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The Signia™ Stapler, when used with Endo GIA™ single-use reloads, Endo GIAM single-use reloads with Tri-Staple™ Technology. Tri-Staple™ 2.0 single-use reloads and Sigma™ loading with Tri-Staple™ 2.0 single-use cartidges. has applications in abdominal, gynecological, pediatic, and thoracic surgery for resection, and creation of anastomosis. It may be used for transection of liver substance, hepatic vasculature, and biliary stuctures and for transection and resection of the pancreas.

The Signia™ Stapler, when used with Endo GIA™ curved tip single use reloads or Tri-Staple™ 2.0 curved tip single-use reloads, can be used to blunt dissect or separate target tissue from other certain tissue.

The Signia TM Stabler, when used with single use Radial Reloads with Tri-Staple™ Technology, has applications in open or minimally invasive general abdominal, gynecologic, pediatric and thoracic surgery for resection and transection of tissue and creation of anastomosis, as well as application deep in the pelvis, i.e., low anterior resection. It may be used for transection of liver substance, hepatic vasculature and biliary structures and for transection and resection of the pancreas.

The Signia™ Stapler, when used with Endo GIA™ single use reinforced reloads with Tri-Staple™ Technology preloaded with polyglycolic acid staple line reinforcement or Tri Staple™ 2.0 single use reinforced reloads preloaded with polyglycolic acid staple line reinforcement, has applications in abdominal, gynecologic, pediatric and thoracic surgery for resection, transection of tissue and creation of anastomosis. It may be used for transection of liver substance, hepatic vasculature and biliary structures, and for transection and resection of the pancreas.

The Signia™ Stapler is a battery powered microprocessor controlled surgical stapler that provides pushbutton powered maneuverability and firing of compatible Covidien stapling reloads. The Signia™ Stapler when used with a compatible reload is a surgical device for stapling and cutting tissues. The Signia™ Stapler is intended to be used by medical professionals qualified in the transportation, preparation, cleaning, sterilization, and use of surgical devices. All of which stay unchanged when compared to the predicate device.

The Signia™ Stapler is composed of the Signia™ Power Handle, Signia™ Power Shell, and Signia™ Linear Adapter. System accessories include the Signia™ Reusable Insertion Guide, Signia™ Manual Retraction Tool, Signia™ Single Bay Charger, Signia™ Sterilization Tray (optional), and Signia™ Four-Bay Smart Charger (optional).

The design modification is to introduce the new software version for the Signia™ Power Handle. The new software will extend the real time force gauge display on the Signia™ Power Handle for the non-intelligent reloads that are already in the market without an ID chip. Currently, the force gauge is displayed only when an intelligent reload with an ID chip is attached to the Signia™ Stapler. By installing the new software, the force gauge will be displayed consistently for all compatible Covidien reloads. This is a user interface/display change but does not impact how the handle operates.

The FDA 510(k) summary for the Signia™ Stapler with new software (K201672) does not include specific acceptance criteria or an explicit study proving the device meets those criteria in the way typically found for diagnostic algorithms. This submission is for a modification to an existing surgical stapler, primarily focusing on a software update that affects the user interface and display of a force gauge. Therefore, the details requested in your prompt (e.g., sample size for test sets, ground truth establishment, MRMC studies) are not directly applicable or present in this document.

However, I can extract the relevant information regarding the software change and the types of studies performed to demonstrate substantial equivalence.

Here's a breakdown of the available information in the context of your request:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria for the software change in terms of metrics like sensitivity, specificity, or accuracy, which are common for diagnostic AI. Instead, the "acceptance criteria" are implied by the demonstration of substantial equivalence through verification and performance testing that the software change does not negatively impact safety or effectiveness.

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

The document states: "Non-clinical performance data such as software verification and performance testing have demonstrated substantial equivalence to the predicate device."

• Test set sample size: Not specified. The testing would likely involve internal software verification and validation (V&V) tests, which might use various simulated scenarios or bench test results rather than a "test set" in the sense of clinical data.
• Data provenance: Not explicitly stated as country of origin. Given it's "non-clinical performance data" and "software verification and performance testing," it would be internal engineering and quality testing rather than retrospective or prospective clinical data.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of experts: Not applicable/not specified. For software verification and performance testing of a device's functional display, the "ground truth" is typically defined by the device's design specifications and expected behavior, verified by engineers.
• Qualifications of experts: Not applicable/not specified.

4. Adjudication Method for the Test Set

• Adjudication method: Not applicable/not specified. This type of software change would not typically involve expert adjudication in the way clinical diagnostic studies do.

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

• MRMC study: No. The document explicitly states: "Clinical performance data – No clinical study has been performed." This is not a diagnostic AI device requiring comparison with human readers.

6. Standalone Performance (Algorithm Only Without Human-in-the-Loop Performance)

• This concept is not directly applicable in the terms of a standalone diagnostic algorithm. The software update is for a component of a surgical stapler, which is always used by a human surgeon. The "performance" being evaluated is whether the software correctly displays the force gauge for non-intelligent reloads without affecting the device's core functionality or safety. This is a functional and safety assessment within the device, not a standalone diagnostic output.

7. Type of Ground Truth Used

• Type of ground truth: The "ground truth" for this software modification would be the design specifications and expected functional behavior of the device's display and operation. The verification and performance testing would confirm that the software change aligns with these predefined specifications and does not introduce new safety or effectiveness concerns.

8. Sample Size for the Training Set

• Training set sample size: Not applicable/not specified. As this is a software update for a medical device's user interface, it is likely developed through traditional software engineering processes, not machine learning that requires a "training set" in the AI sense.

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

• Ground truth for training set: Not applicable. (See point 8).

In summary: The FDA 510(k) for the Signia™ Stapler with new software focuses on demonstrating that a software update to extend display functionality does not alter the device's intended use, indications, or fundamental safety and effectiveness profile. The "study" referenced is non-clinical performance and software verification testing, which confirms adherence to design specifications rather than clinical outcomes or diagnostic accuracy comparisons.

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