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The Stryker SDC3 HD Information Management System (SDC3) is intended for use with compatible endoscopic and general surgery devices. SDC3 can be used in general laparoscopy, nasopharyngoscopy, ear endoscopy, sinuscopy, and plastic surgery wherever a laparoscope, endoscope, or an arthroscope is indicated for use. A few examples of the more common endoscopic surgeries are laparoscopic cholecystectomy, laparoscopic hernia repair, laparoscopic appendectomy, laparoscopic pelvic lymph node dissection, laparoscopically assisted hysterectomy, laparoscopic & thorascopic anterior spinal fusion, anterior cruciate ligament reconstruction, knee arthroscopy, shoulder arthroscopy, small joint arthroscopy, decompression fixation, wedge resection, lung biopsy, pleural biopsy, dorsal sympathectomy, pleurodesis, internal mammary artery dissection for coronary artery bypass, coronary artery bypass grafting where endoscopic visualization is indicated and examination of the evacuated cardiac chamber during performance of valve replacement. SDC3 users are general surgeons, gynecologists, cardiac surgeons, thoracic surgeons, plastic surgeons, orthopedic surgeons, and urologists.

The Stryker SDC3 HD Information Management System (referred to as "SDC3 system" in the following sections) is a medical device that allows the surgeon to control the state, selection, and settings of any compatible device attached to it. It also has operating room documentation functionalities (Class I device function) to electronically capture, transfer, store and display medical device data independently of the functions or parameters of any connected medical device.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Stryker SDC3 HD Information Management System:

Summary of Acceptance Criteria and Device Performance (Limited Information Provided)

The document provides very high-level information about "performance testing" but does not detail specific acceptance criteria values or specific device performance results in a quantitative manner. The acceptance criteria are broadly described as being in accordance with "internal design specifications" and "applicable performance standards." The reported performance is generally stated as "conforms" or "successfully passed."

Detailed Study Information:

The document describes various performance tests conducted, but it does not present a typical clinical study or a study specifically designed to establish acceptance criteria with quantitative outcomes. Instead, it refers to internal verification and validation activities.

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

   • Test Set Sample Size: Not specified. The document mentions "verification/validation testing" and "simulated use testing," but does not provide any sample sizes in terms of number of users, cases, or data points used for these tests.
   • Data Provenance: Not specified. Given that these are internal verification and validation tests, the data would likely originate from Stryker's internal testing environments. It's not a retrospective or prospective clinical study on patient data.

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):

   • Not applicable/Not specified. The document does not describe a process of establishing ground truth by external experts for a clinical test set. The ground truth for internal performance testing would be defined by engineering specifications and expected behavior.

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

   • Not applicable/Not specified. This type of adjudication method is used in studies involving human reviewers and ambiguous cases, which is not described here.

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:

   • No MRMC comparative effectiveness study was done or reported in this document. The SDC3 system is an information management and control system, not an AI-assisted diagnostic tool for "human readers." Its purpose is to control other surgical devices and manage documentation.

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

   • The document describes "system level testing and simulated use testing" which implies testing the algorithm's performance in isolation or in simulated environments. However, the term "standalone" performance usually refers to diagnostic algorithms operating without human intervention for decision-making. In the context of a control and information management system, the "standalone" performance would be its ability to accurately execute commands, capture data, and maintain system integrity, which is implicitly covered by the various verification/validation tests mentioned (voice recognition, device control, digital documentation). No specific "standalone" study is detailed with quantitative results.

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

   • The term "ground truth" as typically used in medical AI studies (e.g., for disease detection) is not directly applicable here. For the types of tests described (voice recognition, device control, digital documentation), the "ground truth" would be established by:
     • Voice Recognition: Correct interpretation of spoken commands based on predefined linguistic models.
     • Device Control: Accurate and timely execution of commands to connected devices as per specifications.
     • Digital Documentation: Correct capture, storage, transfer, and display of data as per functional requirements and data integrity standards.
     • Electrical Safety/EMC: Compliance with the pass/fail criteria of the relevant IEC standards.
     • Software Validation: Adherence to software requirements specifications and absence of bugs, verified by unit, integration, and system-level tests.

7. The sample size for the training set:

   • Not applicable/Not specified. The document does not describe the use of machine learning that would require a distinct "training set." While voice recognition technology does involve training, the details of such a training set are not provided in this 510(k) summary, which focuses on validation of the final product.

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

   • Not applicable/Not specified. As no training set is described for the device's functionality, the method for establishing its ground truth is also not provided.

Conclusion based on the provided text:

The provided document, a 510(k) summary, primarily seeks to establish substantial equivalence to a predicate device (Stryker SIDNE™ System). It details various engineering verification and validation activities rather than a clinical study with quantitative acceptance criteria and performance metrics. The focus is on demonstrating that the device conforms to internal specifications and relevant industry standards (e.g., ISO, IEC), and that any differences from the predicate device do not raise new questions of safety or effectiveness. Specific numerical acceptance criteria, clinical study designs, expert ground truth establishment, or sample sizes for testing beyond general statements of "verification/validation testing" are not included in this summary.

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