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The United Orthopedic Knee Patient Specific Instrumentation is indicated as an orthopedic instrument system to assist in the positioning of compatible total knee arthroplasty systems. It is comprised of surgical planning software (Intelligent Surgery Knee CT Segmentation Engine/Knee X-ray Segmentation Engine, and Implant Recognition Engine) intended to preoperatively plan the surgical placement of the United Orthopedics Knee implants on the basis of provided patient radiological images and 3D reconstructions of bones with identifiable anatomical landmarks, and surgical instrument components that include patient specific or customized guides fabricated on the surgical plan to precisely reference the placement of the implant components intra-operatively per the surgical plan. The United Orthopedic Knee Patient Specific Instrumentation is indicated for patients without severe bone deformities, such as a HKA greater than 15° or deformities due to prior fracture of the distal femur or proximal tibia.

The instruments are intended for use with the U2 Total Knee System when the clinical evaluation complies with its evaluation complies with its cleared indications for use. The instruments are intended for single use only.

The United Orthopedic Knee Patient Specific Instrumentation is comprised of: United Orthopedics (UO) surgical guides (hardware), anatomical models (physical replica), Intelligent Surgery Knee CT Segmentation Engine / Intelligent Surgery Knee X-ray Segmentation Engine (software), and Intelligent Surgery Knee Implant Recognition Engine (software). Enhatch is responsible for design and development of all three components of the system.

The subject device is intended to facilitate the implantation of the U2 Knee protheses under the U2 Knee System developed and distributed by United Orthopedics Corporation: U2 Total Knee System.

[THE SOFTWARE]

The Intelligent Surgery Knee Segmentation Engine consists of two imaging modalities, CT (Knee CT Segmentation) and X-ray (Knee X-ray Segmentation). The Intelligent Surgery Knee CT Segmentation Engine and X-ray Segmentation Engine are web applications that use deep learning algorithms to detect and extract region of interest (ROI) information (femur and tibia) from medical imaging data (DICOM). The segmentation engines generate 3D models which can be used for treatment planning of Total Knee Arthroplasty (TKA), design of surgical guides, or generation of 3D printed anatomical models.

The Intelligent Surgery Knee Implant Recognition Engine is a web application that uses an optimization algorithm as a treatment planning tool for total knee arthroplasty. It assists in selecting implant size and position, from a range of implants of a total knee implant system, using a range of run parameters based upon the TKA surgical technique of that system. The software identifies anatomical landmarks of the patient's bony anatomy and articular surface topographies to reference the position and alignment of the femoral and tibial implant components. This positioning and alignment in turn allows for design of surgical guides and of the United Orthopedic Knee Patient Specific Instrumentation .

Note, these algorithms are static and non-adaptive; they do not alter their behavior over time based on user input.

[THE HARDWARE]

The UO surgical guides and anatomical models are patient-specific instruments designed to facilitate the implantation of the United Orthopedics Knee protheses. The UO surgical guides are designed based on preoperative plan generated by the software Intelligent Surgery Knee Implant Recognition Engine.

The provided text describes a 510(k) submission for the "United Orthopedic Knee Patient Specific Instrumentation" and highlights different performance tests conducted. However, the document primarily focuses on demonstrating substantial equivalence to predicate devices and general software verification and validation, rather than a detailed acceptance criteria table with reported device performance specifically for an AI algorithm's diagnostic or predictive capabilities.

The AI components mentioned are:

• Intelligent Surgery Knee CT Segmentation Engine / Intelligent Surgery Knee X-ray Segmentation Engine: Uses deep learning algorithms to detect and extract region of interest (ROI) information (femur and tibia) and generate 3D models.
• Intelligent Surgery Knee Implant Recognition Engine: Uses an optimization algorithm as a treatment planning tool for total knee arthroplasty, assisting in implant selection and position.

The information provided about the study mainly focuses on general software testing and system verification, not a specific study proving the AI's diagnostic/predictive accuracy against a gold standard in a clinical context, which is typically what is asked for in such acceptance criteria discussions for AI/ML medical devices.

Therefore, I cannot fully complete all sections of your request based on the provided text, especially regarding specific performance metrics for the AI components and clinical study details (e.g., MRMC study, human reader improvement).

Here's what can be extracted and inferred, along with what's missing:

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

The document lists general testing categories and states that the device "passed the acceptance criteria and demonstrated satisfactory performance per the intended use" for each. It does not provide specific quantitative acceptance criteria or reported numerical performance metrics for the AI algorithms (e.g., sensitivity, specificity, Dice score for segmentation, or accuracy of landmark detection against a ground truth). It describes the type of testing but not the results in detail.

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

• Sample Size: Not specified for any of the tests. The document generically mentions "specimens" and "cadaver specimens."
• Data Provenance: Not specified (e.g., country of origin).
• Retrospective or Prospective: Not specified. "Cadaver specimens" suggest an ex-vivo or lab-based study rather than a direct clinical 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)

• The document states "Full use simulations tests using cadaver specimens were performed by multiple surgeons to verify and validate the overall system performance."
• Number of experts: "Multiple surgeons," but no specific number is given.
• Qualifications: "Surgeons," but no specific experience or specialty (e.g., orthopedic surgeon, years of experience) is provided.
• It's unclear if these surgeons established the ground truth or simply evaluated the system's performance in a simulated setting. For segmentation or landmark detection, ground truth typically involves more rigorous, independently verified annotations.

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

• Not specified.

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 MRMC study described. The focus appears to be on the system's accuracy in generating models and plans, rather than its impact on human reader performance in a diagnostic context. The "multiple surgeons" evaluating the system in the System V&V test is a system performance evaluation, not an MRMC study comparing human performance with and without AI assistance for a specific task.

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

• The "Segmentation System Testing" and "Software System Testing" sections likely represent standalone algorithm testing, where the algorithm's output (segmentation masks, 3D models, landmark detection accuracy) was compared against a reference.
• However, no specific performance metrics (e.g., accuracy, precision, recall, Dice score) are provided to quantify this standalone performance.

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

• The document implies that the ground truth for "Segmentation System Testing," "Model Verification Testing," and "Software System Testing" was based on established benchmarks, comparisons to expected outputs, or potentially expert-derived ground truth for the "accuracy" claims. However, the specific method (e.g., expert consensus, manual measurements, etc.) for establishing this ground truth is not explicitly stated.
• For the "System Verification and Validation Test" using cadaver specimens and surgeons, the "satisfactory performance" seems to imply that the surgeons found the system's output acceptable and accurate for surgical planning, but the method of establishing the "true" anatomical values or optimal plans as ground truth is not detailed.

8. The sample size for the training set

• Not specified. The document mentions the use of "deep learning algorithms" but provides no details on the training data.

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

• Not specified. The document mentions deep learning but no details about the ground truth creation for training.

In summary, the provided FDA 510(k) summary focuses on general validation of a device that includes AI components, but it does not provide the highly specific, quantifiable details often required for AI/ML device submissions regarding their specific performance metrics, test set characteristics, or the rigorous establishment of ground truth that one would expect for a diagnostic AI algorithm. This submission appears to be more focused on the overall system's functionality and its role in surgical planning rather than a detailed clinical validation study of the AI's diagnostic capabilities.

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The Triathlon AS-1 patient specific cutting guides are disposable, single-use surgical instruments intended to assist orthopedic surgeons in the positioning of femoral and tibial total knee arthroplasty components intraoperatively, provided that anatomic landmarks necessary for alignment and positioning of the implant are identifiable on patient imaging scans. They are intended for use with the Cruciate Retaining (CR), Condylar Stabilizing (CS), Posterior Stabilized (PS) and Tritanium® components of the Triathlon® Total Knee System and the Total Stabilizer (TS) and Posterior Stabilized Rotation (PSR) Triathlon® tibial inserts.

The subject device, Triathlon AS-1, is a new Conformis Inc. device offering. The Triathlon AS-1 is comprised of patient matched single-use disposable cutting guides (also referred to as instruments or jigs) with corresponding surgical documentation which includes the Instructions for Use, Surgical Protocol and Surgical Plan. The single-use patient-matched instruments (including documentation) are similar to those of the legally marketed predicate device Knee Replacement Systems by Conformis Inc. (predicate devices K180906, K201023).

The subject device, Triathlon AS-1, does not include an implant or reusable instrumentation associated with knee replacement systems. The subject device, however, is designed to be compatible with selected legally marketed Triathlon Total Knee implants and reusable instrumentation from Stryker Orthopaedics. While no implant is part of this subject device, the subject device utilizes software to determine the size and position of the compatible implant for an individual patient and then design the subject device around the patient's anatomy to prepare for the identified implant.

For the subject device, the predicate Conformis design software, surgical plan, and single-use cutting guides (instrumentation) are being modified to accurately size, position, and prepare the bone for off-the-shelf Triathlon femoral and tibial implants. Using patient imaging (CT scans), a Triathlon Total Knee implant set is sized and positioned to best meet the unique anatomic requirements of the specific patient. The Triathlon AS-1 planning process allows for efficient 3D planning, providing optimized fit of an off-the-shelf Triathlon implant and providing the design of the patient-matched, single-use guides to prepare for the planned implant in the planned position.

Jigs are designed to fit the contours of the patient's femoral and tibial anatomies and to facilitate a simpler surgical technique. The design of the instrumentation will be modified as needed to be compatible with the Stryker Orthopaedics Triathlon manual surgical instrumentation.

Each set of instruments is designed for single use, specifically for one patient. The disposable single-use instrument set is manufactured from biocompatible nylon material and supplied sterile.

Note: The provided text is a 510(k) summary for a medical device (surgical cutting guides). It primarily focuses on demonstrating substantial equivalence to predicate devices and does not contain detailed information about a study proving the device meets specific acceptance criteria in the manner of an AI/ML algorithm evaluation (e.g., performance metrics like sensitivity, specificity, or AUC).

Therefore, I cannot fully address all aspects of your request, particularly those related to the performance of an AI/ML device, such as sample sizes for test/training sets, ground truth establishment methods, expert adjudication, or MRMC studies. The document describes a traditional medical device (patient-specific cutting guides) whose "performance" would relate to its physical properties, manufacturing quality, and usability.

Based on the provided text, I will address what information is available and indicate where it is not provided.

Acceptance Criteria and Study Proving Device Meets Acceptance Criteria

Overview: The provided document is a 510(k) summary for the Triathlon AS-1 patient-specific cutting guides. The "acceptance criteria" in this context are primarily related to demonstrating substantial equivalence to legally marketed predicate devices, ensuring the device is safe and effective, and performs as well as the predicate. The "study" described is a series of non-clinical tests and evaluations rather than a traditional clinical trial or AI/ML performance study.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of the device (surgical cutting guides) and the 510(k) submission, the "acceptance criteria" are not presented as quantitative performance metrics for an AI/ML model. Instead, the focus is on functional equivalence and safety.

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

• Test Set Sample Size: Not explicitly stated in the document. The testing involved "Cadaver testing" and "Implant sizing and positioning testing," but the number of cadavers or patient cases used for these tests is not provided.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). The data appears to be from non-clinical testing (cadavers, software validation).

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

• Number of Experts: Not specified. "Surgeon evaluation" is mentioned for cadaver testing, implying expert involvement, but the number or specific qualifications (e.g., "radiologist with 10 years of experience") are not detailed. Given the device, these would likely be orthopedic surgeons.

4. Adjudication method for the test set

• Adjudication Method: Not specified.

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, an MRMC study was not stated to be done. This device is a surgical cutting guide, not an AI-assisted diagnostic or interpretation tool for human readers. Its evaluation would focus on the accuracy of the cuts, fit to anatomy, and ease of use in surgery, rather than improving human "reading" performance.

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

• Standalone Performance: The device itself (the physical cutting guides) is the output of a CAD design process that utilizes patient imaging (CT scans) and an algorithm for sizing and positioning. The "software verification and validation" would assess the standalone performance of this design algorithm in terms of accurately generating the guides for the planned implant position. However, specific metrics for this standalone performance (e.g., precision of cuts, deviation from plan) are not detailed in this summary.

7. The type of ground truth used

• Type of Ground Truth: For the "Implant sizing and positioning testing" and "Software verification and validation," the ground truth would likely be based on established anatomical landmarks, implant specifications, and potentially manual measurements or a "gold standard" 3D model derived from the patient's CT scan. For "Cadaver testing (surgeon evaluation and usability)," the ground truth would be based on surgical assessment of fit, alignment, and ease of use, likely against pre-defined surgical goals or standards.

8. The sample size for the training set

• Training Set Sample Size: Not applicable or not provided. This device involves a patient-specific design process initiated from a CT scan for each individual patient, rather than a machine learning model that is trained on a large dataset and then applied. The underlying CAD design software is a rule-based or algorithmic system that may have been developed and validated over time, but the concept of a distinct "training set" in the context of an AI/ML model is not directly applicable here.

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

• Training Set Ground Truth Establishment: Not applicable, as there is no explicitly mentioned "training set" in the context of an AI/ML model. The "ground truth" for the design software would be established through engineering design principles, anatomical studies, and clinical experience informing the parameters and rules within the CAD system.

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