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The Proximity Anterior Cervical Plate System is intended for anterior screw fixation to the cervical spine (C2-T1) for the following indications: degenerative disc disease (as defined by neck pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies), trauma (including fractures), tumors, deformity (kyphosis, lordosis or scoliosis), pseudarthrosis, failed previous fusions, spondylolisthesis, and spinal stenosis.

The Segmental Plating System (SPS) is intended for anterior screw fixation to the cervical spine (C2-T1) for the following indications: degenerative disc disease (as defined by neck pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies), trauma (including fractures), tumors, deformity (kyphosis, lordosis or scoliosis), pseudarthrosis, failed previous fusions, spondylolisthesis, and spinal stenosis.

The subject Alphatec Plating Systems consists of two anterior cervical plate subsystems, Proximity Anterior Cervical Plate System and Segmental Plating System, intended for anterior fixation to the cervical spine. The Alphatec Plating Systems consist of a variety of sizes of plates and screws that are manufactured from titanium alloy conforming to ASTM F136. The systems offer instrumentation for the delivery of the plate and screw constructs. The instruments in this system are intended for use in surgical procedures. The Alphatec Plating Systems implants are provided either terminally sterile or non-sterile to be steam sterilized by the end user.

I apologize, but the provided text from the FDA 510(k) clearance letter for the Alphatec Spine Proximity™ Anterior Cervical Plate System does not contain any information regarding acceptance criteria or a study that proves the device meets those criteria for software-based AI/ML devices.

The document details:

• Device Type: Spinal Intervertebral Body Fixation Orthosis (a physical implantable device, not a software/AI device).
• Regulations: Primarily related to medical devices, specifically orthopedic implants.
• Performance Data: Lists non-clinical (mechanical) testing based on ASTM standards (e.g., Static and Dynamic Compression Bending, Static Screw Push-out) and sterilization/packaging validations.
• Substantial Equivalence: Compares the mechanical design and indications for use of the Proximity™ system to predicate physical spinal fixation devices.

Therefore, I cannot extract the information required by your prompts (acceptance criteria table, sample sizes for AI test/training sets, expert ground truth establishment, MRMC studies, standalone performance, etc.) because these concepts are not applicable to the type of device described in this 510(k) clearance letter. The letter pertains to a traditional, physical Class II medical device, not a software-based or AI/ML-driven diagnostic or therapeutic tool.

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CASIA2 is a non-contact, high-resolution tomographic and biomicroscopic device intended for the in vivo imaging and measurement of ocular structures in the anterior segment. CASIA2 measures corneal thickness, anterior chamber depth and lens thickness.

The Tomey Cornea/Anterior Segment OCT CASIA2 (CASIA2) is a non-contact, high resolution tomographic and biomicroscopic device indicated for in vivo imaging of ocular structures in the anterior segment. The Tomey Cornea/Anterior Segment OCT CASIA2 is indicated as an aid in the visualization and measurement of anterior segment findings. CASIA2 measures corneal thickness, anterior chamber depth and lens thickness.

This medical device product has functions subject to FDA premarket review (corneal thickness, curvature, anterior chamber depth and lens thickness) as well as functions that are not subject to FDA premarket review. For this application, for the (510(k) exempt functions that are not subject to FDA premarket review, FDA assessed those functions only to the extent that they either could adversely impact the safety and effectiveness of the overall device.

CASIA2 consists of several components: the main unit, AC input power source, a touch panel LCD monitor, an external hard drive (HDD), a mouse and a keyboard.

Here’s a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter for the Tomey CASIA2.

Device: Tomey Cornea/Anterior Segment OCT (CASIA2)
Measurements evaluated: Central Corneal Thickness (CCT), Anterior Chamber Depth (ACD), and Lens Thickness (LT).

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state pre-defined quantitative acceptance criteria (e.g., "CCT agreement must be within X µm"). Instead, it focuses on demonstrating agreement and precision compared to a legally marketed reference device (LENSTAR LS900). The "acceptance criteria" can be inferred from the study's objective to show substantial equivalence through these performance metrics. The reported device performance is presented as the actual agreement (mean difference and 95% Limits of Agreement - LOA) and precision (Repeatability and Reproducibility %CV).

Therefore, the table below reflects the demonstrated performance and implicitly what was considered acceptable for substantial equivalence.

Note on "Acceptance Criteria": The document implies that meeting or exceeding the performance of the LS900 in terms of precision, and demonstrating good agreement via Bland-Altman analysis, constituted the acceptance for substantial equivalence.

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

• Sample Size for Test Set: A total of 224 subjects were enrolled and completed the study for precision and agreement testing.
  • 55 subjects in the normal group
  • 60 subjects in the cataract group
  • 109 subjects in the special eyes group (eyes without a natural lens or eyes containing artificial materials)
    N for specific analyses (e.g., Agreement Analysis for LT) varied based on acceptable scans (e.g., N=122 for LT agreement, N=138 for CCT/ACD precision, N=76 for LT precision).
• Data Provenance: The document does not explicitly state the country of origin. It indicates "The subjects of this study had no notable or unexpected/untoward assessments..." which suggests a single clinical site. However, no specific country is mentioned.
• Retrospective or Prospective: The study was a prospective clinical study, as subjects were "enrolled," "randomized," and "assigned" to configurations and sequences, and data was collected during the study.

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

• The document implies that the LENSTAR LS900 device itself served as the reference standard for establishing "ground truth" (or more accurately, the comparator for agreement) for the measured parameters.
• It states that "The clinical site had 3 device operators trained on the devices used in the study."
• Qualifications of Experts: The specific qualifications (e.g., radiologist, ophthalmologist, optometrist expertise, years of experience) of these 3 device operators are not explicitly stated in the provided text.

4. Adjudication Method for the Test Set

• The document states, "Additional scans were taken at the operator's discretion if image quality was unacceptable based on the device DFU and the Tomey CASIA2 Reference Guide and included, missing scans, truncated scans, image defocus, floaters, presence of eye blinks, eye motion, etc. Each device operator had up to 3 attempts to obtain an acceptable scan for each of the required scans."
• This suggests an operational approach to ensure data quality rather than a formal, independent adjudication process (e.g., 2+1/3+1 consensus by experts) for the measurements themselves. The "ground truth" was derived from the in-device measurements of the LS900, not a separate expert review. Therefore, there was no expert consensus-based adjudication method for the measurements.

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

• No, a traditional MRMC comparative effectiveness study was not done.
• The study design was focused on device-to-device agreement and precision (CASIA2 vs. LENSTAR LS900) rather than evaluating how human readers' performance (e.g., diagnostic accuracy) improved with or without AI assistance.
• The CASIA2 is described as a "tomographic and biomicroscopic device intended for the in vivo imaging and measurement of ocular structures," with software providing "quantitative outputs." It does not appear to be an AI-assisted diagnostic aid for image interpretation that would typically require an MRMC study to show human reader benefit.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance)

• The study primarily assessed the measurement performance of the CASIA2 device (algorithm/system) in generating quantitative outputs (CCT, ACD, LT) and compared these directly with a reference device. It's implied that these measurements are generated automatically by the device's software.
• The role of the human operators was to acquire an "acceptable scan" based on predefined image quality criteria, not to interpret the images or provide a human "answer" for comparison with an AI-generated reading.
• Therefore, the precision and agreement studies essentially represent the standalone performance of the CASIA2's measurement capabilities compared to the LS900.

7. Type of Ground Truth Used

• The "ground truth" (or clinical reference standard) for comparison was the measurements obtained from the legally marketed predicate/reference device, LENSTAR LS900.
• This is a device-based comparative ground truth, not expert consensus, pathology, or outcomes data. The study aimed to show that the CASIA2's measurements were interchangeable or highly agreeable with those from an established, cleared device.

8. Sample Size for the Training Set

• The document describes a clinical study for validation/testing of the updated software. It does not provide any information about a separate training set size for the development of the algorithms generating these quantitative measurements.
• It only mentions: "The device is a software upgraded version of the predicate K213265 that provides quantitative measurements. All quantitative measurements are derived from OCT images acquired with optical coherence tomography." This implies the software update incorporated algorithms to derive these measurements, but details on their development (including training data) are not provided in this 510(k) summary.

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

• As the document does not describe the training set or its development, there is no information provided on how the ground truth for any training set was established.

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The device is intended for radiation treatment planning for use in stereotactic, conformal, computer planned, Linac based radiation treatment and indicated for cranial, head and neck and extracranial lesions.

RT Elements are computed-based software applications for radiation therapy treatment planning and dose optimization for linac-based conformal radiation treatments, i.e. stereotactic radiosurgery (SRS), fractionated stereotactic radiotherapy (SRT) or stereotactic ablative radiotherapy (SABR), also known as stereotactic body radiation therapy (SBRT) for use in stereotactic, conformal, computer planned, Linac based radiation treatment of cranial, head and neck, and extracranial lesions.

The device consists of the following software modules: Multiple Brain Mets SRS 4.5, Cranial SRS 4.5, Spine SRS 4.5, Cranial SRS w/ Cones 4.5, RT Contouring 4.5, RT QA 4.5, Dose Review 4.5, Brain Mets Retreatment Review 4.5, and Physics Administration 7.5.

Here's the breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter for RT Elements 4.5, specifically focusing on the AI Tumor Segmentation feature:

Acceptance Criteria and Reported Device Performance

Note: For "Gliomas and glio-/neuronal tumors," the reported lower bound 95% CI for Recall (0.74) is slightly below the stated acceptance criteria of 0.8. Additional clarification from the submission would be needed to understand how this was reconciled for clearance. However, for all other categories and overall, the reported performance meets or exceeds the acceptance criteria.

Study Details for AI Tumor Segmentation

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

• Sample Size: 412 patients (595 scans, 1878 annotations)
• Data Provenance: De-identified 3D CE-T1 MR images from multiple clinical sites in the US and Europe. Data was acquired from adult patients with one or multiple contrast-enhancing tumors. ¼ of the test pool corresponded to data from three independent sites in the USA.

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

• Number of Experts: Not explicitly stated as a number, but referred to as an "external/independent annotator team."
• Qualifications of Experts: US radiologists and non-US radiologists. No further details on years of experience or specialization are provided in this document.

4. Adjudication method for the test set:

• The document mentions "a well-defined data curation process" followed by the annotator team, but it does not explicitly describe a specific adjudication method (e.g., 2+1, 3+1) for resolving disagreements among annotators.

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 comparing human readers with and without AI assistance was not reported for the AI tumor segmentation. The study focused on standalone algorithm performance against ground truth.

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

• Yes, a standalone performance study was done. The validation was conducted quantitatively by comparing the algorithm's automatically-created segmentations with the manual ground-truth segmentations.

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

• Expert Consensus Segmentations: The ground truth was established through "manual ground-truth segmentations, the so-called annotations," performed by the external/independent annotator team of radiologists.

8. The sample size for the training set:

• The sample size for the training set is not explicitly stated in this document. The document mentions that "The algorithm was trained on MRI image data with contrast-enhancing tumors from multiple clinical sites, including a wide variety of scanner models and patient characteristics."

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

• How the ground truth for the training set was established is not explicitly stated in this document. It can be inferred that it followed a similar process to the test set, involving expert annotations, but the details are not provided.

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DrAid™ for Liver Segmentation is a web-based software, non-invasive image analysis application designed for the visualization, evaluation, and reporting of liver and physician identified lesions using multiphase images (with slice thickness

DrAid™ for Liver Segmentation is a web-based software that processes and analyzes multiphase CT images in DICOM format. The software utilizes AI algorithms for semi-automated liver segmentation, combined with manual editing capabilities. Additionally, the device provides tools for manual segmentation with user input of seed points and boundary editing for physician-identified lesions within the liver.

Key device components:

• AI algorithm for liver segmentation
• Measurement algorithm
• DICOM Processing Module for CT images
• Liver Segmentation viewer
• Results Export Module
• Device Characteristics:
• Software

Environment of Use:

• Healthcare facility/hospital

Key Features for SE/Performance:

• Visualization modes:
• Original DICOM 2D image viewing
• -MPR visualization
• -Manual correction tools:
• Seed point placement
• Boundary editing for lesions
• Segmentation refinement
• Reporting tool.
• Energy Source:
• -Web-based application running on standard hospital/clinic workstations

Here's a breakdown of the acceptance criteria and the study details for the DrAid™ for Liver Segmentation device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. 95% CI lower bound of Dice scores ≥ 0.90
3. 95% CI upper bound of HD95 score ≤ 4.0 | 1) Dice score:
   • Mean ± std: 0.9649 ± 0.0195
   • 95% CI Dice: 0.9649 [0.9631, 0.9667]
4. HD95:
   • Mean ± std: 1.7061 ± 1.5800
   • 95% CI: 1.7061 [1.5595, 1.8526] |
     | Liver volume measurement | 95% CI upper bound of Volume Error ≤ 5% | NVE (Normalized Volume Error):
     Mean ± std: 2.7269 % ± 3.1928 %
     95% CI: [2.4308 %, 3.0230 %] |

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

• Sample Size: 450 contrast-enhanced CT scans. These scans were from 150 patients.
• Data Provenance:
  • Country of Origin: US medical institutions (USA).
  • Retrospective/Prospective: Not explicitly stated, but typically these types of validation studies on existing datasets are retrospective.

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

• Number of Experts: 3
• Qualifications of Experts: US board-certified radiologists.

4. Adjudication Method for the Test Set

The document mentions that the ground truth was established by "annotations provided by 3 US board-certified radiologists," but it does not specify an adjudication method (e.g., 2+1, 3+1, majority vote, etc.). It implies that the annotations from these three radiologists collectively formed the ground truth, but the process for resolving discrepancies among them is not detailed.

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

• No, an MRMC comparative effectiveness study was not done. The study focuses on evaluating the standalone performance of the AI algorithm against expert-created ground truth. There is no information provided about comparing human readers' performance with and without AI assistance or any effect size for such an improvement.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Yes, a standalone study was performed. The reported performance metrics (Dice score, HD95, NVE) are for the DrAid™ liver segmentation algorithm itself, evaluated against the established ground truth. The device is described as having "semi-automated quantitative imaging function, utilizing an AI algorithm to generate liver segmentation that is then editable by the physician if necessary," but the performance data presented is for the initial AI segmentation without physician editing.

7. Type of Ground Truth Used

• Expert Consensus (or Expert Annotation): The ground truth was established by "annotations provided by 3 US board-certified radiologists." This falls under expert consensus/annotation.

8. Sample Size for the Training Set

• The sample size for the training set is not provided in the document. The text only describes the test set (450 CT scans from 150 patients).

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

• The document does not provide information on how the ground truth for the training set was established. It only details the ground truth establishment for the independent test set.

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Contour+ (MVision Al Segmentation) is a software system for image analysis algorithms to be used in radiation therapy treatment planning workflows. The system includes processing tools for automatic contouring of CT and MR images using machine learning based algorithms. The produced segmentation templates for regions of interest must be transferred to appropriate image visualization systems as an initial template for a medical professional to visualize, review, modify and approve prior to further use in clinical workflows.

The system creates initial contours of pre-defined structures of common anatomical sites, i.e., Head and Neck, Brain, Breast, Lung and Abdomen, Male Pelvis, and Female Pelvis.

Contour+ (MVision Al Segmentation) is not intended to detect lesions or tumors. The device is not intended for use with real-time adaptive planning workflows.

Contour+ (MVision Al Segmentation) is a software-only medical device (software system) that can be used to accelerate region of interest (ROI) delineation in radiotherapy treatment planning by automatic contouring of predefined ROIs and the creation of segmentation templates on CT and MR images.

The Contour+ (MVision Al Segmentation) software system is integrated with a customer IT network and configured to receive DICOM CT and MR images, e.g., from a CT or MRI scanner or a treatment planning system (TPS). Automatic contouring of predefined ROIs is performed by pre-trained, locked, and static models that are based on machine learning using deep artificial neural networks. The models have been trained on several anatomical sites, including the brain, head and neck, bones, breast, lung and abdomen, male pelvis, and female pelvis using hundreds of scans from a diverse patient population. The user does not have to provide any contouring atlases. The resulting segmentation structure set is connected to the original DICOM images and can be transferred to an image visualization system (e.g., a TPS) as an initial template for a medical professional to visualize, modify and approve prior to further use in clinical workflows.

The provided text does not include a table of acceptance criteria and the reported device performance, nor does it specify the sample sizes used for the test set, the number of experts for ground truth, or details on comparative effectiveness studies (MRMC).

However, based on the available information, here is a description of the acceptance criteria and study details:

Acceptance Criteria and Study for Contour+ (MVision AI Segmentation)

The study evaluated the performance of automatic segmentation models by comparing them to ground truth segmentations using Dice Score (DSC) and Surface-Dice Score (S-DSC@2mm) as metrics. The acceptance criteria were based on a "set level of minimum agreement against ground truth segmentations determined through clinically relevant similarity metrics DSC and S-DSC@2mm." While specific numerical thresholds for these metrics are not provided, the submission states that the device fulfills "the same acceptance criteria" as the predicate device.

It's important to note that the provided document is an FDA 510(k) clearance letter and not the full study report. As such, it summarizes the findings and affirms the device's substantial equivalence without detailing every specific test result or acceptance threshold.

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

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: The exact sample size for the test (golden) dataset is not specified, but it's referred to as "various subsets of the golden dataset" and chosen to "achieve high granularity in performance evaluation tests."
• Data Provenance: The datasets originate from "multiple EU and US clinical sites (with over 50% of data coming from US sites)." It is described as containing "hundreds of scans from a diverse patient population," ensuring representation of the "US population and medical practice." The text does not explicitly state if the data was retrospective or prospective, but the description of "hundreds of scans" from multiple sites suggests it is likely retrospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

The number of experts used to establish the ground truth for the test set is not specified in the provided text. The qualifications are vaguely mentioned as "radiotherapy experts" who performed "Performance validation of machine learning-based algorithms for automatic segmentation." No specific years of experience or board certifications are detailed.

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

The adjudication method for establishing ground truth on the test set is not specified in the provided text. The text only states that the auto-segmentations were compared to "ground truth segmentations."

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

A multi-reader multi-case (MRMC) comparative effectiveness study focusing on the improvement of human readers with AI assistance versus without AI assistance is not explicitly described in the provided text.

The text states: "Performance validation of machine learning-based algorithms for automatic segmentation was also carried out by radiotherapy experts. The results show that Contour+ (MVision AI Segmentation) assists in reducing the upfront effort and time required for contouring CT and MR images, which can instead be devoted by clinicians on refining and reviewing the software-generated contours." This indicates that experts reviewed the output and perceived a benefit in efficiency, but it does not detail a formal MRMC study comparing accuracy or time, with a specific effect size.

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

Yes, a standalone performance evaluation of the algorithm was conducted. The primary performance metrics (DSC and S-DSC@2mm) were calculated by directly comparing the "produced auto-segmentations to ground truth segmentations," which is a standalone assessment of the algorithm's output. The statement "Performance verification and validation results for various subsets of the golden dataset show the generalizability and robustness of the device" further supports this.

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

The ground truth used was expert consensus segmentations. The text repeatedly refers to comparing the device's output to "ground truth segmentations" established by "radiotherapy experts." There is no mention of pathology or outcomes data being used for ground truth.

8. The sample size for the training set

The exact sample size for the training set is not specified, but the models were "trained on several anatomical sites... using hundreds of scans from a diverse patient population."

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

The text states that the machine learning models were "trained on several anatomical sites... using hundreds of scans from a diverse patient population." While it doesn't explicitly detail the process for establishing ground truth for the training set, it is implied to be through expert contouring/segmentation, as the validation uses "ground truth segmentations" which are established by "radiotherapy experts." Given the extensive training data required for machine learning, it's highly probable that these "hundreds of scans" also had expert-derived segmentations as their ground truth for training.

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Segmental Plating System (SPS)

The Segmental Plating System (SPS) is intended for anterior screw fixation to the cervical spine (C2-T1) for the following indications: degenerative disc disease (as defined by neck pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies), trauma (including fractures), tumors, deformity (kyphosis or scoliosis), pseudarthrosis, failed previous fusions, spondylolisthesis, and spinal stenosis.

IdentiTi SPS Interbody System

The IdentiTi SPS Interbody System is an anterior cervical interbody fusion system intended for spinal fusion procedures in skeletally mature patients with cervical disc degeneration and/or cervical spinal instability, as confirmed by imaging studies (radiographs, CT, MRI), that results in radiculopathy, myelopathy, and/or pain at multiple contiguous levels from C2-T1. The IdentiTi SPS Interbody System is intended for use with supplemental fixation systems. The system is designed for use with autograft, allograft comprised of cortical, cancellous, and/or corticocancellous bone graft, demineralized allograft with bone marrow aspirate, or a combination thereof.

IdentiTi NanoTec SPS Interbody System

The IdentiTi SPS Interbody System with advanced NanoTec surface treatment is an anterior cervical interbody fusion system intended for spinal fusion procedures in skeletally mature patients with cervical disc degeneration and/or cervical spinal instability, as confirmed by imaging studies (radiographs, CT, MRI), that results in radiculopathy, and/or pain at multiple contiguous levels from C2-T1. The IdentiTi NanoTec SPS Interbody System is intended for use with supplemental fixation systems. The system is designed for use with autograft, allograft comprised of cortical, cancellous, and/or corticocancellous bone graft, demineralized allograft with bone marrow aspirate, or a combination thereof.

Transcend SPS Interbody System

The Transcend SPS Interbody System is an anterior cervical interbody fusion system intended for use in skeletally mature patients with cervical disc degeneration and/or cervical spinal instability, as confirmed by imaging studies (radiographs, CT, MRI), that results in radiculopathy, and/ or pain at multiple contiguous levels from C2-T1. The Transcend SPS Interbody System is intended for use with supplemental fixation systems. The system is designed for use with autograft comprised of cortical, cancellous and/or corticocancellous bone graft, demineralized allograft with bone marrow aspirate, or a combination thereof.

Transcend NanoTec SPS Interbody System

The Transcend SPS PEEK Interbody System with advanced NanoTec surface treatment is an anterior cervical interbody fusion system intended for use in skeletally mature patients with cervical disc degeneration and/or cervical spinal instability, as confirmed by imaging studies (radiographs. CT, MRI), that results in radiculopathy, and/or pain at multiple contiguous levels from C2-T1. The Transcend NanoTec SPS Interbody System is intended for use with supplemental fixation systems. The system is designed for use with autograft, allograft comprised of cortical, cancellous and/or corticocancellous bone graft, demineralized allograft with bone marrow aspirate, or a combination thereof.

The Segmental Plating System (SPS) is intended for anterior fixation to the cervical spine. The Segmental Plating System (SPS) consists of a variety of sizes of 2 - 4 holes plates and 3.5 mm and 4.0 mm screws that are manufactured from titanium alloy conforming to ASTM F136 and are offered non-sterile. The plate includes a screw anti-backout mechanism. The system will offer instrumentation for the delivery of the plate and screw construct. The instruments in this system are intended for use in surgical procedures. The plate system implants are provided non-sterile to be steam sterilized by the end user.

The IdentiTi and Transcend SPS Interbody Systems are cervical intervertebral body fusion systems designed to be inserted through anterior surgical approaches. The interbody spacers are manufactured from PEEK (polyetheretherketone) Optima LT1 per ASTM F2026, tantalum per ASTM F560, commercially pure titanium (CP Ti Grade 2) per ASTM F67, and an optional hydroxyapatite nano (HAMM) surface treatment. The subject system implants consist of various lengths, widths, heights and lordotic options to accommodate individual patient anatomy. To mitigate risk of expulsion, the interbody endplates feature teeth. All interbody spacers feature an internal graft aperture for placement of graft material to promote fusion through the cage. Additionally, the IdentiTi implants are offered with a microstructure due to the layering of material that forms the porous architecture. This porous geometry extends to the superior and inferior surfaces of the device for implant fixation. The subject IdentiTi and Transcend NanoTec SPS Interbody Systems interbody implant surfaces have been treated with a 20-40 nanometer thin hydroxyapatite (HA) surface treatment. The surface treatment presents a nano-scale topography on the entirety of the implant surface. in addition to macro-/micro-scale topography existing from prior to HA man treatment. The interbody spacers are provided individually packaged and sterile.

The provided text is a 510(k) summary for a medical device (Alphatec Spine Inc.'s Segmental Plating System and Interbody Systems). It discusses regulatory clearance based on substantial equivalence to predicate devices, outlines the device's description, indications for use, and a technological comparison. It also lists performance data from non-clinical testing.

However, the provided text does not contain information about acceptance criteria for an AI/ML medical device, nor does it describe a study involving a test set, ground truth determination, expert consensus, or human-in-the-loop performance evaluation. The document primarily focuses on the mechanical and material aspects of spinal implants and their equivalence to existing devices, with performance data relating to mechanical testing standards (e.g., ASTM F2077, F2267, F1717).

Therefore, I cannot fulfill the request to describe the acceptance criteria and the study that proves the device meets them based on the provided text, as this information is not present. The device in question is a physical implant, not an AI/ML-based diagnostic or therapeutic device.

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Axial3D Cloud Segmentation Service is intended for use as a cloud-based service and image segmentation Framework for the transfer of DICOM imaging information from a medical scanner to an output file, which can be used for the fabrication of physical replicas of the output file using additive manufacturing methods.

The output file or physical replica can be used for treatment planning and/or diagnostic purposes in the field of orthopedic, maxillofacial, and cardiovascular applications in adults. The or physical replica may also be used for pediatrics between the ages of 12 and 21 years of age in cardiovascular applications.

Axial3D Cloud Segmentation Service should be used with other diagnostic tools and expert clinical judgment.

Axial3D Cloud Segmentation Service is a secure, highly available cloud-based image processing, segmentation, and 3D modelling framework for the transfer of imaging information either as a digital file or as a 3D printed physical model.

This document describes the Axial3D Cloud Segmentation Service and its FDA clearance.

Here's an analysis of the acceptance criteria and study data based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state the sample size used for the test set in terms of the number of patient cases or images. It mentions "nonclinical testing" and "software design verification and validation testing on all three software components of the device."

The data provenance (country of origin, retrospective/prospective) is not specified in the provided text.

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

The document does not specify the number of experts used or their qualifications for establishing ground truth for the test set.

4. Adjudication Method for the Test Set

The adjudication method used for the test set is not specified in the provided text.

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

The document does not mention an MRMC comparative effectiveness study or any effect size related to human readers improving with AI vs. without AI assistance. The device is a "segmentation framework" and its output (digital file or 3D printed model) is to be "used in conjunction with other diagnostic tools and expert clinical judgment." This suggests the device's role is preparatory rather than directly diagnostic in an unassisted workflow.

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

Yes, a standalone performance assessment was conducted for the algorithm's segmentation accuracy. The "measurement accuracy and comparisons were performed and confirmed to be within specification of +/- 0.7mm," indicating an evaluation of the algorithm's output against a reference.

7. The Type of Ground Truth Used

The type of ground truth used is not explicitly stated. However, given the measurement accuracy reported ("+/- 0.7mm") for segmentation, it implies a comparison against a highly precise reference, likely either a gold standard segmentation created by expert manual contouring or a known physical dimension.

8. The Sample Size for the Training Set

The sample size used for the training set is not specified in the provided text.

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

How the ground truth for the training set was established is not specified in the provided text.

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aprevo® Digital Segmentation software is intended to be used by trained, medically knowledgeable design personnel to perform digital image segmentation of the spine, primarily lumbar anatomy. The device inputs DICOM images and outputs a 3-D model of the spine.

The device is a software medical device that will use DICOM images as input and provide 3D model of the spine structure. Pre-processing will be performed on the uploaded DICOM files to filter soft tissue and identifying spine. Upon removal of soft tissue and identification of spine structure, the software will utilize an AI-based algorithm to segment the structure and render a 3D model as an output.

The provided text describes the acceptance criteria and the study that proves the device meets those criteria for the "aprevo® Digital Segmentation" software.

Here's a breakdown of the requested information:

Acceptance Criteria and Device Performance

Study Details:

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

   • Test Set Sample Size: Not explicitly stated in the provided text, but it mentions that "Independent training and validation datasets were selected to ensure model performance would reflect real clinical performance" and "Validation datasets represented diversity in populations and equipment."
   • Data Provenance: Not explicitly stated, however, the phrase "diversity in populations and equipment" suggests data from various sources but does not specify countries of origin. The study was a "Non-Clinical Testing" which implies retrospective data.

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

   • Not specified. The document states that the ground truth for the algorithm was used for model performance evaluation, but does not detail how this ground truth was established, or the number/qualifications of experts involved.

3. Adjudication Method for the Test Set:

   • Not specified.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • If done: No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or indicated. The document states "CLINICAL TESTING: Not applicable." The study solely focuses on the standalone performance of the software.
   • Effect size of human readers improvement: Not applicable, as no MRMC study was conducted.

5. Standalone Performance (Algorithm only without human-in-the-loop):

   • If done: Yes, a standalone performance evaluation was done. The "NON-CLINICAL TESTING" section describes the evaluation of the "software performance" using IOU and accuracy metrics for segmentation and labeling, without human intervention in the reported performance metrics.

6. Type of Ground Truth Used:

   • The type of ground truth used is not explicitly stated as expert consensus, pathology, or outcomes data. However, for "segmentation" and "vertebral body labeling," the ground truth would typically be established by expert annotation or a similar gold standard, refined through a consensus process, but this is not detailed.

7. Sample Size for the Training Set:

   • Not explicitly stated. It only mentions that "Independent training and validation datasets were selected to ensure model performance would reflect real clinical performance."

8. How the Ground Truth for the Training Set Was Established:

   • Not explicitly stated. The document mentions that "Independent training and validation datasets were selected," but does not elaborate on the method used to establish the ground truth for the training data (e.g., expert annotations, manual segmentation).

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EFAI HCAPSeg is a software device intended to assist trained radiation oncology professionals, including, but not limited to, radiation oncologists, medical physicists, and dosimetrists, during their clinical workflows of radiation therapy treatment planning by providing initial contours of organs at risk on non-contrast CT images. EFAI HCAPSeg is intended to be used on adult patients only.

The contours are generated by deep-learning algorithms and then transferred to radiation therapy treatment planning systems. EFAI HCAPSeg must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results generated. EFAI HCAPSeg is not intended to be used for decision making or to detect lesions.

EFAI HCAPSeg is an adjunct tool and is not intended to replace a clinician's judgment and manual contouring of the normal organs on CT. Clinicians must not use the software generated output alone without review as the primary interpretation.

EFAI RTSuite CT HCAP-Segmentation System, herein referred to as EFAI HCAPSeg, is a standalone software that is designed to be used by trained radiation oncology professionals to automatically delineate organs-at-risk (OARs) on CT images. This auto-contouring of OARs is intended to facilitate radiation therapy workflows.

The device receives CT images in DICOM format as input and automatically generates the contours of OARs, which are stored in DICOM format and in RTSTRUCT modality. The device does not offer a user interface and must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results. Once data is routed to EFAI HCAPSeg, the data will be processed and no user interaction is required, nor provided.

The deployment environment is recommended to be in a local network with an existing hospital-grade IT system in place. EFAI HCAPSeg should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer:

• Local network setting of input and output destinations;
• Presentation of labels and their color; ●
• Processed image management and output (RTSTRUCT) file management. ●

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

Acceptance Criteria and Device Performance

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