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Smart Stitching software is indicated for use to allow post-capture positioning and joining of individual images to produce an additional composite image for two dimensional image review.

Smart Stitching provides a function to read multiple medical images at one time by stitching them to one image based on overlapping areas. It supports DICOM 3.0 which is standard of medical image format as well as Tiff and Raw images. It provides additional functions such as image retrieval, storage, and transmission. Smart Stitching is designed to facilitate diagnosis by easily stitching multiple medical images, up to 5 images, from multiple captures to one image. Smart Stitching software does not serve as acquisition software nor has control function for acquisition setting.

This FDA 510(k) summary for the OSKO Smart Stitching Software System provides limited details about specific acceptance criteria and the study that rigorously proves the device meets those criteria with statistical significance. However, based on the provided text, here's an attempt to extract and infer the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or a detailed "reported device performance" against those criteria. It refers to "predetermined acceptance criteria" and successful completion of "in house testing criteria." The primary function described is "auto stitching," so the implicit criterion would be the accurate and functional joining of images.

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

• Sample Size: Not explicitly stated. The document mentions "software validation testing," but no specific number of images or cases used in this testing is provided.
• Data Provenance: Not explicitly stated. Given it's "in house testing criteria" and no clinical testing was performed, the data could be internal test data, possibly simulated or representative images. There is no mention of country of origin, retrospective, or prospective data collection.

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

• Number of Experts: Not mentioned.
• Qualifications of Experts: Not mentioned.
• Ground Truth Establishment: The document refers to "in house testing criteria" and "predetermined acceptance criteria," suggesting that acceptance was determined by the manufacturer's internal quality and testing procedures, rather than an independent expert panel establishing a separate ground truth for a test set.

4. Adjudication Method for the Test Set

Not applicable/Not mentioned. There is no mention of a formal adjudication process involving multiple reviewers for establishing ground truth, as no clinical testing or reader study is described. The acceptance seems to be based on internal software validation against pre-defined engineering and functional specifications.

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

• MRMC Study: No, an MRMC comparative effectiveness study was explicitly not done. The document states: "No clinical testing is performed."
• Effect Size: Not applicable, as no MRMC study was conducted. Thus, there is no effect size reported for human readers improving with AI assistance.

6. If a Standalone Study (algorithm only without human-in-the-loop performance) was Done

Yes, implicitly. The "software validation testing" and "auto stitching function and SW compatibility test" described are standalone tests of the algorithm's functionality and performance against its intended design. These tests were conducted without human-in-the-loop performance evaluation in a clinical setting.

7. Type of Ground Truth Used

The ground truth for the device's functionality appears to be based on:

• Engineering Specifications/Design Intent: The software's ability to "allow post-capture positioning and joining of individual images to produce an additional composite image for two dimensional image review."
• Internal Validation Standards: "all in house testing criteria" and "predetermined acceptance criteria."
• Functional Correctness: Verification that the "software functions as intended" and performs its defined input/output operations.

It is not based on expert consensus, pathology, or outcomes data, as no clinical testing was performed.

8. Sample Size for the Training Set

Not mentioned. The document focuses on validation/testing rather than the development or training process of any machine learning components (although it's referred to as "Smart Stitching Software System, Image Processing," implying some algorithmic intelligence, details of its training are absent).

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

Not mentioned, as details about a training set are not provided.

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