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The MONDRIAN Lumbar Plate System is indicated as additional support during fusion in the treatment of lumbar and lumbosacral (L1-S1) spine instability as a result of fracture (including dislocation and subluxation), tumor, degenerative disc disease (defined as back pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies), scoliosis, kyphosis, lordosis, spinal stenosis, or a failed previous spine surgery.

The MONDRIAN Anterior Lumbar Plate System is anterior lumbar fusion devices used to provide structural stability in skeletally mature individuals. The system consists of plates, screws, and instruments to facilitate the installation of the implants. Plates and screws are manufactured from Titanium Alloy per ASTM F136. The instruments are manufactured from Stainless Steel per ASTM F899. Implants and instruments will be provided in non-sterile configuration and will require steam sterilization prior to use.

Based on the provided FDA 510(k) Clearance Letter for the MONDRIAN™ Anterior Lumbar Plate System, here's an analysis of the acceptance criteria and the study that proves the device meets them:

Important Note: The provided document is a 510(k) clearance letter, which focuses on demonstrating substantial equivalence to a legally marketed predicate device, primarily through non-clinical performance data and comparison of technological characteristics. It is not a detailed study report for a novel medical device, especially an AI-powered one. Therefore, many of the requested items related to AI device performance evaluation (such as sample sizes for test sets, expert ground truth, MRMC studies, standalone performance, and training set details) are not applicable or present in this type of document because the device in question is a physical surgical implant, not an AI/software device.

Acceptance Criteria and Reported Device Performance

Explanation: The "acceptance criteria" for a physical implant device like the MONDRIAN™ Anterior Lumbar Plate System are primarily related to its mechanical strength, durability, material safety (biocompatibility), and functionality under simulated physiological loads. The reported device performance is that the non-clinical tests showed the device meets these requirements and performs within its intended uses, indicating no new safety or effectiveness concerns compared to the predicate device. Specific numerical acceptance thresholds are not detailed in this high-level summary but would be part of the full submission.

Study Details (Focusing on the provided document's context)

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

   • Sample Size: Not explicitly stated as a "sample size" in the context of clinical data or AI test sets, as this is a non-clinical bench testing submission for a physical implant. The "test set" for this device refers to the number of implants or test specimens subjected to the various mechanical tests. This information is typically found in the test reports themselves, not the 510(k) summary.
   • Data Provenance: Not applicable in the context of country of origin for clinical data, as no clinical studies were performed ("Clinical Tests: N/A"). The non-clinical tests would have been performed in a laboratory setting, likely within the manufacturing entity's or a contract testing facility's country.

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

   • Not Applicable. This question typically pertains to studies involving human interpretation (e.g., radiologists, pathologists) to establish ground truth for AI algorithms. For a physical implant, "ground truth" is established by engineering specifications, material standards, and validated mechanical testing methodologies.

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

   • Not Applicable. Adjudication methods are used to resolve disagreements among human experts in establishing ground truth for clinical studies or AI performance evaluation. For mechanical testing, results are objective measurements against predefined engineering criteria, eliminating the need for adjudication.

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:

   • No. An MRMC study is not relevant here as the MONDRIAN™ Anterior Lumbar Plate System is a physical surgical implant, not an AI software device intended to assist human readers.

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

   • No. This refers to the performance of an AI algorithm alone. The MONDRIAN™ Anterior Lumbar Plate System is a physical medical device; thus, this type of standalone performance evaluation is not relevant.

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

   • Engineering Specifications and Material Standards: For this device, the "ground truth" for evaluating its performance comes from established engineering principles, material science (e.g., ASTM F136 and ASTM F899 standards for Titanium Alloy and Stainless Steel), and validated mechanical testing protocols. The device's ability to withstand specified loads (static and dynamic) without failure or excessive deformation, and its material compliance, constitute the 'ground truth' it must meet.

7. The sample size for the training set:

   • Not Applicable. There is no "training set" in the context of machine learning or AI for this physical medical device.

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

   • Not Applicable. As there is no training set for this physical device, the concept of establishing ground truth for it does not apply.

In summary, the provided document details the 510(k) clearance for a physical implant device. The "study" proving it meets acceptance criteria consists of non-clinical mechanical bench testing demonstrating its equivalence in performance and characteristics to legally marketed predicate devices, rather than clinical trials or AI performance evaluations.

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