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Segmentron Viewer is a software product intended for processing and manipulating maxillofacial radiographic images. Segmentron Viewer allows users to perform the following functions:

1. Viewing patient images (provides tools for image processing and viewing functions);
2. Reading and 3D visualization of CBCT images;
3. Generating editable 3D STL files (for educational purposes only).

The device is indicated for use by medical professionals (such as dentists and radiologists), in patients 14 years and older with permanent teeth.

Segmentron Viewer is a web application. It can be used in a network environment.

Segmentron Viewer is a semi-automated software as a medical device (SaMD) for dental image processing and management. The device's main function is to perform automated analysis of maxillofacial Cone Beam Computed Tomography (CBCT) images uploaded by the user, which consists of applying artificial neural network models (AI) to such images to obtain automatically generated 3D segmentations of teeth and anatomy. The user is able to edit these segmentations. The device also provides functions for enhancement and 3D visualization of the images. It additionally enables uploading, saving, and sharing CBCT images for the clinician's ease of use.

Segmentron Viewer identifies each tooth and tooth pulp present in the upper and the lower jaw (as shown on the input scan), numbers them, and segments them. Similarly, the device identifies each of eight maxillofacial anatomy structures in a CBCT scan, and segments them. The software facilitates navigation through the images for detailed evaluation and produces multi-planar reconstruction (MPR) views of each segmented object. The device generates a segmentation report from the input CBCT scan, for the healthcare provider's (HCP) use to further evaluate a patient's teeth and anatomy.

Here's an analysis of the acceptance criteria and study detailed in the provided FDA 510(k) clearance letter for Segmentron Viewer, organized as requested:

Acceptance Criteria and Device Performance

Device Name: Segmentron Viewer

Note: The FDA summary states that the reported Dice Coefficients exceeded the pre-defined performance goal for Tooth and Pulp Segmentation, and exceeded their respective pre-defined PGs for Anatomy Segmentation. While the specific numerical acceptance criteria (PGs) are not explicitly provided in this summary, the clearance indicates they were met.

Study Details

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

   • Tooth Segmentation: 126 CBCT scans (retrospective)
   • Pulp Segmentation: 43 CBCT scans (retrospective)
   • Anatomy Segmentation: 56 CBCT scans (retrospective)
   • Labeling Performance: 40 CBCT scans (from the larger validation dataset, retrospective)
   • Data Provenance: "sourced from a variety of geographic regions and demographics." It is a retrospective study.

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

   • Number of Experts: Not explicitly stated, but plural ("radiologists") is used in the summary.
   • Qualifications of Experts: U.S. board-certified radiologists. No specific years of experience are mentioned.

3. Adjudication Method for the Test Set:

   • Not explicitly stated. The summary mentions "U.S. board-certified radiologists established a reference standard for each CBCT image." This implies a single consensus or primary expert approach, but does not detail a formal adjudication process like 2+1 or 3+1.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

   • No, a MRMC comparative effectiveness study was not reported. The studies described are "standalone validation studies," focusing on the algorithm's performance against a ground truth.

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

   • Yes, standalone validation studies were performed. The performance data section explicitly states, "DGNCT LLC evaluated the performance of Segmentron Viewer in four retrospective standalone validation studies."

6. The Type of Ground Truth Used:

   • Expert consensus, specifically manual segmentation (for segmentation studies) or annotation (for labeling studies) by U.S. board-certified radiologists.

7. The Sample Size for the Training Set:

   • Not provided in the summary. The summary describes the validation studies but does not detail the training set used for the artificial neural network models.

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

   • Not provided in the summary. The summary mentions "supervised machine learning" for the algorithm, which implies a labeled training set was used, but it does not describe how these labels/ground truth were established for the training data.

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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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Segment 3DPrint is a software for review and segmentation of images from a medical scanner as well as of medical 3D models. Segment 3DPrint is intended to generate 3D models for diagnostic purposes in both paediativ and adult populations in the field of orthopaedic, maxillofacial, and cardiovascular applications. The models can be used for visualisation, measuring, and treatment planning. Output from Segment 3DPrint can be used to fabrical replical replical replical replical replical replical replical replical by use of additive manufacturing methods. Segment 3DPrint is intended to be used by trained professionals in conjunction with expert clinical judgement.

Seqment 3DPrint is a software for review and segmentation of images from a medical scanner as well as of medical 3D models. Segment 3DPrint is intended to generate 3D models for diagnostic purposes. The models can be used for visualisation, measuring, and treatment planning. Output from Segment 3DPrint can be used to fabricate physical replicas, by use of additive manufacturing methods.

Here's an analysis of the acceptance criteria and study details for the Segment 3DPrint device 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

• Test Set for AI Bone Segmentation: 21 data sets.
• Test Set for Print Accuracy (3D Models): 12 models (representing complex structures and worst-case scenarios).
• Data Provenance: Studies were performed in Europe. The text does not specify exact countries or whether the data was retrospective or prospective. It mentions "a great variety of data (such as scanner model, image quality, and anatomy)" was included.

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

The text mentions "agreement between reference segmentation by expert readers." However, it does not specify the number of experts used or their specific qualifications (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set

The text does not explicitly state the adjudication method used for establishing ground truth (e.g., 2+1, 3+1, none). It only refers to "reference segmentation by expert readers."

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance was not explicitly described in the provided text. The studies focused on the standalone performance of the device's segmentation and 3D printing accuracy.

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

Yes, a standalone performance evaluation was largely conducted.

• The "AI bone segmentation algorithm was trained on 20 data sets, and 21 data sets were used for its validation." The reported metrics (Dice, Jaccard, distances) are measures of this algorithm's performance against ground truth.
• The "device validation study validates digital models and 3D models from additive manufacturing" with reported accuracy for the generated models.
• The device is described as a "support tool" for "medically trained professionals" but the performance results are for the algorithm's output directly.

7. The Type of Ground Truth Used

The ground truth for the AI bone segmentation was established by "reference segmentation by expert readers" (expert consensus). For the 3D model accuracy, the text implies that the "validation or application studies using established methods as reference standards" were used, but the exact nature of this reference standard for physical replica accuracy is not fully detailed beyond implying measurement against the physical object vs. the digital model.

8. The Sample Size for the Training Set

• AI Bone Segmentation: 20 data sets.
• 3D Models/Print: The text mentions "The device validation study validates digital models and 3D models... In total 12 models... were printed." This appears to be a validation set rather than a training set for print accuracy itself. The training set for the 3D model generation process is not explicitly stated.

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

The text states that the "AI bone segmentation algorithm was trained on 20 data sets." It can be inferred that the ground truth for these 20 training data sets would have been established in a similar manner to the validation set, likely through "expert readers" creating reference segmentations. However, the document does not explicitly detail the ground truth establishment method for the training set.

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The Segmented Esophageal Stent System is intended for maintaining esophageal luminal patency in esophageal strictures caused by intrinsic, or extrinsic malignant turnors only, and occlusion of concurrent esophageal fistula.

The stent is provided pre-loaded in the delivery system. The stent is made up of several segments. The main frame is woven by Nitinol wire, and the connecting part is PTFE connecting loop. The stent with this segmented structure can flexibly conform to the curvature of the human body lumen, thereby reducing the stimulation of the end to the lumen. Silicone membrane applied to the stent covers the complete body of the stent along both flanges, which means the segmented esophageal stent is a fully covered stent. The stent is formed with a flange at either end. The increased diameter of the stent at the stent ends helps provide resistance to migration. The covering is intended to reduce the risk of tissue ingrowth and provides a occlusion for esophageal fistulas. To aid in visibility under fluoroscopy there are 4 bands at either end of the stent and 2 bands at the middle of the stent. The stent has a retrieval loop which can be used to reposition the stent during the initial placement procedure if desired. The delivery system allows for desheathing to deploy the stent and recapturing the stent during stent deployment. The device is supplied sterile, intended for single use only and is available for prescription use only. Use of this device is restricted to a trained healthcare professional.

The provided document is a 510(k) Summary for the Segmented Esophageal Stent System (K182910), which is a medical device. This document describes the device's technical characteristics and the studies performed to demonstrate its substantial equivalence to a predicate device, not its performance against specific acceptance criteria in a clinical study for an AI/CADe device.

Therefore, the requested information regarding acceptance criteria, device performance, sample sizes, data provenance, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for a study proving the device meets acceptance criteria cannot be extracted from this document, as it pertains to a different type of medical device submission (510(k) for a physical stent, not an AI/CADe device).

The document focuses on demonstrating that the new stent device is substantially equivalent to a previously cleared predicate device (CHOOSTENT™ Covered Esophageal Stent, K072094) based on material, design, manufacturing, and general performance testing (bench tests and biocompatibility).

Although I cannot provide the requested information in the format of an AI/CADe device study, here's a summary of the performance data included in this 510(k) submission, which is primarily focused on demonstrating safety and efficacy for a physical implantable device:

Performance Data Summary (for the physical stent device):

The Segmented Esophageal Stent System underwent various performance tests to demonstrate its substantial equivalence to the predicate device.

Biocompatibility Testing:

• Stent Biocompatibility Testing:
  • Vitro Cytotoxicity
  • Skin Sensitization
  • Irritation
  • Acute Systemic Toxicity
  • Pyrogen
  • Salmonella Reverse Mutation Test
  • In Vitro Mammalian Cell Gene Mutation Test
  • Muscle Implant
• Delivery System Biocompatibility Testing:
  • Vitro Cytotoxicity
  • Skin Sensitization
  • Irritation
  • Acute Systemic Toxicity

Device-Specific Performance Testing (Bench Tests):

• Visual Inspection
• Dimension Testing
• Deployment Force Testing
• Expansion Force Testing
• Compression Force Testing
• Corrosion Testing
• Tensile Strength Testing
• Sterility Testing
• Shelf Life Testing
• MR Compatibility Testing

Compliance Standards:

• ISO 10993-1: 2009 "Biological Evaluation of Medical Devices - Part 1: Evaluation and Testing within a Risk Management Process"
• ISO 11135 "Sterilization of Health Care Products-Ethylene Oxide-Part 1: Requirements for Development, Validation, and Routine Control of Sterilization Processes for Medical Devices"
• FDA's biocompatibility guidance, Use of International Standard ISO-10993-7, "Biological Evaluation of Medical Devices-7: Ethylene Oxide Sterilization Residuals"
• Guidance for the Content of Premarket Notifications for Esophageal and Tracheal Prostheses issued April 28th, 1998 (specifically "VIII Performance testing-Bench")

Conclusion of Testing:
The testing performed demonstrated that the proposed device and predicate device are equivalent in terms of performance characteristics relevant to their intended use (maintaining esophageal luminal patency).

Clinical Study:
No clinical study was included in this submission.

Regarding your specific points (and why they cannot be answered from this document):

1. Table of acceptance criteria and reported device performance: Not applicable for this type of submission. The document relies on equivalence to a predicate device, not predefined performance metrics for an AI/CADe system.
2. Sample size for test set and data provenance: No test set of medical images/data to assess AI performance. The "test sets" would refer to physical prototypes for bench testing.
3. Number of experts for ground truth and qualifications: Not applicable. Ground truth as typically understood for AI/CADe devices (e.g., expert consensus on image findings) is not relevant here.
4. Adjudication method: Not applicable.
5. MRMC comparative effectiveness study: Not applicable. This assesses human reader improvement with AI, which is irrelevant for a physical stent.
6. Standalone performance: Not applicable.
7. Type of ground truth used: Not applicable.
8. Sample size for training set: Not applicable, as there's no AI model being trained.
9. How ground truth for training set was established: Not applicable.

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Segment CMR is a software that display and analyzes medical images in DICOM-format using multi-slice, multi-frame and velocity encoded MR images. Segment CMR provides features for analysis of cardiac function, such as cardiac pumping and blood flow. The ventricular analysis is provided for usage in both pediatric (from newborn) and adult population. Images and associated data analysis can be stored, communicated, rendered, and displayed within the system and across PACS system. The data produced by Segment CMR is intended to support qualified cardiologist, radiologist or other licensed professional healthcare for clinical decision making. It is a support tool that provides relevant clinical data as a resource to the clinician and is not intended to be a source of medical advice or to determine or recommend a course of action or treatment for a patient.

Segment CMR is a software that displays and analyzes multi-slice, multiphase and velocity encoded DICOM compatible medical MR images. Segment CMR provides quantitative measures for analysis of function of the cardiovascular system. The data produced by Segment CMR is intended to be used to support qualified cardiologist, radiologist or other professional healthcare practitioners for clinical decision making. Functional, flow, valve, vessel, and tissue analysis is performed using standardized algorithms and user input. The quantification methods are validated and reproducible. The ventricular analysis is provided for usage in both pediatric (from newborn) and adult population. MR images may be imported from various sources including images stored on portable media, network storage devices, and other vendor systems and supports cardiovascular MR images from all of the major MRI scanner vendors.

The provided text describes the acceptance criteria and a study proving the device meets them for "Segment CMR", a cardiovascular MRI analysis software.

Here's a breakdown of the requested information:

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

The document does not explicitly present a discrete table of acceptance criteria with numerical targets and corresponding device performance metrics. Instead, it makes a general statement about the device's performance relative to reference methods and its predicate device. This is typical for a 510(k) submission where substantial equivalence is demonstrated rather than meeting specific performance thresholds for a novel device.

Acceptance Criteria (Implied from the text):

• Safety and Effectiveness: Segment CMR should be as safe and effective as the predicate devices.
• Agreement with Reference Methods: Values from the evaluated features in Segment CMR should be in good agreement with values from established reference methods.
• No Adverse Events/Complications: No adverse events or complications associated with the device should be observed in studies.
• Acceptable Residual Risk: All identified hazards should be mitigated to accepted levels of residual risk, and the overall risk evaluation should conclude that the risk is acceptable.
• Benefit Outweighs Risk: Specifically for pediatric use, the benefits should outweigh the risks.
• Performance in Accordance with Intended Use: The device performs in accordance with its intended use and similarly to existing cardiovascular MRI image analysis products.
• No Alteration of MRI Data: The device should not alter MRI imaging data in the analytical process.

Reported Device Performance:

• "The results by the studies show that the values from the evaluated features in Segment CMR were in good agreement with values from the reference method." (Page 6)
• "No adverse advents or complications associated with the subject device were observed in the studies." (Page 6)
• "Based on the clinical performance as documented in the performance studies, Segment CMR was found to have a safety and effectiveness profile that is similar to the predicate device." (Page 6)
• "We conclude that the subject device Segment CMR is as safe and effective as the predicate devices." (Page 7)
• "All identified hazards for Segment CMR have been mitigated to accepted levels of the residual risk and the overall risk residual evaluation concluded that the risk of Segment CMR is acceptable." (Page 7)
• "The extension of pediatric use for the left ventricular analysis did not, according to the hazard analysis, increase the overall residual risk and by evaluating safety and effectiveness, the benefits with pediatric use can be considered to outweigh the risks." (Page 7)
• "Segment CMR performs in accordance with its intended use as well as the cardiovascular MRI image analysis products currently on the market." (Page 7)

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 for Test Set: Not explicitly stated in terms of a specific number of cases or patients. The document refers to "studies" in the plural, implying multiple tests and evaluations.
• Data Provenance: "The studies were performed in US or in Europe." The text does not specify whether the data was retrospective or prospective.

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)

• Number of Experts: Not explicitly stated for establishing ground truth.
• Qualifications of Experts: Not explicitly stated. The device is intended to support "qualified cardiologist, radiologist or other licensed professional healthcare practitioners for clinical decision making." This implies that the reference methods or ground truth would be established by such qualified professionals.

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

• Adjudication Method: Not explicitly stated.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study: The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study designed to measure the improvement of human readers with AI assistance. The focus is on the standalone performance of the software in agreement with established methods. The device is presented as a "support tool" that provides data "as a resource to the clinician," implying it does not replace human interpretation but assists it.
• Effect Size of Human Reader Improvement: Not applicable, as no such MRMC study is described.

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

• Standalone Performance: Yes, the performance data presented (e.g., "values from the evaluated features in Segment CMR were in good agreement with values from the reference method") primarily focuses on the standalone performance of the algorithms within the software compared to established reference methods or predicate devices. The software "provides quantitative measures for analysis," suggesting its output is directly compared.

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

• Type of Ground Truth: "establish methods as reference standard." This implies ground truth was based on validated and recognized clinical methods, likely expert interpretations, other validated software, or established measurement techniques. The specific nature (e.g., expert consensus vs. pathology) is not detailed.

8. The sample size for the training set

• Sample Size for Training Set: Not explicitly stated. The document mentions that the predicate device Segment, "on which Segment CMR is built upon, is used by more than 200 research groups," which suggests a large body of data might have been implicitly involved in the development and refinement of the underlying algorithms, but a specific "training set" for this device (Segment CMR) is not quantified.

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

• Ground Truth for Training Set: Not explicitly described. The general statement about "establish methods as reference standard" likely applies to both training and testing, but the process of establishing ground truth specifically for a training set (if one was formally used) is not detailed.

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Segasist P-AC contouring software is a standalone software application for Windows platforms that assists clinicians in generating estimates of the anatomy boundary contours of the prostate gland in Computed Tomography (CT) scans, Magnetic Resonance (MR) images and ultrasound (sonography) scans to aid in patient diagnosis, treatment planning and post-treatment monitoring. The software is intended to be used to provide clinicians with tools to efficiently contour/delineate the prostate gland in volume data and save the results in DICOM and BMP format. The clinician has the ability to use the saved contours directly or import them in other software tools to perform the task at hand.

The clinician retains the ultimate responsibility for making the pertinent diagnosis and patient management decisions based on their standard practices and visual comparison of the individual images. The Segasist P-AC software tool is a compliment to manual contouring techniques.

Segasist P-AC (Segmentation Assistant for Prostate – Auto-Contouring) is a standalone atlas-based segmentation software tool for auto-contouring of the prostate gland from different input image modalities (Computed Tomography (CT) scans, Magnetic Resonance (MR) images, ultrasound scans). The software can read, write and display DICOM images from/to local directories, and offers the possibility of defining regions of interest (ROls) around the prostate gland in order to delineate the prostate for contouring, visual assessment, and size and volume calculation purposes, either manually, or via semi-automated or automated processes.

The Segasist P-AC software is a tool that has been designed and developed to assist clinicians (radiologists, oncologists, medical physicists etc.) in performing contouring/delineation of the prostate gland in images in multiple modalities more efficiently. The software is capable of segmenting the prostate gland in individual slices, in a choice of different modalities, and for any given view (axial, sagittal, or coronal). This is done by requiring some user input (clicks or drawing ROIs).

For volume prostate data. Segasist P-AC calculates the prostate volume in cubic centimeters and displays the contours on each slice. The results (contours) can be saved as DICOM or binary images (BMP), which can be edited/modified at any time, completely dismissed or accepted and saved by the end user.

The efficiency of contouring performed by the Segasist P-AC software may be improved by generating/using an advanced atlas using gold standard images created by the experienced clinician(s). This requires the software to be trained (atlas creation) before being used. The software can be delivered pre-trained with the comprehensive atlas or the end user can generate their own atlas; a well-established practice for atlas-based segmentation software products.

Segasist P-AC also offers a built-in editor, enabling the user to edit, modify, or change the extracted prostate boundaries to their desired configuration based on their medical and clinical knowledge and experience. The results provided by the Segasist P-AC software needs to be approved by the experienced clinician and can always be modified or corrected by him/her. In addition, the end user can delineate the prostate gland manually using the P-AC software, if necessary or desired. As a result, when Segasist P-AC generates a result, the expert user always has the final decision to override the software result, if deemed appropriate in his/her clinical judgment. It is up to the expert user to accept the result without any change, reject it completely and delineate manually, or modify the Segasist P-AC result and then save it. The software does not provide any auto-detection or auto-saving functionalities. Regardless of the accuracy of Segasist P-AC result. it is always the experienced clinician that remains the decision maker regarding the acceptability of the computed segmentation. Therefore, the final decision on diagnosis, treatment and overall management of the patient is not based on the software result.

Segasist P-AC software does not alter the original input images of the prostate gland. nor does it change the final results obtained once approved by the clinical expert.

Segasist P-AC offers several features and functionalities such as, but not limited to:

• . Import/Export DICOM images
• . Saving Contours to DICOM or BMB format
• . Semi-automated Segmentation
• . Auto-Segmentation (fast slice-to-slice auto-segmentation with minimal user interactions)
• . Volume Segmentation and measurement
• . Edge Enhancement (contour enhancement by user controlled edge snapping).
• . Standard Functionalities for Image Visualization (windowing, contrast, brightness, zoom, panning etc)
• . Advanced Functionalities for Contour Editing For Manual Segmentation (drawing, inflating, deflating, shifting, cut & add etc)
• . User access to modify the resulting contours at any time

Here's an analysis of the Segasist Prostate Auto-Contouring Software based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state quantitative acceptance criteria or specific reported device performance metrics (e.g., Dice score, Hausdorff distance, sensitivity, specificity) for the Segasist P-AC software. The document focuses on demonstrating substantial equivalence to predicate devices and describes the software's functionalities and validation approach.

However, based on the text, the implicit acceptance criteria are that the device "works as intended" and "was acceptable for clinical use, and did not introduce any new concerns of safety or effectiveness compared to predicate products or manual contouring of the prostate gland." The performance is reported as meeting these general criteria.

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

• Sample Size for Test Set: The document states that "imported prostate image datasets from the various imaging modalities were used as input for testing of the software functionalities in accordance with the software validation/verification plans." It also mentions "sufficient numbers to support the intended use of the device." However, a specific number for the test set sample size is not provided.
• Data Provenance: The document does not explicitly state the country of origin for the image data used in testing. It only mentions "imported prostate image datasets." It implies these were retrospective clinical test cases, as it refers to "bench testing using imported images from the various imaging modalities" and "clinical test cases."

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

• Number of Experts: The document details the software development process involved "the input and collaboration of experienced and trained professionals, such as radiologists, oncologists or other highly qualified medical clinicians." For the independent testing, it states "independently by experienced and trained medical professionals who are representative of the commercial end users." However, a specific number of experts used to establish ground truth for the test set is not provided.
• Qualifications of Experts: The experts involved in development and testing are described as "radiologists, oncologists or other highly qualified medical clinicians that are proficient in reading, evaluating and interpreting images of the prostate produced by MR, CT or ultrasound devices." Specific years of experience are not mentioned, but the description emphasizes their proficiency and experience.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (like 2+1 or 3+1). It states that the "input was captured in a written and approved Software Requirement Specifications Document" and testing was conducted "both internally at Segasist Technologies and independently by experienced and trained medical professionals." The final contours generated by the software are subject to review and modification by the clinician, indicating a human-in-the-loop approach where the expert has the final say. However, for the initial ground truth used to evaluate the algorithm itself, the method is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, If So, What Was the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done as described in this document. The submission focuses on demonstrating substantial equivalence and the software's functional validation, rather than an explicit comparative effectiveness study showing improvement with AI assistance for human readers using quantitative metrics (e.g., sensitivity, specificity, reading time reduction). The device is positioned as a "compliment to manual contouring techniques," implying assistance, but without a formal study to quantify the improvement.

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

Yes, a standalone performance assessment was implicitly done. The document describes "bench testing" using "imported images" to determine if the "Segasist P-AC software works as intended." While the intended use involves human oversight and modification, the initial evaluation of the software's ability to generate contours (before human intervention) constitutes a form of standalone performance assessment. The "Segasist P-AC calculates the prostate volume in cubic centimeters and displays the contours on each slice," representing its standalone output.

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

The ground truth for the test set appears to be expert consensus or expert-defined contours. The document states that "input and collaboration of experienced and trained professionals" guided development, and "independent... medical professionals" tested the software using "clinical test cases." The creation of "an advanced atlas using gold standard images created by the experienced clinician(s)" further supports that expert-defined contours were used as the reference. There is no mention of pathology or outcomes data being used as ground truth for contouring accuracy directly, though the software's use is indicated for diagnosis and treatment planning, where such data would eventually be relevant.

8. The Sample Size for the Training Set

The document mentions that the software "can be delivered pre-trained with the comprehensive atlas or the end user can generate their own atlas." Training involves "atlas creation." However, the specific sample size used for the pre-trained comprehensive atlas (the training set) is not provided.

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

The ground truth for the training set (the "comprehensive atlas" or user-generated atlases) is established by experienced clinician(s). The document states, "The efficiency of contouring performed by the Segasist P-AC software may be improved by generating/using an advanced atlas using gold standard images created by the experienced clinician(s)." This indicates that human experts manually contoured the images that form the basis of the atlas used for training.

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Segment is a software that analyzes DICOM-compliant cardiovascular images acquired from magnetic resonance (MR) scanners. Segment specifically analyzes the function of the heart and its major vessels using multi-slice, multi-frame and velocity encoded MR images. It provides clinically relevant and reproducible data for supporting the evaluation of the function of the chambers of the heart such as left and right ventricular volumes, ejection fractions, stroke volumes, peak ejection and filling rates, myocardial mass, regional wall thickness, fractional thickening and wall motion. It also provides quantitative data on blood flow and velocity in the arterial vessels and at the heart valves. Segment is tested on MR images acquired from both 1.5 T and 3 T MR scanners. The data produced by Segment is intended to be used to support qualified cardiologist, radiologist or other licensed professional healthcare practitioners for clinical decision making. It is a support tool that provides relevant clinical data as a resource to the clinician and is not intended to be a source of medical advice or to determine or recommend a course of action or treatment for a patient.

Segment is a software for analysis of cardiovascular MR images. Segment provides clinical quantitative data by analyzing multi-slice, multi-phase DICOM compatible cardiovascular MR images. Functional and blood flow analysis is performed using 2D, 3D and 4D data sets using standard algorithms and user input. MR images may be imported from various sources including images stored on portable media, network storage devices, PACS, and other vendor systems and supports cardiovascular MR images from all of the major MRI scanner vendors. Segment can be used for quantitative and qualitative analysis of cardiovascular MR images.

The provided text is a 510(k) Summary for the Medviso AB Segment software, dating from 2009. It focuses on demonstrating substantial equivalence to predicate devices (MRI-MASS and MRI-FLOW) rather than providing detailed acceptance criteria and a specific study proving the device meets those criteria with statistical significance as typically found in more recent submissions for AI/ML devices.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state quantitative acceptance criteria or a table of reported device performance in the way a modern AI/ML device submission would. Instead, the "performance" is implicitly tied to demonstrating substantial equivalence to the predicate devices and general claims of clinical utility and reproducibility.

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

The document does not specify a sample size for any formal "test set" in the context of validating performance against ground truth. The submission predates the common expectations for rigorous AI/ML performance validation studies. It states that "Extensive testing of the software package is performed by programmers, by non-programmers, quality assurance staff and by potential customers prior to commercial release." This refers to internal software quality assurance and user acceptance testing, not a clinical performance validation study with a defined test set and ground truth.

There is no mention of data provenance (e.g., country of origin or retrospective/prospective nature) for any data used in testing.

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

The document does not mention using experts to establish ground truth for a test set. This type of submission relies on the established clinical utility of MRI images and the predicate devices' method of analysis, with the software acting as a tool to aid qualified professionals. The output is "intended to be used to support qualified cardiologist, radiologist or other licensed professional healthcare practitioners."

4. Adjudication Method:

Given that no formal expert-derived ground truth test set is described, there is no mention of an adjudication method (e.g., 2+1, 3+1).

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

No, a MRMC comparative effectiveness study was not done (or at least, not reported in this 510(k) summary). The submission focuses on substantial equivalence based on features and functionality, not on comparing reader performance with and without AI assistance. Therefore, no effect size calculation for human reader improvement with AI assistance is provided.

6. Standalone (Algorithm Only) Performance Study:

No, a standalone performance study was not explicitly described in the context of quantitative performance metrics against a defined ground truth. The document states, "Segment is used for image analysis and quantification... and all of the information is subject to his/her oversight and control." This indicates that the device is intended as a support tool, not a fully autonomous diagnostic algorithm. The "extensive testing" mentioned is internal software testing and potential customer feedback, not a formal standalone clinical performance study.

7. Type of Ground Truth Used:

No specific "ground truth" (e.g., pathology, outcomes data, or expert consensus specifically for a test set) is described as being used to validate the device's performance. The basis for safety and effectiveness is largely on clinical acceptance of MRI imaging itself and substantial equivalence to existing, legally marketed devices. The software "does not in any way alter the images" and provides "clinically relevant numeric computations."

8. Sample Size for the Training Set:

The document does not mention a training set or its sample size. This is consistent with the era and type of device; Segment is an image analysis software based on "standard algorithms and user input," rather than a machine learning model that requires a "training set" in the modern sense.

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

As no training set is discussed, no information is provided on how its ground truth would have been established.

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Biomet's Segmental Distal Femoral Components are intended for use in total knee replacement and the Proximal Femoral Bodies are intended for use in hip replacement procedures. Specific indications for these devices are:

1. Painful and disabled joint resulting from avascular necrosis, osteoarthritis, rheumatoid arthritis or traumatic arthritis.
2. Correction of varus, valgus or post traumatic deformity
3. Correction of revision of unsuccessful osteotomy, arthrodesis, or previous joint replacement.
4. Ligament deficiencies
5. Tumor resections
6. Treatment of non-unions, femoral neck and trochanteric fracture of the proximal femur with head involvement, unmanageable using other techniques
7. Revision of previously failed total joint arthroplasty
8. Trauma

These devices are to be used with bone cement unless a proximal femur is indicated for use (USA)

When used with Biomet's Compress Segmental Femoral Replacement System, the indications for use are uncemented application in cases of:

1. Correction of revision of unsuccessful osteotomy, arthrodesis, or previous ioint replacement.
2. Tumor resections.
3. Revision of previously failed total joint arthroplasty.
4. Trauma.

The Segmental Distal Femoral Components with a Compress® Female Taper are designed to replace the distal end of the femur including the knee articulating surface. The Proximal Femoral Bodies with a Compress® Female Taper are designed to replace the proximal end of the femur including the hip articulating surface. These components are intended for use with Biomet's Compress® Segmental Femoral and Orthopedic Salvage Systems. The new devices that are the subject of this 510(k) have a taper bore that is directly compatible with the taper of the Compress spindle eliminating the need for a taper adapter and allowing for smaller resection lengths.

This document is a 510(k) summary for a medical device (orthopedic implants) and does not contain information about acceptance criteria or a study proving the device meets acceptance criteria.

The document states:

• "Non-Clinical Testing: None provided as a basis for substantial equivalence."
• "Clinical Testing: None provided as a basis for substantial equivalence."

Instead, this 510(k) relies on demonstrating "substantial equivalence" to legally marketed predicate devices, meaning it is considered as safe and effective as existing devices without requiring new clinical trials or performance studies against specific acceptance criteria.

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The Mick Radio-Nuclear Instruments, Inc. Segmented Vaginal Applicators are intended for use in Brachytherapy. The system described in this 510(k) has been developed to function as an applicator for the positioning of sealed sources in the intracavitary treatment of the vagina.

The Mick Radio-Nuclear Segmented Vaginal Applicator meets these requirements by providing an system that can be adjusted in length and diameter to meet the dimensions of the treatment volume and by utilizing radio opaque markers for visualization.

This submission K993472 is for a Segmented Vaginal Applicator, which is a medical device used in brachytherapy. The submission asserts substantial equivalence to a predicate device and does not involve a study to demonstrate performance against specific acceptance criteria.

Therefore, most of the requested information cannot be extracted from the provided text as the application did not involve a performance study with acceptance criteria.

Here's what can be stated based on the given document:

1. A table of acceptance criteria and the reported device performance: Not applicable. The submission asserts substantial equivalence based on design, construction, materials, intended use, and performance characteristics being similar to a predicate device, rather than providing specific performance metrics against pre-defined acceptance criteria for a new study.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. No test set or data provenance is mentioned as this was not a clinical performance study.

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. No ground truth establishment for a test set is mentioned.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. No test set adjudication is mentioned.

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 an applicator for radiation therapy, not an AI-assisted diagnostic tool.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is a physical medical device, not a standalone algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable. No ground truth was established for a performance study. The "ground truth" for this submission is the established safety and effectiveness of the predicate device to which it claims substantial equivalence.

8. The sample size for the training set: Not applicable. This device is not an AI algorithm requiring a training set.

9. How the ground truth for the training set was established: Not applicable.

The core argument for the device's marketability lies in its substantial equivalence to a previously cleared device, not in a new performance study with specific acceptance criteria. The document states:
"This device is similar in design and construction, utilizes the identical materials, and has the same intended use and performance characteristics to the predicate devices. No new issues of safety or effectiveness are introduced by using this device."

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The Segmental Defect Replacement System, when used with bone cement, is intended for reconstruction of the humeral diaphysis with an extensive bone lesion due to metastatic disease, myeloma, or lymphoma, that places the humerus at risk for pathologic fracture.

The Clinical Indication for Use is:

"The Segmental Defect Replacement System, when used with bone cement, is indicated for pathologic fractures or impending pathologic fractures of the diaphysis of the humerus secondary to metastatic bone disease and hematologic malignancies."

The Segmental Defect Replacement System is an array of proximal and distal intramedullary rods which, when locked together, replace the resected diseased segment and fixation in the remaining humeral bone The system includes a locking device to secure the rod components, and instrumentation to facilitate stock. insertion.

Modular bistemmed intramedullary rod system with rigid cam locking mechanism designed to provide intraoperative adjustment after cementing components into the proximal and distal segments of the resected humerus.

Materials: Ti-6Al-4V NiTi alloy (locking system)

The provided text describes a 510(k) summary for a "Segmental Defect Replacement System." This document is a premarket notification for a medical device aiming to demonstrate substantial equivalence to a legally marketed predicate device, rather than proving the device meets specific acceptance criteria through a clinical study with performance metrics.

Therefore, the specific information requested regarding acceptance criteria, a study proving device performance, sample sizes, ground truth establishment, expert qualifications, adjudication methods, MRMC studies, or standalone performance studies is not present in the provided text.

The document focuses on comparing the proposed device to existing predicate devices based on:

• Intended Use: Reconstruction of the humeral diaphysis with extensive bone lesions due to metastatic disease, myeloma, or lymphoma, placing the humerus at risk for pathologic fracture.
• Design: Modular bistemmed intramedullary rod system with a rigid cam locking mechanism for intraoperative adjustment after cementing components.
• Materials: Ti-6Al-4V, NiTi alloy (locking system).

The document states: "The Segmental Defect Replacement System has been evaluated by use of the static and fatigue tests. It has been shown to be substantially equivalent to the predicate devices." However, it does not provide details about the specific acceptance criteria for these tests, the performance metrics achieved, or the study design/results for demonstrating this substantial equivalence beyond this general statement.

In summary, the provided content is a regulatory submission for substantial equivalence, not a detailed report of a clinical or performance study with defined acceptance criteria and study results.

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