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The Pre-Sure software system is intended for use as a software interface and image segmentation system for the transfer of DICOM imaging information from a medical scanner to an output file. It is also intended as pre-operative software for surgical planning. For this purpose, the output file may be used to produce a physical replica is intended for adjunctive use along with other diagnostic tools and expert clinical judgment for diagnosis, patient management, and/or treatment selection of genitourinary, cardiovascular, neurological, respiratory, musculoskeletal, gastrointestinal, craniofacial, and pediatric applications.

The Pre-Sure patient modeling system is a method for the creation of patient models. Importantly, and unlike other 3D modeling systems, in the Pre-Sure process the design and production of patient models is performed by Lazarus 3D or its affiliates (with physician input), and not by the End User. The Pre-Sure software process is FDA cleared for the analysis of patient radiological data to create a digital patient design, referred to as a "Digital Twin". Digital Twins can be used by End Users, when viewed in an appropriately cleared viewing program, for a variety of uses including training, education, and surgical planning.

Patient specific Digital Twins may be further used as an input to a 3D printing-based production process performed by Lazarus 3D. This process creates physical patient models, referred to as Pre-Sure models. Each individual patient's model can be created rapidly from the patient's radiological data. The resulting physical models of patient anatomy are primarily composed of silicone materials that can be sutured, and in some cases can even bleed, and of hard plastic materials that can be sawed, drilled, and can accept screws.

End Users of the Preoperative Surgical Rehearsal (Pre-Sure) device receive digital and/or physical patient anatomical models from Lazarus 3D. Digital models may be viewed by End Users using any program cleared for their intended use. The physical models are intended for adjunctive use along with other diagnostic tools and expert clinical judgment for diagnosis, patient management, and/or treatment selection for genitourinary, cardiovascular, craniofacial, gastrointestinal, neurological, musculoskeletal, respiratory, and pediatric applications.

The provided document describes the Pre-Sure device, a software system and physical replica production process for surgical planning and adjunctive use with other diagnostic tools. The document primarily focuses on non-clinical performance data (accuracy studies and materials testing) to support the device's substantial equivalence to a predicate device.

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance:

The document states that during the various accuracy and materials testing, "measurements fell within predefined acceptance criteria" and "Results show that sufficient accuracy can be achieved" or "can reproduce complex [anatomical] conditions with sufficient accuracy for the intended use." However, it does not explicitly define these predefined acceptance criteria or provide specific quantitative performance metrics for each study. It only offers qualitative statements of success.

Since the document does not provide a table with specific acceptance criteria and detailed quantitative performance results, I will summarize the findings qualitatively based on the text.

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

• Test Set Sample Sizes:
  • Production Process Accuracy Study: "Models of various dimensions were created." Specific number not provided.
  • Digital and Physical Model Accuracy Study: "MRI and CT scan input data from actual cases were analyzed." Specific number not provided.
  • Build Envelope Testing: "Models of genitourinary conditions were built using 3D prints from different locations... and in different orientations." Specific number not provided.
  • Materials Testing: "genitourinary conditions using combinations of the most difficult to use materials..." Specific number not provided.
  • Challenging Case Studies (all anatomical regions): "Multiple replicates were produced for several different [anatomical] conditions." Specific numbers not provided for cases or replicates.
  • Hard and Soft Material Bonding: "Many models with worst case designs and materials..." Specific number not provided.
• Data Provenance: The document implies the use of "actual cases" for the "Digital and Physical Model Accuracy Study," suggesting real patient data, but it does not specify the country of origin, nor does it explicitly state if the data was retrospective or prospective. For the "Production Process Accuracy Study," it refers to "dimensions pre-defined in silico," indicating synthetic/computer-generated data. The other studies involve manufacturing physical models based on designs, but the origin of those designs is not detailed in terms of patient data.

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

The document does not explicitly mention the number of experts used or their qualifications for establishing ground truth in any of the accuracy studies. The comparisons are described as being against "in-silico input data" or "digital design" for physical models, and against the predicate device's output.

4. Adjudication Method:

The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) used for reviewing or establishing ground truth for the test sets.

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

No MRMC comparative effectiveness study is mentioned. The studies described are focused on the accuracy of the device's output (digital designs and physical models) compared to expected values or the predicate device, not on human reader performance with or without AI assistance.

6. Standalone (Algorithm Only) Performance Study:

The document describes studies that evaluate the accuracy of the "Pre-Sure patient model production processes" and "the entire Pre-Sure workflow" from MRI/CT input to physical models. This evaluates the performance of the system as an algorithm/process, independent of human interaction during the model generation itself but for use alongside expert clinical judgment. However, the exact "standalone" performance of the software interface and image segmentation system component (which is part of the device indications for use) is not separately detailed with specific metrics beyond "measurements fell within predefined acceptance criteria." The primary focus is on the output model accuracy.

7. Type of Ground Truth Used:

• Production Process Accuracy Study: "In-silico input data" (computer-generated/defined ground truth).
• Digital and Physical Model Accuracy Study: Implicitly, the initial medical image data (MRI/CT scans) and likely the output from the predicate device serve as a reference point for comparison. The definition of "ground truth" for this study is not explicitly stated but appears to be based on consistency with the source data and predicate.
• Challenging Case Studies (all anatomical regions): The "digital design" is used as the ground truth against which the manufactured physical models are compared volumetrically via CT scanning.

8. Sample Size for the Training Set:

The document does not provide any information about the sample size used for a training set. The descriptions focus on V&V (Verification and Validation) testing rather than algorithm development/training data.

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

Since no information on a training set is provided, there is no information on how its ground truth was established.

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