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The Smith & Nephew SURESHOT™ Targeting System is intended to be an intraoperative image guided localization system. It is a computer assisted orthopedic surgery tool to aid the surgeon with drill positioning for screws during intramedullary nail implantation. It provides information to the surgeon that is used to place surgical instruments during surgery utilizing intraoperatively obtained electromagnetic tracking data. The Smith & Nephew SURESHOT™ Targeting System is indicated for long bone fractures treated with intramedullary nails in which the use of stereotactic surgery may be appropriate.

Subject of this premarket notification are modifications to the SURESHOT™ Distal Targeting System V4.0- targeter which has been designed with a reduced size and weight compared to the previous design of the targeter (K092497). The SURESHOT Targeting system targeter is intended to be used the SURESHOT trauma interfaces (K170977, K100107) and software V4.0 (K170280).

The SURESHOT™ Targeting System is a computer controlled electromagnetic tracking system. It assists the surgeon in locating and positioning screws in an intramedullary nail implant during orthopedic trauma surgery. The link between the sterile surgical area (patient) and the instrument system is provided through an electromagnetic tracking system. Electromagnetic spatial measurement systems determine the location of instruments that are embedded with sensor coils. When the sensor-embedded instrument is placed inside controlled, varying magnetic fields, voltages are induced in the sensor coils. These induced voltages are used by the measurement system to calculate a 3D virtual position of the instrument. Because the magnetic fields are of low field strength and can safely pass through human tissue, location measurement of an object is possible without the line-of-sight constraints of an optical spatial measurement system that requires a camera.

The SURESHOT™ Distal Targeting System V4.0 targeter is intended to be used with existing Smith & Nephew software, trauma interface, instruments and implants, No new instruments or implants are being cleared via this premarket notification.

The provided text describes a 510(k) premarket notification for the Smith & Nephew SURESHOT Distal Targeting System V4.0. The submission focuses on modifications to an existing device (targeter) with reduced size and weight, and largely relies on the substantial equivalence to a predicate device (PiGalileo Screw Targeting System V1.1) and pre-clinical testing.

Based on the provided text, here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them:

Overall Conclusion from the Document:

The document states that "Clinical data was not needed to support the safety and effectiveness of the subject device." This is a crucial piece of information, as it indicates that the FDA's acceptance was primarily based on:

1. Substantial Equivalence: Demonstrating that the modified device is as safe and effective as a legally marketed predicate device with identical indications for use and equivalent technological principles, despite minor design changes (reduced size/weight).
2. Pre-clinical Testing (Verification and Validation): Ensuring the device's functionality, electrical safety, electromagnetic compatibility, and software performance meet specified standards.

Therefore, for the SURESHOT Distal Targeting System V4.0 targeter, the "acceptance criteria" are not framed as specific performance metrics derived from a human-in-the-loop or standalone AI study, but rather as successful demonstration of substantial equivalence and compliance with engineering/software V&V standards.

Detailed Breakdown for the SURESHOT Distal Targeting System V4.0:

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

Since no clinical study data with specific performance metrics (like accuracy, sensitivity, specificity, etc., which are common for AI/diagnostic devices) are provided, the "acceptance criteria" are derived from the substantial equivalence and pre-clinical testing mentioned.

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

• Test Set Sample Size: Not applicable in the context of clinical data for performance evaluation. The "test set" here refers to the units of the device and associated software/hardware components subjected to engineering and software verification/validation tests. The document doesn't specify the number of physical targeters or software instances used for these tests, but it indicates the tests were conducted successfully.
• Data Provenance: The data provenance is from internal Smith & Nephew pre-clinical testing, performed to demonstrate compliance with standards and equivalence. It is not patient or clinical data, thus "country of origin" and "retrospective/prospective" are not relevant in this context.

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

• Not applicable. As this approval is based on substantial equivalence and pre-clinical V&V, there was no "ground truth" derived from expert medical review of patient data. The "ground truth" for the engineering tests would be the established engineering specifications and regulatory standards. The experts involved would be engineers, quality assurance personnel, and regulatory affairs specialists within Smith & Nephew who designed, built, tested, and submitted the device. Their qualifications would align with these disciplines.

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

• Not applicable. No clinical test set requiring human adjudication of ground truth was conducted. The "adjudication" in this context would be the internal review and sign-off processes for engineering and software validation reports, ensuring tests met predefined acceptance criteria.

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. The document explicitly states: "Clinical data was not needed to support the safety and effectiveness of the subject device." Therefore, no MRMC study, or any clinical comparative effectiveness study involving human readers/users, was performed or presented. This device is a surgical tool, not an AI diagnostic/interpretative device the FDA typically reviews with MRMC studies.

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

• Not directly applicable in the terms of standalone diagnostic AI performance. However, extensive standalone software validation (algorithm performance) was conducted through the listed "Software verification and validation testing," which included tests like "Targeting Calculations META," "Targeting Calculations TAN/FAN," "Drill Depth Measurement," and "Nail Rotation." These tests verify the accuracy and correctness of the algorithms within the software, independent of a human operator, by comparing computed results against expected outputs based on known inputs or simulations. This is a form of "standalone" evaluation of the software's functional correctness.

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

• For the software testing, the "ground truth" was deterministic: it was established through engineered specifications, mathematical models, and simulated inputs that define the expected correct output of the algorithms. For example, for "Targeting Calculations," the ground truth would be the precisely calculated correct target coordinates based on the software's intended geometric and kinematic models. For "Drill Depth Measurement," the ground truth would be the predefined correct depth for a given input. This is not medical expert consensus, pathology, or outcomes data.
• For the hardware (EMC, electrical safety), the ground truth was established international and national regulatory standards (e.g., IEC 60601 series).

8. The sample size for the training set:

• Not applicable. This device is a surgical targeting system, not an AI/machine learning model that requires a "training set" of data in the typical sense (e.g., for image classification or prediction). The software is deterministic and rule-based, performing calculations and tracking based on predefined engineering principles and algorithms, rather than learning from data.

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

• Not applicable, as no training set was used.

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