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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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Ultraflex™ Esophageal NG Stent System (non-covered) is intended for maintaining esophageal luminal patency in esophageal strictures caused by intrinsic and/or extrinsic malignant tumors only.

Ultraflex™ Esophageal NG Stent System (covered) is intended for maintaining esophageal luminal patency in esophageal strictures caused by intrinsic and/or extrinsic malignant turnors only, and occlusion of concurrent esophageal fistula.

The proposed Ultraflex Esophageal NG Stent System allows for the placement of a self-expanding metallic stent within the esophagus. The systems consist of a flexible delivery catheter preloaded with an expandable stent. The stent is offered either bare or covered and with either a proximal release or distal release delivery system. The stent may be placed fluoroscopically using radiopaque markers as a guide or endoscopically using the visual marker on the delivery catheter. The proposed device incorporates an updated Magnetic Resonance (MR) Conditional statement in the Directions for Use.

The Ultraflex™ Esophageal NG Stent System (K130004) is substantially equivalent to its predicates because it has identical design and materials, and the only change is an updated MR Conditional statement in the Directions for Use (DFU). This updated statement ensures compatibility with 1.5 and 3.0 Tesla MRI use, aligning with ASTM standard F 2503-08.

Here's a breakdown of the information:

1. Table of Acceptance Criteria and Reported Device Performance:

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

   • Sample Size: Not explicitly stated as a "test set" in the context of human data. The performance data section refers to "Magnetic Resonance testing."
   • Data Provenance: The document does not specify the country of origin or whether the data was retrospective or prospective. It implies laboratory testing rather than human clinical data.

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

   • Not applicable. The "ground truth" here is defined by the technical standard ASTM F 2503-08 for MR Conditional labeling, not by expert medical opinion on a patient dataset.

4. Adjudication Method for the Test Set:

   • Not applicable. The evaluation is based on adherence to a technical standard and laboratory testing, not on human adjudication of cases.

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

   • No. This study is focused on the MR safety labeling of a medical device, not on diagnostic performance or the effectiveness of human readers with or without AI assistance.

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

   • Not applicable. This device is a stent, not an AI algorithm. The performance evaluation relates to its physical properties and interaction with an MR environment.

7. The Type of Ground Truth Used:

   • Technical Standard Compliance: The "ground truth" for the device's MR compatibility is established by adherence to the ASTM F 2503-08 standard for MR Conditional labeling.

8. The Sample Size for the Training Set:

   • Not applicable. There is no training set mentioned, as this is not an AI/machine learning device.

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

   • Not applicable. There is no training set for this device.

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