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The X-Guide® Surgical Navigation System is a computerized navigational system intended to provide assistance in both the preoperative planning phase and intra-operative surgical phase of dental implantation procedures. The system provides software to preoperatively plan dental implantation procedures and provides navigational guidance of the surgical instruments.

The device is intended for use for partially edentulous and edentulous adult and geriatric patients who need dental implants as part of their treatment plan.

The X-Guide® Surgical Navigation System is an electro-optical dental implantation procedures by providing the surgeon with accurate surgical tool placement and guidal plan built upon Computed Tomographic (CT scan) data.

The X-Guide® Surgical Navigation System is currently cleared (K150222) with Bone Screws which are intended to affix an Edentulous Clin to an edentulous patients anatomy. The Edentulous clip is necessary to attach tracking patterns to facilitate the navigation and tracking process, and is calibrated to the patient anatomy and CT.

As an alternative, the proposed EDX Bone Screws and EDX Nut may be used to secure tracking patterns to edentulous patient anatomy.

The proposed EDX Bone Screws are a titanium screws which are tapered and vary in diameters of 2.7 mm and 3.2 mm wth thread lengths of 7mm and stud lengths of 4 mm, and 16 mm. In addition to the dimensional design changes, the screws have been modified with an M5 threading to accept the EDX Nut. The addition of M5 the Bone Screw is necessary to secure the tracking patterns to the patient.

The 2.7 mm diameter EDX Bone Screws are typically placed to secure the EDX Tracker Arm. Should the bone be too soft resulting in a loose Bone Screw, a large diameter "rescue" EDX Bone Screw (3.2 mm diameter) can be placed.

All of the EDX Bone Screws and EDX Bone Nuts are manufactured using a Ti6AL4V (Grade 5) alloy per ASTM F136 and adhere to the requirements of ASTM F543.

The EDX Bone Screws are intended to be removed from the conclusion of the surgical implant procedure. The EDX Bone Screws are single use devices, sold in a non-sterile state and intended to be sterilized by the end user prior to use.

The body of the proposed EDX Tracker Arms a stanless steel, which is a harder material providing more stability / rigidity in the Tracker Arm. A passivation coating is added to prevent oxidation of the during steam sterilization.

The EDX Tracker Arms include a spike intended to added stability in EDX Tracker Arm registration.

The body of the proposed EDX Tracker Arms are contoured in a variety of geometric shapes intended to minimize interferences with patient soft tissue by positioning on the Mandible and Maxilla in the posterior positions of the oral cavity.

The EDX Tracker Arms are distributed in a non-sterile state, and intended to be sterilized by the end user prior to use.

This is an FDA 510(k) Premarket Notification for the X-Guide® Surgical Navigation System, specifically addressing new components (EDX Bone Screws, EDX Nut, and EDX Tracker Arms). The document details the device description, comparison to predicate and reference devices, and non-clinical performance testing.

Here's the breakdown of the acceptance criteria and study information, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance numbers for the new components in terms of clinical accuracy or effectiveness. Instead, the "Performance Testing - Non-Clinical" section describes various tests performed and their conclusions, primarily focusing on demonstrating equivalence to the predicate device or adherence to existing standards.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not explicitly stated for each specific test mentioned (e.g., mechanical testing, sterilization validation). The document uses a comparative approach, relying on the substantial equivalence to previous tests or established standards.
• Data Provenance: Not explicitly stated (e.g., country of origin). The testing seems to be internal or conducted by the manufacturer based on regulatory standards. The studies referred to are "performance and bench testing." The phrase "Additional mechanical testing was conducted on the subject device" suggests this was done for the current submission.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

Not applicable. The provided document details non-clinical performance testing and a comparison to a predicate device for regulatory clearance. It does not describe a study involving human experts to establish ground truth for a test set in the context of diagnostic accuracy or clinical decision-making.

4. Adjudication Method for the Test Set

Not applicable, as there is no mention of a test set requiring adjudication in the context of expert review or consensus.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done for this submission. The document explicitly states: "Clinical testing was not necessary for the substantial equivalence determination."

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The device itself is a surgical navigation system, which inherently involves a human (the surgeon) in the loop. The "performance and bench testing" described are non-clinical and focus on the physical components and their properties, not a standalone algorithmic performance in the absence of human interaction.

7. The Type of Ground Truth Used

The "ground truth" for the non-clinical tests is established by:

• Established Standards: e.g., ASTM E543-17 for mechanical properties, ISO 10993-1 for biocompatibility, ISO 17665-1 for sterilization.
• Predicate Device Data: Performance characteristics of the previously cleared X-Guide® Surgical Navigation System (K150222) serve as a baseline for demonstrating substantial equivalence of the new components.

8. The Sample Size for the Training Set

Not applicable. This document is not describing an AI/algorithm where a "training set" would be used in the traditional sense. The X-Guide® system is a navigation system that uses CT scan data for preoperative planning and intra-operative guidance, not a machine learning model that requires a training set for its development.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as there is no training set for an AI/algorithm described in this document.

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The KLS Martin Individual Patient Solutions (IPS) Planning System is intended for use as a software system and image segmentation system for the transfer of imaging information from a medical scanner such as a CT based system. The input data file is processed by the IPS Planning System and the result is an output data file that may then be provided as digital models or used in an additive manufacturing portion of the system that produces physical outputs including anatomical models, guides, and case reports for use in thoracic (excluding spine) and reconstructive surgeries. The IPS Planning System is also intended as a pre-operative software tool for simulating surgical treatment options.

The KLS Martin Individual Patient Solutions (IPS) Planning System is a collection of software and associated additive manufacturing equipment intended to provide a variety of outputs to support thoracic (excluding spine) and reconstructive surgeries. The system uses electronic medical images of the patients' anatomy (CT data) with input from the physician. to manipulate original patient images for planning and executing surgery. The system processes the medical images and produces a variety of patient specific physical and/or digital output devices which include anatomical models, guides, and case reports.

The KLS Martin Individual Patient Solutions (IPS) Planning System is a medical device for surgical planning. The provided text contains information about its acceptance criteria and the studies performed to demonstrate its performance.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document describes specific performance tests related to the materials and software used in the KLS Martin Individual Patient Solutions (IPS) Planning System. However, it does not explicitly provide a table of acceptance criteria with numerical targets and corresponding reported device performance values for the device's primary function of surgical planning accuracy or effectiveness. Instead, it relies on demonstrating that materials withstand sterilization, meet biocompatibility standards, and that software verification and validation were completed.

Here's a summary of the performance claims based on the provided text:

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

The document specifies "simulated use of guides intended for use in the thoracic region was validated by means of virtual planning sessions with the end-user." However, it does not provide any specific sample size for a test set (e.g., number of cases or patients) or details about the provenance of data (e.g., retrospective or prospective, country of origin). The studies appear to be primarily focused on material and software validation, not a clinical test set on patient data.

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

The document does not specify the number of experts used to establish ground truth for a test set, nor their qualifications. The "virtual planning sessions with the end-user" implies input from clinical professionals, but no details are provided.

4. Adjudication method for the test set

The document does not describe any adjudication method (e.g., 2+1, 3+1, none) for a 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

The document states that "Clinical testing was not necessary for the determination of substantial equivalence." Therefore, an MRMC comparative effectiveness study involving AI assistance for human readers was not performed or reported for this submission. The device is a planning system for producing physical outputs, not an AI-assisted diagnostic tool for human readers.

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

The device is described as "a software system and image segmentation system for the transfer of imaging information... The system processes the medical images and produces a variety of patient specific physical and/or digital output devices." It also involves "input from the physician" and "trained employees/engineers who utilize the software applications to manipulate data and work with the physician to create the virtual planning session." This description indicates a human-in-the-loop system, not a standalone algorithm. Performance testing primarily focuses on the software's ability to implement requirements and specifications and material properties, rather than an independent algorithmic assessment.

Software verification and validation were performed on "each individual software application used in the planning and design," demonstrating conformity with specifications. This can be considered the standalone performance evaluation for the software components, ensuring they function as intended without human error, but it's within the context of supporting a human-driven planning process.

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

For the materials and sterilization parts of the study, the "ground truth" or reference is established by international standards (ISO 10993-1, ISO 17665-1:2006) and national standards (AAMI ANSI ST72, USP allowed limit for medical devices).

For the "virtual planning sessions with the end-user," the ground truth is implicitly the expert judgment/agreement of the end-user (physician) on the simulated surgical treatment options and guide designs. No further specifics are given.

8. The sample size for the training set

The document does not mention any training set or its sample size. This is expected as the device is not described as a machine learning or AI device that requires a training set for model development in the typical sense. The software components are commercially off-the-shelf (COTS) applications (Materialise Mimics, Geomagic® Freeform Plus™) which would have their own internal validation and verification from their developers.

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

Since no training set is mentioned for the device itself, the establishment of ground truth for a training set is not applicable in this document.

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The KLS Martin Individual Patient Solutions (IPS) Planning System is intended for use as a software system and image segmentation system for the transfer of imaging information from a computerized tomography (CT) medical scan. The input data file is processed by the IPS Planning System and the result is an output data file that may then be provided as digital models or used as input to a rapid prototyping portion of the system that produces physical outputs including anatomical models, guides and case reports for use in the marking of cranial surgery. The IPS Planning System is also intended as a pre-operative software tool for simulating surgical treatment options.

The KLS Martin Individual Patient Solutions (IPS) Planning System is a collection of software and associated additive manufacturing (rapid prototyping) equipment intended to provide a variety of outputs to support reconstructive cranial surgeries. The system uses electronic medical images of the patients' anatomy (CT data) with input from the physician, to manipulate original patient images for planning and executing surgery. The system processes the medical images and produces a variety of patient specific physical and/or digital output devices which include anatomical models, guides, and case reports for use in the marking of cranial bone in cranial surgery.

The KLS Martin Individual Patient Solutions (IPS) Planning System is a software system and image segmentation system used for transferring imaging information from a CT scan. The system processes input data to produce output data files, which can be digital models or physical outputs like anatomical models, guides, and case reports for cranial surgery. It is also a pre-operative software tool for simulating surgical treatment options.

Here's an analysis of the acceptance criteria and supporting studies based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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The KLS Martin Individual Patient Solutions (IPS) Planning System is intended for use as a software system and image segmentation system for the transfer of imaging information from a medical scanner such as a CT based system. The input data file is processed by the IPS Planning System and the result is an output data file that may then be provided as digital models or used as input to a rapid prototyping portion of the system that produces physical outputs including anatomical models, guides, splints, and case reports for use in maxillofacial surgery. The IPS Planning System is also intended as a pre-operative software tool for simulating / evaluating surgical treatment options.

The KLS Martin Individual Patient Solutions (IPS) Planning System is a collection of software and associated additive manufacturing (rapid prototyping) equipment intended to provide a variety of outputs to support reconstructive and orthognathic surgeries. The system uses electronic medical images of the patients' anatomy (CT data) with input from the physician, to manipulate original patient images for planning and executing surgery. The system processes the medical images and produces a variety of patient specific physical and/or digital output devices which include anatomical models, guides, splints and case reports.

The provided text describes the KLS Martin Individual Patient Solutions (IPS) Planning System and its regulatory clearance (K182789) by the FDA. However, it does not contain information about a study proving the device meets specific acceptance criteria in the context of an AI/algorithm's performance.

Instead, the document is a 510(k) summary demonstrating substantial equivalence to a predicate device (K181241), primarily to expand the patient population to include pediatric subgroups. The core of the device is a planning system involving commercial off-the-shelf (COTS) software and additive manufacturing for physical outputs, with human-in-the-loop interaction from trained employees/engineers and physicians.

Therefore, most of the requested information regarding AI acceptance criteria, performance metrics, sample sizes, ground truth establishment, expert adjudication, and MRMC studies is not present in the provided document. The device, as described, is not an AI algorithm in the sense that it performs automated diagnostic or treatment recommendations independently based on image analysis with defined metrics. It's a system to assist human planning processes.

Here's an analysis based on the information available in the document, and a clear indication of what is not available.

Acceptance Criteria and Device Performance (as far as applicable from the document)

The document focuses on demonstrating substantial equivalence, not on acceptance criteria for a freestanding AI algorithm's performance. The "performance" described is primarily related to material properties, biocompatibility, and sterilization, and the functioning of the software as a tool for human planning.

Information NOT Available in the Document (as it pertains to an AI/Algorithm performance study):

1. Sample size used for the test set and data provenance:
   This information is not provided. The study performed was primarily non-clinical (material testing, biocompatibility, sterilization) and a risk assessment for pediatric use, not a clinical trial evaluating algorithm performance on a test set of patient data.

2. Number of experts used to establish the ground truth for the test set and their qualifications:
   Not applicable, as no dedicated test set for evaluating AI/algorithm performance against a ground truth is described. The system relies on physician input and interaction with trained employees/engineers for planning, not on an autonomous algorithmic output that requires expert adjudication for ground truth.

3. Adjudication method for the test set:
   Not applicable for the same reasons as above.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:
   No, an MRMC comparative effectiveness study was not done. The document explicitly states: "Clinical testing was not necessary for the determination of substantial equivalence." (p.11, p.14). The device is a planning system with human-in-the-loop, not an AI intended to improve human readers' performance directly.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   No, a standalone algorithm performance study was not described. The device is presented as a "software system and image segmentation system" where "trained employees/engineers... manipulate data and work with the physician to create the virtual planning session." (p.4, p.13) The core function described is human-assisted planning and production of physical models, not an autonomous algorithmic output.

6. The type of ground truth used:
   Ground truth in the context of an AI algorithm's diagnostic or predictive performance is not relevant here, as no such AI is described. The "ground truth" for the device's function would relate to the accuracy of the generated physical models and plans relative to the patient's anatomy and surgical intent, which is managed through human interaction and validation within the system's intended use.

7. The sample size for the training set:
   Not applicable. The document refers to "commercially off-the-shelf (COTS) software applications" (p.4, p.13) which implies existing, validated software tools are being used, not a newly developed AI model requiring a separate training set.

8. How the ground truth for the training set was established:
   Not applicable, as no new training set for an AI model is described.

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The KLS Martin Individual Patient Solutions (IPS) Planning System is intended for use as a software system and image segmentation system for the transfer of imaging information from a medical scanner such as a CT based system. The input data file is processed by the IPS Planning System and the result is an output data file that may then be provided as digital models or used as input to a rapid prototyping portion of the system that produces physical outputs including anatomical models, guides, splints, and case reports for use in maxillofacial surgery. The IPS Planning System is also intended as a pre-operative software tool for simulating / evaluating surgical treatment options.

The KLS Martin Individual Patient Solutions (IPS) Planning System is a collection of software and associated additive manufacturing (rapid prototyping) equipment intended to provide a variety of outputs to support reconstructive and orthognathic surgeries. The system uses electronic medical images of the patients' anatomy (CT data) with input from the physician, to manipulate original patient images for planning and executing surgery. The system processes the medical images and produces a variety of patient specific physical and/or digital output devices which include anatomical models, guides, splints and case reports.

Here's an analysis of the acceptance criteria and study information based on the provided text, focusing on what is present and what is not:

The document (K181241 510(k) Summary) describes the KLS Martin Individual Patient Solutions (IPS) Planning System, which is a software system for image segmentation and pre-operative planning, and also provides physical outputs like anatomical models, guides, and splints for maxillofacial surgery. The submission focuses on demonstrating substantial equivalence to a predicate device (VSP System K120956) and uses several reference devices.

Acceptance Criteria and Study Information:

Based on the provided text, the device itself is a planning system that ultimately produces physical outputs. The "performance" being evaluated relates to the characteristics of these physical outputs (tensile strength, biocompatibility, sterility, pyrogenicity) and the software's functionality. There isn't a direct "device performance" metric in the traditional sense of an AI diagnostic device's sensitivity, specificity, or accuracy against a clinical outcome.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of the device (planning system with physical outputs, not a diagnostic AI), the performance metrics are primarily related to safety and manufacturing quality.

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

• Test Set Sample Size: The document does not specify a "test set" in the context of a dataset for evaluating AI performance (e.g., medical images for segmentation accuracy). Instead, "testing" refers to non-clinical performance evaluations of the physical outputs and software.
  • For Tensile & Bending, Biocompatibility, Sterilization, and Pyrogenicity, the sample sizes are not explicitly mentioned, but the tests were performed on "the subject polyamide guides" and "titanium" components. The provenance is internal testing performed by the manufacturer, or results leveraged from previous KLS Martin device submissions.
  • For Software V&V, no specific numerical "test set" of software inputs is given, but testing was performed on "each individual software application."
• Data Provenance: The data provenance for non-clinical testing is internal to the manufacturer or relied upon previous regulatory submissions for similar materials/processes. It is not patient or country-specific data as would be for clinical studies. The data used by the IPS Planning System itself (CT data) would be patient data, but the evaluation here is of the system's outputs, not its interpretation of patient data in a diagnostic manner. The document states the system "transfers imaging information from a medical scanner such as a CT based system."

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

• This question is not applicable in the context of this 510(k) submission. The "ground truth" for the non-clinical tests (tensile strength, biocompatibility, etc.) is established by standard scientific and engineering methodologies, not by expert medical review of images.
• For the "Software Verification and Validation," the "ground truth" for software functionality is defined by the established software requirements and specifications, validated by internal quality and user acceptance testing, not by external experts in the medical domain. The document mentions "input from the physician" for manipulation of original patient images, suggesting physicians set the clinical goals for the plan, but the validation of the system's performance is not described as involving experts establishing a "ground truth" concerning image interpretation.

4. Adjudication method for the test set

• Not applicable. There is no adjudication method described as would be used for clinical interpretation or diagnostic performance evaluation by multiple experts. The non-clinical tests follow established standards and protocols.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done.
• This device is a planning system for maxillofacial surgery, not a diagnostic AI that assists human readers in image interpretation or diagnosis. It aids in surgical planning and creates physical outputs. The submission explicitly states: "Clinical testing was not necessary for the determination of substantial equivalence."

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

• Given the device's function, it is inherently a "human-in-the-loop" system. The description states: "The system uses electronic medical images of the patients' anatomy (CT data) with input from the physician, to manipulate original patient images for planning and executing surgery." And, "The physician provides input for model manipulation and interactive feedback through viewing of digital models...that are modified by the trained employee/engineer during the planning session."
• Therefore, performance of the algorithm without human intervention is not the intended use or focus of this submission. The "software verification and validation" (Section 11) is the closest thing to an "algorithm-only" evaluation, but it's about software functionality, not standalone image interpretation performance.

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

• For the non-clinical performance tests of the physical outputs (Tensile & Bending, Biocompatibility, Sterilization, Pyrogenicity), the "ground truth" is defined by established engineering and scientific standards (e.g., ISO 10993-1, ISO 17665-1:2006, AAMI ANSI ST72, and internal specifications).
• For Software Verification & Validation, the "ground truth" is adherence to predefined software requirements and specifications (functional and non-functional, related to image transfer, manipulation, and output file generation). It is not based on medical "ground truth" like pathology or clinical outcomes.

8. The sample size for the training set

• This question is not applicable. The KLS Martin IPS Planning System is described as using "validated commercially off-the-shelf (COTS) software applications" for image manipulation. There is no mention of a "training set" in the context of machine learning or AI model development within this summary. It appears to be a rule-based or conventional algorithmic system rather than a deep learning/machine learning model that would require a distinct training set.

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

• This question is not applicable, as no training set for machine learning was mentioned or identified in the document (see point 8).

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