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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 VISIONAIR™ system is a software application intended to be used with third-party endoscopic systems in the measurement of the nasal respiratory airway. The VISIONAIR™ system measures the nasal respiratory airway from the endoscopic images taken in the region of the internal nasal valve (INV) and nasal cavum (NC).

The VISIONAIR™ software can be utilized to automatically measure the cross-section area of the Internal Nasal Valve (INV) and Nasal Cavum (NV) and measure the nasal respiratory airway in this region of the anatomy. The VISIONAIR™ system consists of the following components: - The VISIONAIR™ algorithm which performs the Internal Nasal Valve and Nasal Cavum cross-section area segmentations. - The VISIONAIR™ Graphical User Interface (GUI) used for data entry, view the endoscopic image from third party endoscopes and display the data analysis to the user. - A smart device such as tablet or laptop which runs on Windows 10 or a later operating system with the VISIONAIR™ application installed. - A cloud service that runs in the background and can be activated by the user when a particular dataset for a case is desired to securely and anonymously be stored to the Cloud Server (REAI). - USB memory used to encrypt and anonymize the patient information, whether the data is stored locally or to the cloud, and stores the credits needed to activate the VISIONAIR™ software for each case. The VISIONAIR™ application interfaces with third-party endoscopic systems via the ports located on the smart device. The smart device ports enable the third-party systems endoscopic video display to be streamed on the VISIONAIR™ application endoscopic video display. In this 510(k) submission, a FDAcleared endoscope (K970247) was selected as the reference device to support the scientific methodology. The VISIONAIR™ application automatically analyzes the endoscopic images using its trained AI algorithm to measure the nasal valve and nasal cavum surface areas. The VISIONAIR™ application also provides a database file system to manage the data and interface securely and anonymously with the cloud server via the REAl module.

The provided FDA 510(k) summary for the VISIONAIR™ system offers details on its intended use and comparison to a predicate device, as well as a list of non-clinical tests performed. However, it does not explicitly state specific acceptance criteria (e.g., minimum accuracy thresholds) or present the detailed results of a study designed to prove the device meets those criteria with statistical significance.

Instead, it lists tests performed, implying that these tests confirmed design specifications and functionality. The "Substantial Equivalence Table" focuses on comparing attributes to a predicate device and concluding that differences do not raise new safety or effectiveness concerns.

Therefore, many of the requested details about acceptance criteria, specific study results, sample sizes, ground truth establishment, expert qualifications, and MRMC studies are not present in the provided text.

Based on the available information, here's what can be extracted and what is missing:

Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria (e.g., "accuracy > 90%"). Instead, the "Non-clinical Performance Data" section lists various tests performed to ensure the device functions according to design specifications and for substantial equivalence in terms of safety and effectiveness. The "reported device performance" is largely implied by the statement that these tests were "performed" and that the device is deemed "substantially equivalent."

Detailed Study Information (Based on available text):

1. Sample Size used for the test set and the data provenance:

   • Test Set Sample Size: Not specified for any of the listed tests.
   • Data Provenance: Not specified (e.g., country of origin). The document mentions "endoscopic images" and "CT scans" were used, but no details on their origin or whether they were retrospective/prospective.

2. 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 specified.
   • Qualifications of Experts: The "VISIONAIR™ AI Segmentation Accuracy Test" mentions "experienced clinicians" were used for comparison, but their specific qualifications (e.g., specialty, years of experience) are not provided.

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

   • Not specified. The document only mentions "comparison" in the segmentation accuracy tests.

4. 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: Not explicitly described as an MRMC comparative effectiveness study where human readers' performance with and without AI assistance is measured.
   • Effect Size: Not provided. The document mentions "User Validation Test" by clinicians and "comparison of segmented endoscopic images by VISIONAIR™ application vs. segmented endoscopic images by experienced clinicians," but not an assessment of human reader improvement.

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

   • The "VISIONAIR™ AI application confirmation of successful segmentation" in the Nasal Respiratory Airway Analysis Test, and the "CT vs Segmentation Accuracy Test" and "VISIONAIR™ AI Segmentation Accuracy Test" imply a standalone evaluation of the algorithm's performance in segmentation against various ground truths (CT scans, experienced clinicians' segmentations). However, exact methodology and metrics are not detailed.

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

   • CT Scans: Used as a ground truth for cross-sectional areas in the "CT vs Segmentation Accuracy Test." This implies measurements from CT scans were considered the reference.
   • Experienced Clinician Segmentations: Used as a ground truth for segmented endoscopic images in the "VISIONAIR™ AI Segmentation Accuracy Test." This suggests individual or consensus segmentations by clinicians served as the reference.
   • Implicitly, other tests depend on functional specifications and user observation.

7. The sample size for the training set:

   • Not specified in the document. The document refers to the "trained AI algorithm" but does not give details about its training data.

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

   • Not specified in the document.

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Smith & Nephew's VISIONAIRE UK Patient Matched Cutting Guides are intended to be used as patient-specific surgical instrumentation to assist in the positioning of knee replacement components intra-operatively and in guiding the marking. of bone before cutting provided that anatomic landmarks necessary for alignment and positioning of the implant are identifiable on patient imaging scans.

The Smith & Nephew VISIONAIRE UK Patient Matched Cutting Guides are intended for use with the following existing Smith & Nephew, Inc. Knee Systems and their cleared indications for use:

• JOURNEY II Unicompartmental Knee (JOURNEY II UK) System

The Smith & Nephew VISIONAIRE UK Patient Matched Cutting Guides are intended for single use only.

The VISIONAIRE Unicompartmental Knee (UK) Patient Matched Cutting Guides are to be used as patient-specific surgical instrumentation to assist in the positioning of partial knee replacement implant components intra-operatively and in guiding the marking of bone before cutting. The subject device is designed and manufactured from patient imaging data (i.e. MRI, X-Ray) using additive manufacturing of Nylon 12 material to create the patientmatched guides. The blocks achieve mechanical alignment via an intimate fit with the patient's proximal tibia or distal femur. This fit is achieved by reconstructing the patient's bony and cartilaginous anatomy from the MRI scans of the patient's knee. The VISIONAIRE UK Patient Matched Cutting Guides are intended to be used for medial femoral and tibia resections in conjunction with previously cleared Smith & Nephew JOURNEY II Unicompartmental Knee (JOURNEY II UK) System (K190085).

The provided document does not contain information about acceptance criteria or a study that proves the device meets those criteria in the context of device performance metrics like sensitivity, specificity, accuracy, or other similar quantitative measures typically associated with AI/ML diagnostic or measurement devices.

The document is a 510(k) summary for the "VISIONAIRE UK Patient Matched Cutting Guides," which are patient-specific surgical instruments. The assessment for this type of device focuses on substantial equivalence to existing predicate devices, functional performance in a surgical context, and safety.

Here's a breakdown of the information that is available in the document, framed against your requested points:

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

This information is not provided in the document in the format of quantitative performance metrics for AI/ML. The device is a physical surgical guide, not a diagnostic AI.

The document states: "Smith & Nephew conducted cadaveric testing of the VISIONAIRE UK Patient Matched Cutting Guides. The results of this testing demonstrated that the patient matched cutting blocks/guides designed using the case processing applications perform equivalent to the predicate devices listed in the Table 5.1 below."

This implies that the acceptance criteria for the cadaveric testing would have been equivalence to the predicate devices in guiding bone cuts, likely in terms of accuracy of bone resection or component positioning. However, the specific criteria (e.g., "within X degrees of alignment," "within Y mm of cut depth") and the actual performance results (e.g., "average deviation was Z mm/degrees") are not detailed.

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

The document mentions "cadaveric testing," which serves as the "test set" for the physical guides.

• Sample size for the test set: Not specified.
• Data provenance: Cadaveric testing. Country of origin not specified, but typically conducted in-house or at a specialized lab. This is a prospective test, as the testing was conducted specifically for this submission.

The patient imaging data (MRI, X-Ray) used to design the guides for this testing would have been retrospective if taken from existing cadaveric scans or prospective if new scans were performed. This detail is not given.

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

This information is not provided. For cadaveric testing of surgical guides, ground truth would likely be established by direct measurement of the bone cuts or implant placement by qualified engineers or surgeons, but the specific details are absent.

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

This information is not provided.

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 and not done. This device is a surgical cutting guide, not an AI/ML diagnostic or measurement tool that assists human readers in interpreting images.

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

Not applicable. The device is a physical cutting guide, designed using "VISIONAIRE Patient Matched Technology" (which involves software applications, not a standalone AI for diagnosis). The performance evaluation focuses on the physical guide's ability to facilitate accurate surgical cuts, assisted by human surgeons. The software itself is assessed for safety and effectiveness in its role of manufacturing the guide, not as a standalone diagnostic algorithm. The document states: "All changes are made to the non-medical device software as part of the subject device submission. There are no changes required to the previously cleared VISIONAIRE Patient Matched Cutting Blocks software (Secondary Predicate - K200826) that are involved in creating the VISIONAIRE UK Patient Matched Cutting Guides."

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

For the cadaveric testing, the ground truth would likely be physical measurements of the achieved bone resection or implant alignment compared to the planned resection/alignment, performed by engineers or surgeons after the cuts were made using the guides. The document does not specify how this ground truth was measured or established.

8. The sample size for the training set

Not applicable for a typical AI/ML training set. The "training" for this device involves the design and manufacturing process based on patient imaging data. The document mentions "patient imaging data (i.e. MRI, X-Ray)" being used to design and manufacture the guides. There isn't a "training set" in the sense of a machine learning model learning from a dataset. The software uses these scans to generate a patient-specific guide.

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

Not applicable. There isn't a "training set" in the AI/ML context. The patient's own anatomy, as captured by the MRI scans, serves as the basis for the design of their individual guide. The "ground truth" here is the patient's actual anatomical features.

In summary, the provided document is a regulatory submission for a physical medical device (surgical guide), not an AI/ML software as a medical device. Therefore, many of the requested criteria related to AI/ML performance studies are not relevant or addressed in the document. The key study mentioned is "cadaveric testing" to demonstrate equivalence to predicate devices in the context of guiding surgical cuts.

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Smith & Nephew's VISIONAIRE™ Disposable Instruments are intended to be used to further prepare the bone and assist in the positioning of total knee replacement components intraoperatively. Smith & Nephew VISIONAIRE™ Disposable Instruments are accessory devices and are intended to be used to assist in the implantation of Smith & Nephew Genesis II and Legion knee systems and their cleared Indications for Use.

The contraindications, potential adverse events, precautions, and warnings for the knee systems can be found in the Smith & Nephew Knee System package insert. The VISIONAIRE™ Patient Matched Cutting Blocks and Disposable instruments are intended for single use only. Do not reuse due to risks of breakage, failure or patient infection.

Per U.S. Food and Drug Administration (FDA) regulation, device-specific instruments are accessory devices and take on the classification of the device(s) with which they are used. Although these instruments are similar in design to 510(k)-exempt orthopaedic manual instruments classified under 21 CFR 888.4540, instruments which assist in the implantation of Class II Smith & Nephew Total Knee Systems and are classified as Class II devices are subject to pre-market notifications and regulations.

The Smith & Nephew Disposable Instruments include line additions to the Smith & Nephew Disposable Knee Instruments that were cleared in K123159. The subject devices are intended prepare the bone for Total Knee Arthroplasty.

The provided text does not contain information about the acceptance criteria or a study proving that a device meets those criteria, as it relates to a typical AI/ML device submission for medical imaging. The document is an FDA 510(k) clearance letter for VISIONAIRE™ Disposable Instruments, which are surgical instruments used for total knee replacement.

The clearance is based on substantial equivalence to predicate devices, not on meeting specific performance criteria through a study in the sense typically associated with AI/ML device evaluation (e.g., accuracy, sensitivity, specificity on a test set).

Here's a breakdown of why the requested information cannot be extracted and what information is available:

• Device Type: This is a physical surgical instrument, not an AI/ML software device.
• Approval Basis: The approval is based on "substantial equivalence" to existing legally marketed predicate devices, meaning it is similar in intended use, technological characteristics, and safety and effectiveness.
• Clinical Data: The document explicitly states: "Clinical data was not needed to support the safety and effectiveness of the subject devices." This further confirms that a performance study as would be done for an AI/ML device was not conducted or required for this clearance.

Therefore, I cannot populate the requested table and answer the specific questions about acceptance criteria, study design, ground truth, or expert involvement, as these elements are not relevant to the 510(k) clearance process for this type of medical device as described in the provided text.

The closest information available related to "performance" is:

• "Biocompatibility testing for the new colorants and materials was conducted per the recommendations of ISO 10993-1."
• "A review of the results indicates that the Disposable Instruments are equivalent to existing, legally marketed predicate instrumentation with regards to mechanical performance and that there are no new issues related to the safety and effectiveness of the subject devices."

This refers to engineering and material testing, not a clinical performance study using a test set against a ground truth as would be relevant for an AI/ML diagnostic or prognostic tool.

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