgA (EMA) Test System."

Here's an analysis of the acceptance criteria and the study as described in the document:

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

The document does not explicitly state numerical acceptance criteria in the typical sense (e.g., "sensitivity must be >90%"). Instead, the acceptance criteria are implicitly defined by demonstrating the MeDiCa device "matches" or is "substantially equivalent" to the predicate Scimedx test kit in terms of key performance characteristics.

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 provide specific details on the sample size used for the performance evaluation, nor does it mention the data provenance (country of origin, retrospective/prospective). It only states that "Results of performance evaluation show that MeDiCa IIF Anti-Endomysial Antibody Test Kit matches Scimedx test kit."

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)

The document does not provide information on the number of experts used or their qualifications for establishing ground truth for the test set. Given the nature of the device (an in vitro diagnostic test kit), ground truth would likely be established through clinical diagnosis, possibly confirmed by other diagnostic methods or pathology, rather than expert interpretation of images. However, the details of this process are not included.

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

The document does not specify any adjudication method for the test set.

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 section is not applicable as the device is an in vitro diagnostic test kit for the detection of antibodies, not an AI-powered image analysis tool requiring human readers. Therefore, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not conducted or described.

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

This concept is not fully applicable to this device in the same way it would be for an AI algorithm. The device itself is a laboratory test kit that produces a result (qualitative or semi-quantitative detection of antibodies) which is then interpreted by a laboratory professional. The document describes the kit's performance ("matches Scimedx test kit" on specificity, sensitivity, etc.), which represents its standalone analytical performance. However, there isn't an "algorithm" in the typical AI sense. The "interpretation of fluorescent patterns" mentioned in the MeDiCa kit's advantages implies a human in the loop for reading the final result.

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

The document does not explicitly state the type of ground truth used. For a diagnostic test for Celiac Disease and Dermatitis Herpetiformis, ground truth would typically be established by a definitive clinical diagnosis, often involving biopsy (pathology) for Celiac Disease, and/or other established diagnostic criteria and patient outcomes.

8. The sample size for the training set

The document describes a 510(k) submission for a traditional in vitro diagnostic test kit, not an AI/machine learning device. Therefore, the concept of a "training set" for an algorithm is not applicable in the context of this submission. The performance evaluation was likely conducted on a test set of patient samples to demonstrate equivalence to the predicate device.

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

As explained in point 8, the concept of a "training set" is not applicable to this device.

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FlexScope is intended to be used for visualization of tissue in The general surgical applications.

The FlexScope™ is a flexible deflectable endoscope intended to allow physicians to visualize tissue in body cavities and through natural body orifices. The device is designed to be advanced to the site of interest through a cannula or trocar or may be used in an open procedure. The device will is available in various diameters.

The method of construction and materials used for these flexible fiber optic endoscopes are substantially equivalent to previous endoscope products.

This document describes a traditional medical device (an endoscope), not an AI/ML powered device. Therefore, the requested information regarding acceptance criteria, study details, ground truth, and AI/ML specific aspects (like multi-reader multi-case studies, standalone performance, training sets, etc.) is not applicable.

The provided text focuses on the physical characteristics, materials, and intended use of the FlexScope™ Fiber Optic Endoscope, comparing it to predicate devices to establish substantial equivalence.

Here's a breakdown of the relevant information from the provided text, recognizing the absence of AI/ML specific details:

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

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

Not applicable. This is a physical device, and the testing described is primarily engineering and material-based, not data-driven (like an AI/ML model). There's no "test set" in the context of data for an algorithm.

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)

Not applicable. Ground truth, in the context of AI/ML, refers to labels or diagnoses. For a physical endoscope, "ground truth" relates to engineering specifications and performance standards which are met through physical testing.

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

Not applicable. This refers to adjudication of expert opinions for ground truth in AI/ML studies.

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. This type of study is specifically for AI/ML algorithms and their impact on human reader performance. The FlexScope™ is a physical visualization tool.

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

Not applicable. This refers to the performance of an AI/ML algorithm by itself.

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

Not applicable in the AI/ML sense. The "ground truth" for the FlexScope™ would be objective engineering standards and observed physical performance during testing.

8. The sample size for the training set

Not applicable. There is no AI/ML model to train.

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

Not applicable. There is no AI/ML model to train.

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