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MySpine is intended as a thoracic and lumbar posterior pedicle targeting quide for patients requiring spinal fusion between the levels of T1 to L5.

MySpine Screw Placement Guides are intended to be used as anatomical perforating guides specific for a single patient anatomy to assist intraoperatively in the positioning of Pedicle screws in the vertebral body. MySpine is intended for use with M.U.S.T Pedicle Screw System and its cleared indications for use. Use of the quides involves surgical planning software used pre-operatively to plan the surgical placement of the components on the basis of patient radiological images with identifiable placement anatomical landmarks and surqiçal equipment components. These components include patient-specific guides fabricated on the basis of the surgical plan to precisely reference the placement of the implant components intra-operatively per the surgical plan.

MySpine Screw Placement Guides are intended for single use only.

The MySpine Pedicle Screw Placement Guides are patient specific surgical instruments that allow for guided pedicle screw placement of the M.U.S.T pedicle screws (K121115). The M.U.S.T pedicle screws are guided through the patient's anatomically matched MySpine Pedicle Screw Placement Guides in order to provide optimal positioning according to the surgeon's preoperative planning. The MySpine software platform allows the surgeon to complete the preoperative planning in 3D based on the patient's spinal CT scans.

The components of the MySpine Pedicle Screw Placement Guides include a Drill Guide (Polyamide PA 2200), Sleeves for Awls, Probes and Screw Drivers (Polyamide PA 2200). Sleeves for taps and drills (Wrought stainless steel AISI 630. ASTM F 899), and Vertebral Bone Models (Polyamide PA 2200). The MySpine Pedicle Screw Placement Guides are single use, external communicating devices with limited (<24 hours) contact duration and are provided in sterile and non-sterile versions.

The provided document describes the MySpine Pedicle Screw Placement Guides, a patient-specific surgical instrument. It details performance testing and establishes substantial equivalence to a predicate device, but it does not include a typical AI/ML medical device study with acceptance criteria, sample sizes, and expert validation for an algorithm's performance.

Instead, the performance testing focuses on traditional medical device validation, such as biocompatibility, dimensional accuracy, mechanical testing, and process reproducibility. The document mentions "software tools used to manufacture the MySpine Pedicle Screw Placement Guides were validated for their intended use," but this refers to the software used for design and fabrication, not an AI algorithm performing diagnostic or predictive tasks.

Therefore, the requested information elements related to AI/ML device studies cannot be extracted from this 510(k) summary.

However, I can extract information regarding the general performance testing and a design validation cadaver study.

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)

• Test Set Sample Size: The document mentions "worst case component size and option/design" were tested for non-clinical performance testing. For the design validation, it states "a cadaver laboratory" was used. The specific number of cadavers or components tested is not specified.
• Data Provenance: Not specified for the non-clinical tests or the cadaver study. The cadaver study is prospective in the sense that the device was used in a controlled setting for validation.

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 mention the use of experts to establish a "ground truth" for the performance or design validation in the sense of clinical decision-making or image interpretation. This refers to engineering and design validation tests on the physical device. The cadaver study would typically involve surgeons, but their number and specific qualifications are not stated.

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

• Not applicable as the testing performed is for physical device performance and design validation, not for an algorithm's output requiring expert 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

• No. An MRMC comparative effectiveness study was not conducted. This is not an AI/ML diagnostic or assistive device in the context typically studied by MRMC.

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

• No. This is not an AI algorithm with standalone performance. The "software tools" mentioned are for manufacturing and surgical planning, where the human surgeon makes the final decisions and uses the physical guides.

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

• For the non-clinical performance tests, the "ground truth" would be established by engineering specifications and measurements (e.g., dimensional accuracy within tolerances, material strength meeting standards).
• For the cadaver laboratory design validation, the "ground truth" would be established by successful placement of pedicle screws as intended by the surgical plan using the guides, likely assessed by direct observation, imaging, or dissection. This is a form of simulated outcome verification.

8. The sample size for the training set

• Not applicable. The device is a patient-specific surgical guide manufactured based on individual patient CT scans using pre-validated software. There is no "training set" for an AI algorithm in the context described.

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

• Not applicable as there is no training set for an AI algorithm.

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