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SurgiCase Guides are intended to be used as surgical tools to transfer a pre-operative plan to the surgery. The devices are intended to guide the marking of bone and/or guide surgical instruments during craniofacial osteotomies.

SurgiCase Guides are intended for single use only.

The SurgiCase Guides are patient specific devices or templates that are based on a pre-operative software planning and are designed to fit a specific patient. These templates are used to assist a surgeon in transferring this pre-operative plan to the surgery by guiding the marking of bone and/or guiding surgical instruments. Guides are individually designed and manufactured for each patient using a design and manufacturing process with strict procedures and work instructions to guarantee templates that consistently perform in a safe and effective way.

The SurgiCase Guides are based on a software planning generated using the previously cleared SurgiCase software (K073449).

The provided text describes non-clinical tests but does not include a table of acceptance criteria or specific performance metrics with numerical values. Therefore, I cannot generate 'A table of acceptance criteria and the reported device performance'.

Here's the information extracted from the document regarding the study and acceptance criteria:

1. A table of acceptance criteria and the reported device performance
No quantitative acceptance criteria or reported performance metrics are provided in the document. The text broadly states: "The guides meet the predefined acceptance criteria." and "Testing verified that the accuracy and performance of the system is adequate to perform as intended."

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not specified. The document mentions "bone models and cadaveric specimens" but does not give the number used.
• Data Provenance: Not specified. The study involved "bone models and cadaveric specimens," implying a laboratory or simulated environment rather than human patient data, making it prospective in nature for device validation. Country of origin is not mentioned.

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. The ground truth for the "quantitative validation" would be based on measurements against the pre-operative plan, not expert consensus as it's a device accuracy study. Therefore, no experts were explicitly used to establish ground truth in this context.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. This study focused on the accuracy of the device in transferring a pre-operative plan, not on diagnostic interpretations 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
No, a multi-reader multi-case (MRMC) comparative effectiveness study was not performed. The study focused on the device's accuracy in guiding surgical procedures, not on human reader performance with or without AI assistance. The device in question is a surgical guide, not an AI-driven diagnostic tool for interpretation by human readers.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
Yes, a standalone performance assessment was conducted for the device. The "Quantitative validation using bone models and cadaveric specimens to validate the accuracy the guides obtain in transferring a surgical planning to the actual surgery during craniofacial osteotomies" evaluated the device's inherent accuracy. This is a standalone assessment of the physical guide's performance.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
The ground truth implicitly used for the "quantitative validation" would be the precise measurements and positions defined in the pre-operative software plan against which the actual surgical outcome (guided by the device) was compared. This is a form of direct measurement against a defined target. For "qualitative validation," the ground truth was the expected "fit and stability" as assessed by observers, but the specific criteria for this are not detailed.

8. The sample size for the training set
Not applicable. This device is a physical surgical guide developed through a design and manufacturing process, not a machine learning algorithm that requires a training set. The "SurgiCase software" which generates the planning data for the guides was "previously reviewed under K073449" and is not part of this 510(k) submission. Therefore, no training set for the SurgiCase Guides themselves is relevant here.

9. How the ground truth for the training set was established
Not applicable, as there is no training set for the SurgiCase Guides.

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SurgiCase is software for pre-operative simulation of orthognathic surgical treatment options, based on imaging information from a medical scanner such as a CT scanner or a Magnetic Resonance Imaging (MRI) scanner.

This submission is a Traditional 510(k) for the Orthognathic wizard of SurgiCase software application.

SurgiCase is software for pre-operative simulation of orthognathic surgical treatment options, based on imaging information from a medical scanner such as a CT scanner or a Magnetic Resonance Imaging (MRI) scanner.

Based on the software planning several options are available to transfer the result of the planning to surgery. Examples:

• The software planning can be used to select appropriate implants or implant sizes for use during surgery.
• . Based on the planning, patient-specific surgical guides and implants can be designed.
• . Patient-specific surgical splints can be generated to transfer the planned dental occlusion to surgery.

The SurgiCase software platform is the basis of all clinical Materialise software designed for surgery planning. The platform allows basic functionality such as visualizing 3D objects, visualizing medical image data, generating 3D objects from medical image data and measuring.

On top of this platform, modules, also called wizards, can be added that each offer additional functionality such as planning a specific surgical routine. This platform is the main general wizard, while additional modules (wizards) are mainly based on the functionality of this general wizard; they assist the surgeon to plan specific surgery types step-by-step by providing each a different user interface, giving the surgeon the opportunity to fine tune parameters specific for that type of surgery. Current premarket notification is only for the Orthognathic wizard of the SurgiCase software. The rest of software wizards have been cleared under K073449 submission for the SurgiCase software.

Here's an analysis of the provided text regarding the acceptance criteria and study for the SurgiCase Orthognathic software wizard, based on the information available in the 510(k) summary:

Summary of Device Acceptance Criteria and Performance Data (Based on this 510(k) Pre-submission Documentation):

Based on the provided 510(k) summary, the device's acceptance criteria primarily revolve around its equivalence to its predicate device (SurgiCase, K073449) and successful completion of non-clinical software verification and validation. There are no explicit, quantifiable acceptance criteria or reported performance metrics in the provided text other than the successful completion of these non-clinical tests.

1. Table of Acceptance Criteria and Reported Device Performance:

Missing Information/Caveats: The document explicitly states that "Software verification and validation testing will be completed by the end of August 2011." While the FDA's clearance letter implies these were successfully completed and reviewed, the detailed reports themselves are not part of this public summary. Therefore, specific quantifiable acceptance criteria (e.g., accuracy, precision, processing time) and their corresponding performance values from these internal tests are not provided in this document. The "reported device performance" in the table above is inferred from the FDA's clearance.

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

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified. The document states the software uses "imaging information from a medical scanner such as a CT scanner or a Magnetic Resonance Imaging (MRI) scanner," but does not mention the origin (country, specific hospitals) or nature (retrospective/prospective) of any specific data used for testing or validation.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not specified.

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:

• MRMC Study: No. The document explicitly states "Clinical testing: Not applicable." This indicates that no studies comparing human readers with and without AI assistance were conducted or submitted as part of this 510(k).

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

• Standalone Performance: The 510(k) summary only mentions "Software verification and validation testing." While these tests likely assessed the algorithm's performance in isolation (standalone), the specific details of these tests, including the metrics and results, are not provided in this document. The document primarily focuses on the regulatory submission process and substantial equivalence, not detailed technical performance studies.

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

• Type of Ground Truth: Not specified. Given the absence of detailed clinical or performance studies, the specific type of ground truth used for any internal software testing is not available in this summary.

8. The sample size for the training set:

• Sample Size for Training Set: Not specified. This type of detail, if applicable to the software's development (e.g., for machine learning components, which are not explicitly mentioned but could be part of "image processing"), is not included in this 510(k) summary.

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

• Ground Truth for Training Set: Not specified.

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SurgiCase Guides are intended to be used as surgical tools to transfer a pre-operative plan to the surgery. The devices are intended to guide the marking of bone and/or guide surgical instruments in mandibular and maxillofacial surgical procedures.

SurgiCase Guides are intended for single use only.

The SurgiCase Guides are patient specific devices or templates that are based on a preoperative software planning and are designed to fit a specific patient. These templates are used to assist a surgeon in transferring this pre-operative plan to the surgery by guiding the marking of bone and/or guiding surgical instruments.A standardized design and manufacturing process with detailed procedures and work instructions allows manufacturing patient-specific templates that consistently perform in a safe and effective way during surgery.

The SurgiCase Guides are based on a software planning generated using the previously cleared SurgiCase software (K073449).

SurgiCase is software for pre-operative simulation and evaluation of implant placement and surgical treatment options, based on imaging information from a medical scanner such as a CT scanner or a Magnetic Resonance Imaging (MRI) scanner. The SurgiCase software was previously reviewed under K073449 and is not submitted for review in this 510k submission. References to the software are included to give a complete overview on the guide design process.

The provided text does not contain detailed information about specific acceptance criteria with quantifiable metrics, a formal study demonstrating the device meets these criteria, or granular details about test sets, ground truth establishment, or multi-reader multi-case studies typically associated with AI/ML device evaluations.

The document describes a 510(k) submission for "SurgiCase Guides," which are patient-specific surgical templates based on pre-operative software planning. The performance data section broadly mentions "non-clinical tests such as quantitative validation using bone models and cadaveric specimens" were performed and "verified that the accuracy and performance of the system is adequate to perform as intended." However, it does not provide the specific acceptance criteria or the results of these tests in a quantifiable manner.

Here's an analysis of the requested information based on the provided text:

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)

• Sample size: Not specified. The document mentions "bone models and cadaveric specimens" but does not give the number of models or specimens used.
• Data provenance: Not specified.

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/Not specified. The term "ground truth" as typically used in AI/ML evaluation referring to expert-annotated data is not mentioned. The device is a surgical guide system, and its validation involves physical accuracy on models/cadavers, not interpretation of images by experts.

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

• Not applicable/Not specified. Adjudication methods are typically relevant for cases where multiple experts provide annotations that need to be reconciled to establish a ground truth for a test set in AI/ML contexts. This is not described for a physical device validation.

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 is not mentioned. This type of study is usually conducted for AI-powered diagnostic or assistive tools where human readers are involved in interpreting data with and without AI. The SurgiCase Guides are physical surgical tools, not AI for human interpretation.

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

• The document implies that the "quantitative validation using bone models and cadaveric specimens" focused on the physical accuracy of the guides themselves, which could be considered a form of standalone performance of the device. However, it's not an "algorithm only" performance because the device itself is a physical object designed to guide surgical instruments. The software used to design the guides (SurgiCase software K073449) was previously cleared and is not the subject of this 510(k) submission.

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

• The "ground truth" in this context would likely refer to the actual surgical plan (as transferred to the models/cadavers) or ideal anatomical positioning, against which the guided surgical actions were compared for accuracy. The document mentions "quantitative validation," suggesting objective measurements of accuracy were taken, but the specific nature of this "ground truth" (e.g., precise measurements of osteotomy planes or screw placement from a gold standard) is not detailed.

8. The sample size for the training set

• Not applicable/Not specified. Surgical guides are precisely manufactured to a patient's anatomy based on imaging and a pre-operative plan. There is no "training set" in the sense of machine learning model development mentioned for the SurgiCase Guides themselves. The underlying SurgiCase software (K073449) generates the plan, but its "training" details are not part of this submission.

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

• Not applicable/Not specified. As there is no "training set" described for the SurgiCase Guides, the establishment of ground truth for such a set is not discussed.

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Signature™ Personalized Patient Care System is intended to be used as a surgical instrument to assist in the positioning of total knee replacement components intra-operatively and in guiding the marking of bone before cutting provided that anatomic landmarks necessary for alignment and positioning of the implant are identifiable on patient imaging scans.

The Signature™ Personalized Patient Care System can be used with the following Biomet knee systems and their respective components: Vanguard® Complete Knee System, Vanguard® SSK 360, Vanguard® SSK Revision Knee System, Regenerex® Primary Tibial System, Offset & Microplasty Tibial Systems, Maxim® Complete Knee System, Ascent™ Total Knee System, and AGC® Complete Knee system.

The Signature™ guides are intended for single use only.

Signature тм Personalized Patient Care System consists of a software component, Signature™ Planner and a hardware component, Signature™ guides and is designed to assist the surgeon in the placement of total knee replacement components.

Here's an analysis of the provided text regarding the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state specific numerical acceptance criteria for the device's performance. Instead, it refers to validation against intended use and substantial equivalence to predicate devices. The performance is broadly stated as "adequate."

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

• Test Set Sample Size: The text states "cadaveric trials" were performed. However, it does not provide a specific number or range of cadavers used for these trials.
• Data Provenance: The cadaveric trials imply a prospective data collection for the performance testing. The country of origin for the cadavers is not specified.

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

The document does not mention the use of experts to establish ground truth for the CADAVERIC test set. The evaluation appears to be based on direct measurement of device output (e.g., pin placement accuracy). If human judgment was involved, it is not detailed.

4. Adjudication Method for the Test Set

As the document does not detail the use of experts or a subjective assessment for the test set's ground truth, an adjudication method is not described. The assessment seems to be based on objective measurement during the cadaveric trials.

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 mentioned in this 510(k) summary. The device, Signature™ Planner, is a software for pre-surgical planning and the Signature™ guides are patient-specific templates. The focus is on the accuracy of these tools directly, not on improving human reader performance with AI assistance.

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

Yes, in spirit. The "Signature™ Planner software... has been validated for its intended use" and "Accuracy performance testing by means of cadaveric trials" was performed. While a surgeon uses the software to "inspect, fine-tune and approve the pre-surgical plan," the performance testing described seems to primarily evaluate the system's (software + guides) ability to accurately transfer the plan to the physical space (cadaver) without explicitly measuring the impact of human fine-tuning on the accuracy results reported in the cadaveric trials. The accuracy reported ("adequate") is the outcome of the system's ability to produce precise pin locations.

7. The Type of Ground Truth Used

The ground truth for the cadaveric trials would likely be established through direct, precise measurement of the actual pin placements on the cadaveric bones relative to the intended pre-surgical plan. This would involve highly accurate measurement techniques (e.g., CMM, optical tracking, or high-resolution CT scans of the cadaver after pin insertion) to determine the deviation from the planned positions. This falls under objective physical measurement rather than expert consensus, pathology, or outcomes data.

8. The Sample Size for the Training Set

The document does not provide any information regarding a training set size. This is typical for medical device submissions focusing on predicate pathway and substantial equivalence, especially for technologies predating the widespread use of deep learning models that require large training datasets. The "Signature™ Planner" likely relies on established geometric algorithms and imaging processing techniques rather than a machine learning model that requires a distinct training phase with labeled data.

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

As no training set is mentioned (see point 8), no information is provided on how its ground truth would have been established.

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