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The Conventus DRS™ is intended for use in the fixation of fractures of the distal radius.

The Conventus DRSTM is an intramedullary device intended to treat distal radius fractures. The device remains flexible during placement, but is made rigid at the completion of the surgical implant procedure. The implant is made from titanium alloy (Ti-6Al-4V) and Nitinol.

The provided document describes a 510(k) premarket notification for the Conventus DRS™ device, which is an intramedullary device for fixing distal radius fractures. The document is a regulatory submission and focuses on demonstrating substantial equivalence to legally marketed predicate devices, rather than a clinical study evaluating the performance of an AI algorithm or diagnostic tool.

Therefore, many of the requested elements are not applicable to this type of submission.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

Note: The document does not explicitly state "acceptance criteria" for radiographic success or DASH scores in quantitative terms (e.g., "X% of subjects must achieve radiographic success"). Instead, it reports the "demonstrated" performance. For adverse events, the reporting of specific numbers implies a monitoring and reporting requirement rather than a pass/fail criterion.

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

• Sample Size (Test Set): Not explicitly stated as a separate "test set" from a larger training set. The clinical evaluation refers to "study subjects," implying this cohort serves as the primary evaluation set for substantial equivalence. The exact number of subjects is not provided.
• Data Provenance: Clinical evaluation was conducted "outside the United States in Tier 1 countries."
• Retrospective or Prospective: Not explicitly stated, but the mention of "collection of radiographic outcomes, functional outcomes (DASH), and adverse event information" suggests a prospective clinical trial or observation period following implantation of the device.

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

• This information is not provided in the document. The document refers to "radiographic outcomes" being collected, but it does not specify how these outcomes were interpreted or if expert consensus was used to establish ground truth for the fracture classification or healing assessment.

4. Adjudication method for the test set:

• This information is not provided in the document.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and if so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• An MRMC study was not conducted. This document describes a medical device (intramedullary fixator), not an AI algorithm or diagnostic tool that assists human readers.

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

• A standalone performance assessment was not done. This is a physical medical device, not an algorithm.

7. The type of ground truth used:

• The "ground truth" for the device's performance is implicitly based on clinical outcomes data, including:
  • Radiographic outcomes: Assessment of fracture reduction and healing.
  • Functional outcomes: Measured by the DASH (Disabilities of the Arm, Shoulder and Hand) score.
  • Adverse event information: Documented complications.

8. The sample size for the training set:

• The concept of a "training set" in the context of an AI algorithm is not applicable here. This document describes a physical medical device. The "clinical evaluation" described would be the primary data for demonstrating safety and effectiveness relative to predicates.

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

• As mentioned above, the concept of a "training set" for an AI algorithm is not applicable. The ground truth for the clinical evaluation was established through direct clinical observation, radiographic assessment, and patient-reported outcomes.

In summary:

This 510(k) summary focuses on demonstrating the substantial equivalence of a bone fixation device to existing predicate devices. It relies on a clinical evaluation (implicitly a prospective study) to gather data on radiographic and functional outcomes, as well as adverse events. The submission does not involve AI, diagnostic image interpretation, or the associated methodologies for establishing ground truth for algorithms.

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The ENSPLINTRx™ Distal Radius System is intended to be used for the fixation of unstable distal radius fractures in which closed reduction is not suitable:

• Joint destruction and/or subluxation visible on x-ray; .
• Osteotomy and repair of distal radius malunion with or without bone graft; .
• Non-displaced fractures in which the physician and patient opt for rigid stability .
• Transverse fractures of the distal radius with or without comminution (e.g. AO . classifications A2 and A3)
• Transverse fractures of the distal radius with an extension into the joint without . comminution (e.g. AO classification C1);
• Failed fracture fixation with or without bone graft for the types of fractures above; .
• The above types of fractures (i.e. AO classifications non-displaced transverse, A2, . A3, and C1) in which reduction has been lost following fixation with percutaneous pins with or without an external fixator.

The EnsplintRx™ configuration consists of a flexible implant manufactured from stainless steel.

Here's a breakdown of the acceptance criteria and study information for the ENSPLINTRx Distal Radius System, based on the provided 510(k) summary:

This device is not an AI/ML device, so many of the requested fields (e.g., sample size for test set, data provenance, number of experts, adjudication method, MRMC study, standalone performance) are not applicable. The device is a traditional medical device, relying on bench testing for substantial equivalence.

1. Table of Acceptance Criteria and 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 device is a traditional implantable medical device, and its performance was demonstrated through non-clinical (bench-top) laboratory testing, not a clinical study involving human test sets or data provenance.

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: As this was non-clinical bench testing, there was no human "test set" and thus no need for experts to establish ground truth in the context of clinical interpretation or diagnosis.

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

• Not Applicable: There was no clinical test set requiring adjudication.

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 an AI/ML device, so no MRMC study was conducted.

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

• Not Applicable: This is not an AI/ML device, so standalone algorithm performance is not relevant.

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

• Engineer-defined performance metrics through bench testing: The "ground truth" for this device's performance was established by engineering specifications and comparative mechanical testing against predicate devices. This involves measuring various mechanical properties (e.g., strength, stiffness, fatigue life) under controlled laboratory conditions, rather than clinical outcomes or diagnostic accuracy.

8. The sample size for the training set

• Not Applicable: This is not an AI/ML device, so there is no training set in the context of machine learning. The "training" for the device development would refer to engineering design, materials science, and manufacturing processes.

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

• Not Applicable: As above, this is not an AI/ML device. The "ground truth" for the device's design and manufacturing (analogous to a training set for an AI) would be established through established engineering principles, material science standards, and regulatory requirements, informed by the performance of the predicate devices.

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