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For maintaining open the airway, after dilation of the stenosis or resection of the obstruction, in particular in case of:

• Tracheobronchial tumors
• Tracheal stenoses with scarring
• Stenoses following surgical anastomosis, resection or pulmonary transplantation.

The TRACHEOBRONXANE™ DUMON® stents are flexible tracheobronchial tubes made of medical grade silicone, designed to improve patient respiration capacity while acting as trachea or bronchi support. These medical devices are designed to conform to individual anatomies.

Each stent has a specific treatment, to improve the evacuation of mucus in order to limit the risk of obstruction.

A large range of shape, thickness and sizes is available depending on the pathology to be treated, patient intrinsic factor and location of the implantation. Indeed, the TRACHEOBRONXANE™ DUMON® range consists of various models which vary from each other in the following characteristics:

• The stents type: TD, TF, BD, Y, ST corresponding to various profiles made of silicone with different hardness;
• . The dimensions: each profile consists of different sizes in diameter and length;
• The radiopacity: stents can be provided radiopaque or not radiopaque; barium sulfate . is added during the manufacturing process in order to make the stent radiopaque which allows obtaining a better visibility by X-rays radiography.

The TRACHEOBRONXANE™ DUMON® stents have been designed with studs in order to facilitate their anchorage in the trachea or bronchi (on cartilage rings). The number of stud rows is depending on the stent profile and stent final destination (bronchi or trachea).

The TRACHEOBRONXANE™ DUMON® stent holds the respiratory tract open by exerting a radial force.

The Tracheobronxane™ Dumon® device is a medical-grade silicone tracheobronchial tube intended to maintain an open airway after dilation or resection, particularly in cases of tumors, scarring, or post-surgical/transplantation stenoses.

The provided document does not contain a typical acceptance criteria table with reported device performance for an AI/algorithm-based device. Instead, it describes performance data related to the physical and biological characteristics of a medical device (a stent). The assessment is for demonstrating substantial equivalence to a predicate device, not for an AI's diagnostic or predictive capabilities.

However, based on the information provided, we can infer the acceptance criteria and the studies performed to demonstrate equivalence.

Here's an interpretation based on the provided text, framed as acceptance criteria for the device itself (the stent), rather than for an AI:

1. Table of Acceptance Criteria and Reported Device Performance:

• Chemical testing and subsequent risk assessment revealed no systemic toxic effects.
• In-vitro studies on NuSil medical-grade silicones and DUMON® stents passed cytotoxicity tests.
• Chemical testing did not reveal sensitizing or irritating substances; in-vivo studies on NuSil medical-grade silicones passed.
• Devices do not contain substances from DIN EN ISO 10993-11 Annex G; in-vivo studies on NuSil medical-grade silicones revealed no material-mediated pyrogenicity.
• Long history of safe clinical use supports no expectation of systemic toxicity, implantation effects, carcinogenicity, or reproductive/developmental toxicity.
• Due to non-polar nature of silicone, no degradation expected; chemical testing on DUMON® stents revealed no degradation products.
• Chemical testing on DUMON® stents revealed no substances in toxicologically relevant amounts (toxicokinetics). |
  | Mechanical Performance | Fatigue Resistance: Stents maintain performance characteristics after simulating normal usage.
  Weighing: Stents do not absorb or dispose of significant substances.
  Compression: Stents maintain radial performance and required force for compression. | - Specific test procedures performed on brand new, fatigue-stressed, and water-bath stored stents. Data compared. (Implies performance maintained, though specific metrics not given).
• Weighing test performed on water-bath and fatigue-tested stents. (Implies results were acceptable).
• Compression test performed on fatigue-tested stents to examine radial performance. (Implies results were acceptable). |
  | Shelf-Life | 3-year shelf-life demonstrated. | 3 years. (This is a specific claim met). |
  | Sterilization | Supplied sterile. | Steam Sterilization method used, device supplied sterile. (This is a specific claim met). |
  | Equivalence to Predicate | The device is substantially equivalent to the ENDOXANE® predicate device in terms of intended use, indications for use, patient target group, intended user, anatomical sites, environment of use, energy used, materials, principles of operation, and mechanical safety. It maintains the same level of safety and effectiveness. | - Comparison table shows "same" for nearly all categories including intended use, indications, materials, and mechanical safety characteristics.
• The only noted difference is that the proposed device is supplied sterile, while the predicate was non-sterile, which would not negate equivalence. |

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

The document describes benchmarking performance tests and biocompatibility testing. However, it does not specify "test sets" in the context of an algorithm or AI.

• Biocompatibility Testing: The "samples" would be the device materials (medical-grade silicone, barium sulfate) and the final stents themselves. The exact number of samples for each test (cytotoxicity, sensitization, etc.) is not specified, but standard ISO 10993 testing involves a defined number of replicates/samples. The provenance is internal to the manufacturer (Novatech SA) and involves standard lab testing, not patient data in the sense of retrospective or prospective clinical studies.
• Benchmark Performance Tests (Fatigue, Weighing, Compression): The "samples" are the stents themselves. The number of stents used for these tests (brand new, fatigue-stressed, water-bath treated) is not specified. The provenance is internal laboratory testing by the manufacturer.

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

Not applicable. This device is a physical medical implant (stent), not a diagnostic or AI device that relies on expert interpretation for ground truth establishment. The ground truth for its performance is derived from standardized laboratory tests (biocompatibility, mechanical tests).

4. Adjudication Method for the Test Set:

Not applicable. There is no "test set" in the context of expert review or adjudication as would be relevant for an AI/algorithm. Performance is assessed against pre-defined, standardized test methods and criteria.

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

No. This is not an AI-assisted device, so MRMC studies for human readers with and without AI assistance are not relevant or performed.

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 an algorithm.

7. The Type of Ground Truth Used:

• Biocompatibility: Ground truth is established by adherence to ISO 10993-1 standards and the absence of adverse biological reactions in standardized in-vitro and in-vivo tests, as well as a "long history of safe clinical use" for similar materials.
• Mechanical Performance: Ground truth is established by engineering specifications for stent integrity, radial force, and material absorption/disposal, measured through specific mechanical tests (fatigue, weighing, compression).
• Shelf-Life/Sterilization: Ground truth is established by stability testing and validated sterilization methods demonstrating sterility assurance levels.

8. The Sample Size for the Training Set:

Not applicable. This is a physical medical device, not an AI/algorithm that requires a training set.

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

Not applicable. This is a physical medical device, not an AI/algorithm.

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The VisionAir Patient-Specific Airway Stent is indicated for the treatment of adults ≥22 years of age with symptomatic stenosis of the airway. The silicone stent is intended for implantation into the airway by a physician using the recommended deployment system or an equivalent rigid bronchoscope and stent placement system that accepts the maximum stent diameter being placed. The stent is intended to be in the patient up to 12 months after initial placement.

The subject device, VisionAir Patient-Specific Airway Stent is comprised of a cloudbased software suite and the patient-specific airway stent. These two function together as a system to treat symptomatic stenosis of the airway per the indications for use. The implantable patient-specific airway stent is designed by a physician using a CT scan as a guide in the cloud-based software suite. The airway is segmented from the CT scan and used by the physician in designing a patient-specific stent. When design is complete, the stent is manufactured via silicone injection into a 3D-printed mold and delivered to the treating physician nonsterile, to be sterilized before use.

The implantable patient-specific airway stent includes the following general features:

• Deployed through a compatible rigid bronchoscope system
• Made of biocompatible, implant-grade silicone
• Steam sterilizable by the end user
• Anti-migration branched design
• Anti-migration studs on anterior surface of main branch
• Single-use

The cloud-based software suite has the following general features:

• Upload of CT scans
• Segmentation of the airway
• Design of a patient specific stent from segmented airway
• Order management of designed stents

The provided text is a 510(k) Summary for the VisionAir Patient-Specific Airway Stent, which focuses on demonstrating substantial equivalence to a predicate device. It primarily discusses the device description, indications for use, technological characteristics, and a list of nonclinical performance and functional tests conducted.

However, the document does not contain the detailed information required to fulfill the request regarding acceptance criteria and the study that proves the device meets those criteria. Specifically, it lacks:

1. A table of acceptance criteria and reported device performance: While it lists types of tests, it does not provide specific quantitative acceptance criteria or the actual results from these tests.
2. Sample size used for the test set and data provenance: No information is given about the sample size for any clinical or performance test, nor the origin or nature of the data (retrospective/prospective, country).
3. Number of experts used to establish ground truth and qualifications: This information is completely absent.
4. Adjudication method for the test set: Not mentioned.
5. Multi-Reader Multi-Case (MRMC) comparative effectiveness study details: No MRMC study is described; the testing mentioned is primarily non-clinical or related to software validation/verification, not human-AI comparative effectiveness.
6. Standalone (algorithm-only) performance: While "Software Verification and Validation Testing" and "Airway Segmentation Process Testing" are mentioned, no specific standalone performance metrics (e.g., accuracy, precision for segmentation) or acceptance criteria are provided.
7. Type of ground truth used: The document mentions "Airway Segmentation Process Testing" and refers to a predicate device (Mimics) for "performance reference specification" for dimensional testing of airway segmentation. This implies that the ground truth for segmentation would likely be derived from expert-reviewed segmentations or potentially from known anatomical measurements, but the method is not explicitly detailed.
8. Sample size for the training set: There is no mention of a "training set" or any machine learning model that would require one. The software aspect described is for physician-guided design and semi-automated segmentation, not explicitly an AI/ML model that undergoes a training phase in the typical sense for medical image analysis.
9. How the ground truth for the training set was established: Not applicable, as no training set is described.

The document states: "Reference devices, Mimics (K073468) and Osirix MD (K101342) were used for reference software performance specifications." and "Dimensional Testing of Airway Segmentation (reference device Mimics K073468 used for performance reference specification)". These statements hint at software validation, especially for the segmentation component, but do not provide the detailed study design, acceptance criteria, or results.

In summary, the provided text does not contain the necessary information to answer the request in detail, as it focuses on demonstrating substantial equivalence through non-clinical performance and functional testing rather than a clinical study with acceptance criteria for device performance based on human reader interaction or AI model performance.

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The Patient-Specific Airway Stent is indicated for the treatment of adults ≥22 years of age with symptomatic stenosis of the airway. The silicone stent is intended for implantation into the airway by a physician using the recommended deployment system or an equivalent rigid bronchoscope and stent placement system that accepts the maximum stent diameter being placed. The stent is intended to be in the patient up to 12 months after initial placement.

The Patient-Specific Airway Stent is comprised of Web Software and the Patient-Specific Silicone Y-Stent. These two function together as a system to treat symptomatic stenosis of the airway. The Patient-Specific Silicone Stent is designed by a physician using a CT scan as a guide in the Web Software. The Web Software gives the user (physician) the ability to upload a scan, view the airway, and design a stent. The stent, after physician approval, is manufactured via silicone injection into a 3D-printed mold and delivered to the treating physician's medical center nonsterile.

This document (K182743) is a 510(k) Premarket Notification for a Patient-Specific Airway Stent. It primarily focuses on demonstrating substantial equivalence to a predicate device (ENDOXANE, K971509) and the safety/effectiveness of the device.

Based on the provided text, the device in question is a Patient-Specific Airway Stent System, which includes Web Software and the Patient-Specific Silicone Y-Stent. The software allows physicians to design the stent based on a CT scan, and then the stent is manufactured via silicone injection into a 3D-printed mold.

The acceptance criteria and study that proves the device meets them are mostly related to non-clinical performance testing and software verification/validation, rather than a full clinical study with human patients evaluating the AI's diagnostic performance. Therefore, many of the typical acceptance criteria and study elements for an AI-powered diagnostic device are not explicitly detailed in this 510(k) summary.

Here's an attempt to extract the relevant information based on your request, acknowledging the limitations of the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't provide a direct table of acceptance criteria with specific numerical performance metrics for the software's ability to accurately design the stent or for the final stent's intended clinical outcome in terms of patient-specific fit. Instead, it describes various tests performed to ensure the device functions as intended and is substantially equivalent to the predicate.

The closest to "acceptance criteria" are the objectives of the non-clinical performance tests, and "reported device performance" is described qualitatively as "supports the claim" or "confirms."

Please note: The "device performance" here refers to the engineering and functional performance of the stent and software, not clinical outcome metrics (e.g., patient improvement rates).

Note on Quantitative Acceptance Criteria: The document explicitly mentions some differences, such as the subject device having lower flat-plate compression strength than the predicate device. However, it states that "Any risks related to these technological differences have been mitigated to an acceptable level," implying that these differences did not prevent meeting overall safety/effectiveness. For the AI component (segmentation and design), specific quantitative acceptance criteria (e.g., Dice score for segmentation accuracy, deviation from ideal stent dimensions) are not provided in this summary document.

2. Sample Size for Test Set and Data Provenance

• The document does not specify a sample size for the "test set" in the context of typical AI performance evaluation (e.g., number of CT scans used to validate segmentation or design accuracy).
• The closest mentions are "Dimensional Testing of Airway Segmentation" and "Airway Segmentation Process Validation." It's implied that some CT scan data was used for these, but neither the sample size nor the provenance (country, retrospective/prospective) of this data is mentioned.

3. Number of Experts and Qualifications for Ground Truth

• The document describes the device as a "Patient-Specific Airway Stent" where creation involves a "physician using a CT scan as a guide in the Web Software" and the stent is manufactured "after physician approval."
• The "Web Software" allows "COS technicians to segment the airway and automatically calculate a centerline."
• "Ground truth" for the AI component (segmentation, centerline calculation, stent design) is not explicitly defined in terms of expert consensus or pathological verification in this summary. Instead, it appears the software's output is reviewed and approved by a single physician for an individual patient.
• Therefore, it's not a panel of experts establishing ground truth for a general test set, but rather an individual physician performing the critical review and approval step for each patient. No specific number of experts used to establish ground truth for a general test set is mentioned, nor are their qualifications.

4. Adjudication Method for the Test Set

• Given that the "ground truth" for the AI's output is implied to be physician review and approval for each specific case, there is no multi-reader adjudication method (like 2+1 or 3+1) described for a general test set. The process involves one physician approving the design.

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

• No MRMC study comparing human readers with and without AI assistance is mentioned or implied. The product is a custom-designed stent system, not a diagnostic AI intended to assist in interpreting medical images. The AI (software) assists in the design and manufacturing process, which is then approved by a physician.

6. Standalone (Algorithm Only) Performance

• No standalone (algorithm only) performance metrics are explicitly provided. The software acts as a design tool that then requires physician approval before manufacturing. The document describes "Software Verification and Validation Testing" and "Dimensional Testing of Airway Segmentation" which implies internal testing of the algorithm, but specific standalone metrics (e.g., segmentation accuracy against ground truth) are not reported in this summary.

7. Type of Ground Truth Used

• For the software's AI components (segmentation, centerline calculation), the implicit "ground truth" during real-world use is the physician's subjective review and approval based on the CT scan.
• For the non-clinical tests (e.g., tear strength, fatigue), the ground truth relates to engineering specifications and established test methods.
• No pathology or outcomes data is mentioned as ground truth for the software's performance, as this is a device design tool, not a diagnostic algorithm.

8. Sample Size for the Training Set

• The document does not specify the sample size for the training set used for any AI component (segmentation, centerline calculation). It only refers to a "proprietary software" used by "COS technicians" to segment the airway and calculate the centerline.

9. How Ground Truth for Training Set Was Established

• The document does not describe how ground truth for any potential AI training set was established.

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The main indications according to their frequency are : - 1. tracheobronchial tumors - 2. Tracheal stenosis with scarring - 3. Bronchial stenosis after surgical anastomotic, anastomoses resections or pulmonary transplantation. These stenoses have, as a common characteristic, the importance of the extrinsic compression or the collapse of the cartilingeous wall. The indication always focuses on an endoscopic therapeutic action, whether is concerns a resection or dilation. The stent insertion is indicated if the endoscopic resection is incomplete, in case of a persistent extrinsic compression, or if the lumen is insufficient. The most frequent locations are tracheal. The left main bronchus is the second location, followed by the right main bronchus.

the ENDOXANE® is an endoscopy stent made of silicone. This stent, the most fitted in the world, is presented as a silicone tube with stubs allowing to avoid any movement. The non armed polysiloxane is covered with a new coating especially designed in order to keep its' flexibility which facilitates tolerance and evacuation of secretions. A well-adapted ENDOXANE® does not move and cough reflexes are inhibited by reflex center saturation. The stubs prevent a direct contact between the stent surface and the mucus membrane and distribute the pressures among the small surfaces.

The provided document is a 510(k) premarket notification for a medical device called ENDOXANE. It describes the device, its indications for use, and claims substantial equivalence to a predicate device. However, it does not contain the detailed clinical study data, acceptance criteria, or performance metrics that would typically be required to fully answer the request.

Therefore, I can only provide the information that is available in the document and explicitly state where information is missing.

Here's an analysis based on the provided text:

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

No explicit acceptance criteria (e.g., specific thresholds for performance metrics like sensitivity, specificity, accuracy, or complication rates) are stated in the document.

The document mentions "Complications were uncommon, usually easily manage, and rarely life threatening. The main complications were emigration (9.5%)n obstruction by secretions." This is a reported observation from a study, not a pre-defined acceptance criterion.

• Oval-shaped stents regained round shape in less than two days in cases of extreme compression
• Complications: uncommon, usually easily managed, rarely life-threatening
• Main complications: emigration (9.5%), obstruction by secretions |

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

The document refers to a study: "The conclusion that one can reach concerning our stents is best stated in the study published in "The Journal of Bronchology" (3:6-10, 1996) about the placing of 1500 stents (Cavalière, Diaz, Dumon, Vergnon)."

• Sample Size for Test Set: 1500 stents were placed (implicitly, 1500 patients or cases).
• Data Provenance:
  • Country of Origin: Not explicitly stated, but the applicant (NOVATECH S.A.) is based in France, and the authors of the cited study (Cavalière, Diaz, Dumon, Vergnon) are likely affiliated with French institutions, suggesting the data is likely from France or Europe.
  • Retrospective or Prospective: Not explicitly stated. Given that it's a publication summarizing "placing of 1500 stents," it could be a retrospective analysis of a series of cases over time.

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)

This information is not provided in the document. The document refers to a published study, but does not detail the methodology for establishing ground truth within that study. The "ground truth" for stent performance would likely come from clinical follow-up and physician assessment, but specifics are missing.

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

This information is not provided in the document.

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

• MRMC Study: No, this is not an AI device. The ENDOXANE® is a physical medical device (tracheal/bronchial stent). This question is not applicable.

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

• Standalone Performance: No, this is not an AI device. This question is not applicable.

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

The "ground truth" implicitly refers to clinical outcomes and physician assessment of stent performance and complications from the cited study. For example, "no single case of compression was observed" and "complications were emigration (9.5%) and obstruction by secretions" are clinical outcome data. Specifics on how individual outcomes were defined and assessed are not provided in this regulatory document, but would be expected in the full journal article.

8. The sample size for the training set

• Training Set Sample Size: This is not applicable as the ENDOXANE® is a physical medical device, not an AI or software device that undergoes a "training" process in the typical machine learning sense. The device was developed and then tested.

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

• Ground Truth for Training Set: This is not applicable for the same reasons as above.

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