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Advanced Viewer is a web based software for medical professionals that provides doctors with tools for secure online image (DICOM) review including measurement functions and the display of voxel objects.

It is not intended for detailed treatment planning, treatment of patients or the review of mammographic images. It is also not intended to be used on mobile systems.

Advanced Viewer is integrated in the online collaboration platform Quentry to share, discuss and transfer medical image data. The viewer provides capabilities to visualize medical images (DICOM) that have been uploaded to the platform before.

Quentry is a software platform consisting of a set of server-based components providing functions for transfer and storage of medical data, as well as user access via a web-based portal for data management, sharing, and download. The platform is integrated with desktop and server-based applications for upload and download of medical data from workstations and network-based image archive servers. The platform also provides interfaces for integration of third-party systems and applications. Quentry platform is an FDA class I product.

The provided text describes the "Advanced Viewer," a web-based software for medical professionals to review DICOM images. However, the document does not contain specific acceptance criteria, a detailed study that proves the device meets those criteria, or the other requested information like sample sizes, expert qualifications, or ground truth details.

The document primarily focuses on establishing substantial equivalence to predicate devices (CONi and iPlan) for regulatory approval (510(k)). It highlights that the Advanced Viewer offers more viewing features than CONi but states these do not introduce new safety or effectiveness concerns, and that it provides identical functionalities to iPlan, running on a different platform but using the same software framework.

The "Verification/validation summary" section mentions that verification was done to demonstrate that design specifications are met, and non-clinical validation included usability tests that were rated as successful according to their acceptance criteria. However, it does not detail what those acceptance criteria were or the specifics of the validation study.

Therefore, most of the information requested cannot be extracted from this document.

Here's a summary of what can be inferred from the provided text, and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria: Not explicitly stated in terms of quantitative metrics or specific thresholds. The document generally implies that the device must function equivalently or better than the predicate devices for image viewing and manipulation.
• Reported Device Performance: Not explicitly detailed with specific performance metrics. The document states that "All test reports were finally rated as successful according to their acceptance criteria" for usability tests, but doesn't elaborate on the results.

Missing Information (Not Available in the Provided Text):

2. Sample size used for the test set and the data provenance: Not mentioned.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not mentioned. The device's purpose is for viewing existing DICOM images, not for generating a diagnosis that would require ground truth in the typical AI/CAD context.
4. Adjudication method for the test set: Not mentioned.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance: Not mentioned. This device is a viewer, not an AI-based diagnostic tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not mentioned. The device is fundamentally a human-in-the-loop viewer.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not mentioned, and likely not applicable in the traditional sense for a medical image viewer. The "ground truth" for a viewer would relate to proper display and accurate measurements, likely verified against source DICOM data and expert visual inspection.
8. The sample size for the training set: Not mentioned. There is no indication of machine learning or AI algorithm training in the description.
9. How the ground truth for the training set was established: Not mentioned, and likely not applicable given the device's description.

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DASH hip is intended to be an intraoperative image-guided localization system to enable minimally invasive surgery. It links a freehand probe, tracked by a passive marker sensor system to virtual computer image space on an individual 3D-model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface.

The system is indicated for any medical condition in which the use of stereotactic surgery may be considered to be safe and effective, and where a reference to a rigid anatomical structure, such as a long bone, can be identified relative to the anatomy. The system aids the surgeon in controlling leg length and offset discrepancies.

Example orthopedic surgical procedures include but are not limited to:

Total Hip Replacement (THR) Revision surgery of THR Minimally Invasive THR Surgery

DASH hip is intended to enable operational navigation in minimally invasive orthopedic surgery. It links a surgical instrument, tracked by passive markers to virtual computer image space on an individual 3D-model of the patient's bone, which is generated through acquiring multiple landmarks.on the bone surface. DASH hip uses the registered landmarks to determine postoperative changes in leg length and offset,

DASH hip software intraoperatively registers the patient data needed for navigating the surgery. No preoperative CT-scanning is necessary.

Here's an analysis of the acceptance criteria and study information for the BrainLAB DASH hip, based on the provided text:

BrainLAB DASH hip: Acceptance Criteria and Study Details

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly list quantitative acceptance criteria in a table format. However, it states the overall performance goal and the conclusion of the pivotal study.

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

• Sample Size for Test Set: 43 consecutive Total Hip Replacement (THR) surgeries.
• Data Provenance: The study was a "prospective clinical study," implying the data was collected specifically for this purpose in a real-world clinical setting. No specific country of origin is mentioned for the clinical study data.

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

The document does not provide details on the number of experts or their qualifications for establishing ground truth within the clinical study. It states that the study evaluated "navigation accuracy," implying a comparison to some established measurement standard, but doesn't specify who performed or validated these standards. Given the context of surgical navigation, it's highly probable that orthopedic surgeons were involved in the procedures and likely in assessing outcomes, but this is not explicitly stated as "ground truth establishment" by independent experts.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for the clinical study data.

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

No, an MRMC comparative effectiveness study involving human readers and AI assistance was not performed based on the provided text. The study compared the navigation accuracy of the DASH hip (which aids the surgeon) to a previously used predicate device's technique, not the improvement of human readers with AI vs. without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The document describes "verification of the software algorithm itself" as part of the design verification activities. This suggests a standalone evaluation of the algorithm's performance prior to integration with instrumentation and human interaction. However, detailed results of this standalone testing are not provided, only that it was "successfully verified." The primary clinical study focuses on the system's navigation accuracy, which inherently includes human interaction in using the system to measure.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

For the prospective clinical study, the ground truth for "leg length and offset measurements" would likely be derived from:

• Intraoperative measurements: Taken by the surgeon using conventional or established methods, possibly compared against pre-operative plans or fluoroscopic images.
• Radiographic measurements: Post-operative X-rays interpreted to determine actual leg length and offset changes.

The document states the study showed "the accuracy relating purely to the navigation measurements... is comparable to the previously used leg length and offset technique from the predicate device." This implies the "ground truth" was a reliable, established method of measuring these parameters, against which the DASH hip's measurements were compared.

8. The Sample Size for the Training Set

The document does not mention a specific "training set" or sample size for training. The DASH hip is a navigation system that uses landmarks acquired intraoperatively to generate a patient-specific 3D model. It does not appear to be a machine learning or AI system that requires a distinct "training set" in the conventional sense for image analysis or diagnostic tasks. Its core algorithm, though, would have been developed and refined using various data, potentially from predicate devices or simulated scenarios, but this is not described as a "training set."

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

As noted above, a "training set" in the traditional machine learning sense is not explicitly described. The development and verification process included:

• Design verification: Covered instrument and system accuracy during registration, post-operative point acquisition, and analysis by comparing values to "externally-measured reference values." This implicitly establishes ground truth for verification tasks.
• Non-clinical validation: Performed with plastic bone models (sawbones) in "Usability Workshops (use labs)." Ground truth here would be the known anatomical dimensions of the models and the physical measurements taken.
• Pre-clinical validation: Performed in a "cadaver lab." Ground truth here would again be actual anatomical measurements and established surgical reference points.

These pre-clinical and verification steps provided data and a basis for establishing the correctness and accuracy of the device's measurements, which could be considered akin to establishing "ground truth" for development and early testing, distinct from a statistical "training set" for AI model development.

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iPlan RT is a radiation treatment planning system that is intended for use in stereotactic, conformal, computer planned, Linac based radiation treatment of cranial, head and neck, and extracranial lesions.

iPlan RT is a software program to generate treatment plans and to simulate the dose delivery for external beam radiotherapy. The system is the evolutionary successor of the predicate devices iPlan RT Image (K080886) and iPlan RT Dose (K080888). It is specialized for stereotactic procedures for cranial as well as extracranial lesions. It includes functions for all relevant steps from outer contour detection to quality assurance. It combines most of its predecessor's functionality iPlan RT Image and iPlan RT Dose together with additional improvements. Therefore, the new version shall be called "iPlan RT".

The device incorporates conformal beams, conformal IMRT beams, circular arcs, and both static and dynamic arc treatments. Moreover, a combination of optimized dynamic arc treatments together with IMRT beams was added to the treatment modalities.

The system calculates dose using a convolution algorithm as the previous version. Alternatively, a Monte Carlo method based calculation algorithm can be used as in iPlan RT Dose (K080888). The documentation & export function facilitates printouts of all parameters and results for the creation of DICOM RT (RT Plan and RT Image) files.

Adapting existing treatment plans during fractionated radiotherapy treatments is facilitated using an elastic deformation algorithm. Existing structures are morphed from an existing treatment plan onto a new follow-up scan. If necessary, these structures can be adapted by the physician and can be used to update the current treatment plan accordingly.

The provided document, a 510(k) summary for Brainlab AG's iPlan RT, does not contain a study that proves the device meets specific acceptance criteria in the manner typically seen for novel medical device algorithms or AI. Instead, it demonstrates substantial equivalence to predicate devices (iPlan RT Image K080886 and iPlan RT Dose K080888) through non-clinical testing.

Here's an analysis based on the provided text, addressing the requested points:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics. The summary of non-clinical testing only states that the device "has met its specifications" and is "substantially equivalent to the predicate devices" and "safe and effective for its intended use." These are general conclusions rather than detailed performance metrics.

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

The document does not mention the use of a "test set" in the context of clinical data for performance evaluation. The evaluation was based on non-clinical testing and comparison to predicate devices. Therefore, there is no information on sample size or data provenance.

3. Number of Experts Used to Establish Ground Truth and Their Qualifications

Since no clinical test set was used to establish performance against a ground truth, this information is not applicable and not provided in the document.

4. Adjudication Method for the Test Set

As no clinical test set requiring ground truth establishment was used, there is no mention of an adjudication method.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The document explicitly states: "Clinical testing is not required to demonstrate substantial equivalence or safety and effectiveness." Therefore, an MRMC comparative effectiveness study was not performed.

6. Standalone (Algorithm Only) Performance Study

The document describes non-clinical "Verification and Validation tests" which confirmed the device "met its specifications." This implicitly refers to the algorithm's performance in generating treatment plans. However, no specific metrics like sensitivity, specificity, accuracy, or a detailed study design for standalone performance are provided beyond the general statement of meeting specifications. The focus is on functionality and equivalence to predicate devices.

7. Type of Ground Truth Used

For the non-clinical testing, the "ground truth" would likely have been the expected computational output based on known physics and engineering principles for radiation dose calculation and planning. This is inferred from the statement that the device "has met its specifications." There is no mention of external clinical ground truth (e.g., pathology, outcomes data, or expert consensus on clinical cases) for this 510(k) submission.

8. Sample Size for the Training Set

The document does not mention a "training set" in the context of machine learning or AI models with data-driven training. The iPlan RT is described as an "evolutionary successor" of previous devices, suggesting a development and refinement process rather than a machine learning training paradigm. The core dose calculation uses a convolution algorithm or Monte Carlo method, which are physics-based models rather than models trained on large datasets.

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

As no explicit training set for a machine learning model is mentioned, this question is not applicable based on the provided text.

In summary, the 510(k) for iPlan RT focuses on demonstrating substantial equivalence to existing predicate devices through non-clinical verification and validation, rather than extensive clinical studies with specific acceptance criteria tables and ground truth evaluations typically associated with novel AI/ML devices.

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Disposable reflective marker spheres are attached to reference arrays and instruments, thus enabling infrared tracking systems to detect the position of the patient and instruments in the surgical field.

A disposable reflective marker sphere consists of two bonded half spheres and a screw part that is cut in the lower sphere. An adhesive combines the upper and lower half spheres. The raw sphere is covered with a defined retro-reflective foil.

The provided text describes a 510(k) summary for the "Disposable Reflective Marker Spheres" by BrainLAB AG. This document outlines the device's intended use, description, and states that it has been verified and validated. However, it does not detail specific acceptance criteria for performance, nor does it present the results of a study designed to prove the device meets such criteria with quantitative metrics.

The document focuses on substantial equivalence to a predicate device (Cranial Image Guided Surgery System K082060) rather than a direct performance study against a predefined set of acceptance criteria for the "Disposable Reflective Marker Spheres" themselves.

Here's an attempt to answer your questions based only on the provided text, highlighting what is not available:

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.
• Data provenance: Not specified. The document mentions "comparison with previously marketed devices and the results of a clinical evaluation," but no details about the methods, sample sizes, or nature (retrospective/prospective) of these evaluations are provided for the "Disposable Reflective Marker Spheres."

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 document focuses on physical properties and claims of substantial equivalence rather than a diagnostic performance study requiring expert-established ground truth.

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

• Not applicable/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

• Not applicable. This device is a physical marker sphere, not an AI or diagnostic tool that would typically involve human readers.

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

• Not applicable. This device is a physical marker sphere.

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

• Not applicable. The "ground truth" for this device would likely be adherence to engineering specifications and performance in tracking systems, rather than a clinical ground truth. The document alludes to "specifications conform[ing] with user needs and intended use."

8. The sample size for the training set

• Not applicable. This device is a physical marker sphere, not a machine learning algorithm that requires a training set.

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

• Not applicable.

Summary of missing information:

The provided document is a regulatory submission (510(k) summary) focused on demonstrating substantial equivalence to a predicate device. It primarily lists verification and validation activities at a high level. It does not provide detailed performance data, specific quantitative acceptance criteria, study methodologies (like sample sizes, expert involvement, or adjudication methods), or the results of comparative effectiveness studies that would be typically found for diagnostic AI devices. The verification activities mentioned ("Impact resistance, homogenous retroreflectivity, shelf life," "Sterile Packaging," "Sterilization") imply that internal testing was conducted against company specifications, but these specifications and their results are not detailed in this summary.

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The Digital Lightbox is a system intended for the retrieval and display of medical images from picture archiving and communication systems (PACS), file servers, or removable storage media. It includes functions for image manipulation, 3D reconstruction, basic measurements, and multi-modality image fusion. It is not intended for primary image diagnosis or the review of mammographic images.

Digital Lightbox is a medical image viewing device consisting of two high-resolution monitors controlled through touch panels with an integrated PC. It features an Ethernet connection for retrieving medical images through a computer network. Further, the device can read images from CD, DVD or USB drives through external interfaces. The device software is compatible with the DICOM standard and allows basic image manipulation, 3D reconstruction, basic measurements and multi-modality image fusion. The device software integrates a web browser and remote access software.

The provided text is a 510(k) summary for the Digital Lightbox. This document typically focuses on demonstrating substantial equivalence to predicate devices rather than presenting detailed clinical study results and acceptance criteria in the format requested for a medical AI device.

Given that the Digital Lightbox is described as a "medical image viewing device" and "not intended for primary image diagnosis," it is highly unlikely to involve acceptance criteria related to diagnostic accuracy (sensitivity, specificity, etc.) or comparative effectiveness studies with human readers assisted by AI. Its validation would primarily address functional performance, safety, and effectiveness as an image display and manipulation tool.

Therefore, the specific information requested in points 1-9 regarding diagnostic performance, sample sizes for test/training sets, expert ground truth, and MRMC studies is not available or applicable in the provided 510(k) summary for the Digital Lightbox.

However, I can extract information related to the device's validation and substantial equivalence claims:

Acceptance Criteria and Study for Digital Lightbox (Based on 510(k) Summary K080608)

While specific acceptance criteria in terms of diagnostic performance metrics (e.g., sensitivity, specificity, AUC) and detailed study designs (e.g., sample sizes, expert qualifications for ground truth) are not provided in this 510(k) summary, the document does state the general validation approach. The Digital Lightbox is a medical image viewing device, not an AI diagnostic algorithm, so its validation focuses on functional performance, safety, and equivalence to existing display systems.

1. Table of Acceptance Criteria and Reported Device Performance

• No specific quantitative acceptance criteria or reported device performance metrics (e.g., sensitivity, specificity, or image quality scores) related to diagnostic accuracy are provided or expected for this type of device.
• The document implies that the device met functional and safety criteria as part of BrainLAB's internal validation processes and demonstrated substantial equivalence to predicate devices.

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

• Not applicable/Not provided. This device is for image display and manipulation, not for diagnostic interpretation requiring a "test set" of patient data in the typical sense for AI diagnostic algorithms. Its validation would involve functional testing with various image types and formats, but not a "test set" with ground truth in the way a diagnostic algorithm would.

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

• Not applicable/Not provided. As stated above, this is not a diagnostic AI device requiring expert-established ground truth for a diagnostic test set.

4. Adjudication Method for the Test Set

• Not applicable/Not provided.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of human readers improving with AI vs. without AI assistance

• No. This device is a display and manipulation tool, not an AI assistance system for human readers. Therefore, an MRMC study comparing human readers with and without "AI assistance" (from this device) is not relevant or reported.

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

• Not applicable. The Digital Lightbox is an interactive viewing device; its function inherently involves a human user. There is no "algorithm only" diagnostic performance to evaluate.

7. The type of ground truth used

• Not applicable. For a medical image viewing device, "ground truth" as pathology or outcome data is not relevant to its primary function. Its performance would be assessed against expected display fidelity, measurement accuracy, and functionality.

8. The Sample Size for the Training Set

• Not applicable/Not provided. As this is not an AI algorithm trained on patient data for diagnostic purposes, there is no "training set" in this context.

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

• Not applicable/Not provided.

Summary of Device Validation from the 510(k) Document:

The provided text states:

• "Digital Lightbox has been verified and validated according to BrainLAB procedures for product design and development. The validation proves the safety and effectiveness of the system." (Page 1, {1})
• "The information provided by BrainLAB in this 510 (k) application was found to be substantially equivalent with the predicate devices iPlan (K 053127), iPlan Hip Templating (K 042543) and DGSCOPE, RELEASE 1.0 (K 070397)." (Page 1, {1})

This indicates that BrainLAB conducted internal verification and validation activities to ensure the device met its design specifications and performed safely and effectively as an image viewing and manipulation system. The primary "study" for regulatory approval here is the demonstration of substantial equivalence to already legally marketed predicate devices, which implies that the Digital Lightbox meets similar performance and safety standards as those devices.

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BrainLAB VectorVision trauma is intended to be a pre- and intraoperative image quided localization system to enable minimally invasive surgery. It links a freehand probe, tracked by a passive marker sensor system to virtual computer image space on a patient's pre- or intraoperative image data being processed by a VectorVision workstation. The system is indicated for any medical condition in which the use of stereotactic surgery may be appropriate and where a reference to a rigid anatomical structure, such as the skull, a bone structure like tubular bones, pelvic, calcaneus and talus, or vertebra, can be identified relative to a CT, fluoroscopic, X-ray or MR based model of the anatomy.

Example procedures include but are not limited to:

Spinal procedures and spinal implant procedures such as pedicle screw placement.

Pelvis and acetabular fracture treatment such as screw placement or illo-sacral screw fixation.

Fracture treatment procedures, such as intramedullary nailing or screwing or external fixation procedures in the tubular bones.

BrainLAB VectorVision trauma is intended to enable operational navigation in spinal, orthopedic and traumatologic surgery. It links a surgical instrument, tracked by flexible passive markers to virtual computer image space on a patient's intraoperative image data being processed by a VectorVision workstation.

VectorVision trauma allows navigation of intraoperatively acquired images considering patient's movement in correlation to calibrated surgical instruments. This allows implant positioning, screw placement and bone reduction in different views and reduces the need for treatments under permanent fluoroscopic radiation.

The provided text is a 510(k) summary for the VectorVision trauma device. It lacks detailed information about specific acceptance criteria and a structured study demonstrating the device's performance against those criteria. The provided text states "VectorVision trauma has been verified and validated according to BrainLAB's procedures for product design and development. The validation proves the safety and effectiveness of the system." This suggests that internal verification and validation studies were conducted, but the specifics are not included in this document.

Therefore, I cannot extract the information required for the table and other detailed questions from the provided text. The document focuses on the regulatory submission and substantial equivalence to a predicate device rather than presenting a performance study report.

If you have a document that details the specific verification and validation study results, I would be able to answer your request.

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BrainLAB's VectorVision® hip SR is intended as an intraoperative image-guided localization system. It links a freehand probe, tracked by a passive marker sensor system to virtual computer image space on a VectorVision® navigation station. The image data is provided either in the form of preoperatively-acquired patient images or in the form of an individual 3D model of the patient's bone, which is generated by acquiring multiple landmarks on the bone surface. The system is indicated for any medical condition in which the use of stereotactic surgery may be considered to be appropriate and where a reference to a rigid anatomical structure, such as the skull, a long bone, or vertebra, can be identified relative to a CT, X-ray, or MR-based model of the anatomy. The system aids the surgeon in accurately navigating a hip endoprothesis to the preoperatively or intraoperatively planned position.

Example orthopedic surgical procedures include but are not limited to:

· Partial/hemi-hip resurfacing

BrainLAB's VectorVision® hip SR is intended to enable operational planning and navigation in orthopedic hemi resurfacing surgery. It links a surgical instrument, tracked by flexible passive markers to virtual computer image space on an individual 3D-model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface. VectorVision® hip SR uses the registered landmarks to navigate the initial pin insertion into the femur with a pre-calibrated drillguide to the planned position.

VectorVision® hip SR allows 3-dimensional reconstruction of the relevant anatomical axes and planes of the femur and alignment of the implants. The VectorVision® hip SR software has been designed to read in data of implants and tools if provided by the implant manufacturer and offers to individually choose the prosthesis during each surgery. If no implant data is available it is possible to provide information in order to achieve a generally targeted alignment relative to the bone orientation as defined by the operating surgeon. The VectorVision® hip SR software registers the patient data needed for planning and navigating the surgery intraoperatively without CT-based imaging. The system can be used to generally align tool orientations according to the anatomy described and defined by the landmarks acquired by the surgeon.

The provided document is a 510(k) Summary of Safety and Effectiveness for the BrainLAB VectorVision® hip SR device. It details the device's intended use, description, and states that it has been verified and validated according to BrainLAB's procedures for product design and development, proving its safety and effectiveness. However, the document does not contain explicit acceptance criteria or a detailed study report with performance metrics, sample sizes, ground truth establishment, or expert qualifications as requested. It primarily focuses on demonstrating substantial equivalence to predicate devices (VectorVision® hip K040368 and VectorVision® osteotomy K042513) for regulatory clearance.

Therefore, much of the requested information cannot be extracted from this document.

Here's what can be addressed based on the provided text:

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

• Not available in the document. The document states: "The validation proves the safety and effectiveness of the information provided by BrainLAB in this 510 (k) application was found to be substantially equivalent with the predicate device Vector Vision® hip (K 040368) and Vector Vision® osteotomy (K042513)." This indicates that validation was performed, but the specific acceptance criteria and detailed performance metrics (e.g., accuracy, precision) are not included in this summary.

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

• Not available in the document. The document does not provide details on sample sizes for any test sets or the provenance of data used 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)

• Not available in the document. The document does not describe the establishment of ground truth for any test sets or the involvement or qualifications of experts.

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

• Not available in the document. The document does not mention any adjudication methods.

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

• Not available in the document. This document describes an image-guided navigation system for surgery, not an AI-assisted diagnostic device typically evaluated with MRMC studies. There is no mention of human reader studies or AI assistance for diagnostic interpretation.

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

• The device itself is an "intraoperative image-guided localization system" that aids a surgeon. While the "algorithm only" performance (e.g., system accuracy) would be part of its validation, the document does not provide details or results of such a standalone performance study. It only states that the device was "verified and validated."

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

• Not available in the document. The document does not specify the type of ground truth used for any validation. Given it's a navigation system, ground truth would likely relate to accuracy of tool positioning relative to planned positions or anatomical landmarks, but this is not detailed.

8. The sample size for the training set

• This device is described as an image-guided surgery system that uses either "preoperatively-acquired patient images" or an "individual 3D model... generated by acquiring multiple landmarks on the bone surface." It does not explicitly describe a machine learning model that would require a "training set" in the conventional sense for deep learning. If there are underlying algorithms, the training set size for those is not available in the document.

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

• As a "training set" is not explicitly mentioned in the context of machine learning, the establishment of its ground truth is not available in the document. If this refers to the data used to develop the system's underlying algorithms, those details are not provided.

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Ci TKR/UKR is intended to be an intraoperative image guided localization system to enable minimally invasive surgery. It links a freehand probe, tracked by a passive marker sensor system to virtual computer image space on an individual 3D-model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface. The system is indicated for any medical condition in which the use of stereotactic surgery may be appropriate and where a reference to a rigid anatomical structure, such as the skull, a long bone, or vertebra, can be identified relative to a CT, X-ray, MR based model of the anatomy. The system aids the surgeon to accurately navigate a knee prosthesis to the intraoperatively planned position. Ligament balancing and measurements of bone alignment are provided by Ci TKR/UKR.

Example orthopedic surgical procedures include but are not limited to:

Knee Procedures: Total Knee Replacement Unicondylar Knee Replacement Ligament Balancing Range of Motion Analysis Cruciate Ligament Surgery Patella Tracking

Ci TKR/UKR is intended to enable operational planning and navigation in orthopedic surgery. It links a surgical instrument, tracked by flexible passive markers to virtual computer image space on an individual 3D-model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface. Ci TKR/UKR uses the registered landmarks to navigate the femoral and tibial cutting guides and the implant to the planned optimally position.

Ci TKR/UKR allows 3-dimensional reconstruction of the mechanical axis and alignment of the implants. Ci TKR/UKR software registers the patient data needed for planning and navigating the surgery intraoperatively. No preoperative CT-scanning is necessary.

Ci TKR/UKR software has been designed to read in implant data from DePuy and offers to individually choose the prosthesis during each surgery.

The CAS Knee Instrumenation (K-043223) developed and manufactured by DePuy is integrated in the Ci TKR/UKR software. Together, instruments and hardware/software enable operational planning and navigation during minimally invasive orthopaedic knee replacement surgery.

The provided text is a 510(k) Summary for the Ci TKR/UKR device, an image-guided localization system for knee surgery. It describes the device, its intended use, and its substantial equivalence to predicate devices. However, the document does not contain specific acceptance criteria or details of a study that proves the device meets such criteria.

The available information focuses on the regulatory submission and FDA clearance based on substantial equivalence to existing devices, rather than a detailed performance study with explicit acceptance criteria.

Therefore, I cannot fulfill your request for a table of acceptance criteria and reported device performance, or details about a study proving these criteria, based on the provided text. The document states:

"Ci TKR/UKR has been verified and validated according to BrainLAB's procedures for product design and development. The validation proves the safety and effectiveness of the system."

This indicates that validation was performed, but the specifics of that validation, including acceptance criteria, sample sizes, ground truth establishment, or expert involvement, are not included in this publicly available 510(k) summary.

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iPlan's indications for use is to prepare and present patient and image data based on CT, MR, Xray(Fluoro), including

• . image preparation
• . image fusion
• image segmentation .
  where the result is used for the creation of treatment plans for Stereotactic Surgery:
• Surgery Planning
• BrainMAP
• Functional Planning
  In addition iPlan's indications for use is to prepare and present patient and image data based on CT, MR, X-ray(Fluoro) including
• . image preparation
• . image fusion
• image segmentation .
  where the result is preplanned data to be used by other BrainLAB medical devices such as VectorVision (for performing the planned treatment) where these medical devices are used for:
• Image Guided Surgery
• FiberTracking
• BOLD MRI
  iPlan BOLD MRI indication for use is to prepare image data based on BOLD (blood oxygen level dependent) MRI scan studies and display the result as parametric images. When interpreted by a trained physician or surgeon this information may be used with other anatomical information for planning and image guided surgery.

The BrainLAB iPlan BOLD MRI module is software used for processing BOLD (blood oxygen level dependent) MRI sequences and display of calculation results. The slight MRI susceptibility changes between the images are visualized as parametric images.

The provided document is a 510(k) summary for the iPlan BOLD MRI module, a software used for processing BOLD MRI sequences and displaying calculation results as parametric images. The document states that the device has been verified and validated according to BrainLAB's procedures, and that this validation "proves the safety and effectiveness of the system." However, it does not contain specific details about acceptance criteria, a formal study protocol, or performance metrics to demonstrate the device meets acceptance criteria. The approval is based on substantial equivalence to a predicate device (BrainLAB iPlan K041703).

Therefore, most of the requested information regarding acceptance criteria and performance studies cannot be extracted from this document.

Here's a breakdown of what can and cannot be provided based on the input:

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

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

• Sample Size (Test Set): Not specified in the document.
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not specified in the document.

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 in the document.
• Qualifications of Experts: Not specified in the document. The document states that the information "may be used with other anatomical information for planning and image guided surgery" when interpreted by a "trained physician or surgeon," but this is an intended use statement, not a description of ground truth establishment for a study.

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

• Not specified in the document.

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

• Not specified in the document. The document focuses on the software processing and display, not a comparative effectiveness study with human readers assisted by AI.

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

• The document implies a standalone performance (algorithm only) as it describes the software's ability to process BOLD MRI sequences and display results as parametric images. However, it does not provide specific performance metrics for this standalone function to prove it meets certain acceptance criteria. The assessment is based on verification and validation according to internal BrainLAB procedures.

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

• Not specified in the document.

8. The sample size for the training set

• Not specified in the document.

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

• Not specified in the document.

Summary of what is present:

• Device Name: iPlan BOLD MRI
• Manufacturer: BrainLAB AG
• Intended Use: To prepare image data based on BOLD MRI scan studies and display the result as parametric images. This information, when interpreted by a trained physician or surgeon, may be used with other anatomical information for planning and image-guided surgery.
• Device Description: Software used for processing BOLD MRI sequences and displaying calculation results. Visualizes slight MRI susceptibility changes between images as parametric images.
• Basis for Approval: Substantial equivalence to predicate device iPlan (K041703). The device underwent internal verification and validation procedures by BrainLAB to prove safety and effectiveness.

The 510(k) summary provides the regulatory and intended use context but lacks the detailed study results and performance metrics that would typically be found in a clinical trial report or a more comprehensive technical document.

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iPlan!'s indications for use is to prepare and present patient and image data based on CT, MR, X-ray (Fluoro), including - image preparation - - image fusion - image segmentation - where the result is used for the creation of treatment plans for Stereotactic Surgery: - Surgery Planning - BrainMAP - Functional Planning In addition iPlan!'s indications for use is to prepare and present patient and image data based on CT, MR, X-ray (Fluoro) including - image preparation - - image fusion - image segmentation - where the result is preplanned data to be used by other BrainLAB medical devices such as VectorVision (for performing the planned treatment) where these medical devices are used for: Image Guided Surgery BrainLAB's Image Guided Surgery system is intended to be an intraoperative image guided localization system to enable minimally invasive surgery where the image guided surgery system is indicated for any medical condition in which the use of stereotactic surgery may be considered to be appropriate and where a reference to a rigid anatomical structure, such as the skull, a long bone, or verfebra, can be identified relative to a CT, X-ray or MR based model of the anatomy. Example procedures include but are not limited to: - Cranial procedures - -Spine procedures - -ENT procedures Additional Indications For Use: iPlan! FiberTracking's indication for use is to prepare and present patient and image data based on MRI scanned with diffusion-weighted sequences. These diffusion images are used for the calculation and display of fiber bundles in a selected region of interest. The created treatment plans of iPlan! FiberTracking can be used with other iPlan! treatment plans and other BrainLAB medical devices such as VectorVision, where this medical device is used for image guided surgery.

iPlan! FiberTracking is developed to enhance functionality of iPlan! Cranial software with the import and planning of diffusion tensor images (DTI). Additional to the basic functions of iPlan! (viewing, drawing, image fusion and planning) this application provides functions for the import and display MRI anisotropic data, image processing of the DTI data and the display of the calculated fiber tracts.

The provided 510(k) summary for iPlan! FiberTracking does not contain explicit acceptance criteria or a detailed study proving the device meets said criteria.

The document states:
"iPlan! has been verified and validated according to BrainLAB's procedures for product design and development. The validation proves the safety and effectiveness of the information provided by BrainLAB in this 510(k) application was found to be substantially equivalent with the predicate device iPlan! (K020631)."

This statement indicates that a validation process was undertaken, but it does not specify the acceptance criteria used for the validation or details of the study itself. Without this information, I cannot complete the requested tables and descriptions.

Therefore, the following information cannot be extracted from the provided text:

• Table of Acceptance Criteria and Reported Device Performance: This would require specific performance metrics and thresholds, which are not present.
• Sample size used for the test set and data provenance: No information on the dataset used for testing.
• Number of experts used to establish the ground truth for the test set and qualifications of those experts: Not specified.
• Adjudication method for the test set: Not mentioned.
• 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: This is a software component of a planning system; such a study is not typically described in this context for this type of device, and no such study is mentioned.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not specified.
• The type of ground truth used: Not specified.
• The sample size for the training set: Not mentioned.
• How the ground truth for the training set was established: Not mentioned.

Ask a specific question about this device