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The Versius Surgical System is a robotically assisted surgical device that is intended to assist in the precise and accurate control of Versius Surgical endoscopic instruments including rigid endoscopes, blunt and sharp endoscopic dissectors, forceps/pick-ups, needle holders, electrosurgery, and accessories for endoscopic manipulation of tissue, including grasping, cutting, blunt and sharp dissection, approximation, ligation, electrosurgery and suturing.

The Versius Surgical System is indicated for adult patients 22 years of age and older, eligible for soft tissue minimal access surgery, for cholecystectomy.

The Versius Surgical System is intended to assist in the accurate control of its surgical endoscopic instruments across a range of surgical procedures.

The system comprises (Figure 1):

• . a surgeon console,
• a visualization bedside unit .
• . up to three instrument bedside units
• . surgical attachments comprising an endoscopic camera and surgical instruments,
• . and sterile drapes

Each bedside unit includes a robot arm to which the endoscopic camera and instruments are attached.

This application is for the Versius Surgical System, a robotic surgical device. The acceptance criteria and the study that proves the device meets the acceptance criteria are detailed below. It's important to note that the provided text describes validation and verification processes across various aspects (biocompatibility, sterility, electrical safety, software, bench testing, animal testing, human factors, and clinical studies) rather than a single unified "study" with one set of acceptance criteria. The acceptance criteria are largely defined by passing these various tests and achieving specific clinical outcomes.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of the information, which includes various types of testing (non-clinical, animal, human factors, and clinical trials), I will integrate and summarize the "acceptance criteria" based on the "Results" sections for each type of study, particularly focusing on the clinical effectiveness and safety criteria.

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

• Clinical Studies (Cholecystectomy):
  • Poland Study: 30 subjects.
    • Data Provenance: Prospective, single-center, conducted in Poland.
  • India Study: 30 subjects.
    • Data Provenance: Prospective, single-center, conducted in India.
  • Combined for Suturing: 60 subjects (30 from Poland, 30 from India; total laparoscopic hysterectomy cases).
    • Data Provenance: Prospective, from Poland and India.
• Animal Testing:
  • Cholecystectomy: 3 acute, 3 chronic porcine models (6 animals total).
  • TLH (for suturing indication): 3 chronic, 3 acute porcine models (6 animals total).
  • Data Provenance: Not explicitly stated, but implies laboratory/research setting where GLP requirements were followed.
• Human Factors Testing:
  • US Validation Study: Sixteen surgical teams (number of individuals not specified, but typically includes surgeon + surgical staff).
  • Data Provenance: US-based activity, simulated operating room setting.

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

• Clinical Studies (Cholecystectomy):
  • Poland Study: 3 surgeons.
    • Qualifications: All generalists. Principal investigator: 15 years open surgery experience, no previous robotic surgery experience. Other two surgeons: ~10 years laparoscopic surgery experience, no previous robotic surgery experience.
  • India Study: 1 surgeon.
    • Qualifications: Generalist with >35 years open and laparoscopic surgery experience. Completed >500 cholecystectomy cases using OUS commercially available Versius System.
• Animal Testing: Ground truth was established by evaluation per protocol for overall animal health, test article performance, and local tissue response, likely by veterinary pathologists and study investigators. Specific numbers and qualifications of these experts are not provided.
• Human Factors Testing: "Intended users" were involved, including surgical teams. Specific qualifications beyond "surgeon" and "remainder of the surgical team" are not provided. The evaluation for "no pattern of use error" would implicitly involve human factors experts or trained evaluators.

4. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set

The document does not explicitly describe an adjudication method like 2+1 or 3+1 for the clinical outcomes or other testing results. The clinical studies describe outcomes based on the study protocol, and any "conversions" or "adverse events" are reported as observed by the investigators. For bench and animal testing, the results are simply stated as "PASS" based on expert evaluation or predefined criteria.

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 comparative effectiveness study was done regarding human reader improvement with AI assistance. The Versius Surgical System is a robotic surgical device for performing procedures, not an AI-assisted diagnostic or imaging interpretation tool that would typically involve human "readers." The focus is on the device's performance in surgical execution.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The device itself (the robotic system) operates under the control of a human surgeon. Therefore, a "standalone" performance without "human-in-the-loop performance" is not applicable in the context of the device's primary function in assisting surgery. The "standalone" bench tests demonstrate the system's inherent functional capabilities (e.g., motion accuracy, latency, component integrity) independent of surgeon interaction for some tests, but for surgical task performance (the indications for use), human interaction is fundamental.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• Clinical Studies (Cholecystectomy & Suturing): Ground truth was primarily clinical outcomes data, including:
  • Rate of unplanned conversion (primary effectiveness).
  • Rate of intraoperative and postoperative serious adverse events (primary safety).
  • Operative time, estimated blood loss, complications, hospital stay, readmission, reoperation, mortality, histopathology of removed specimen, device deficiencies, etc. (secondary endpoints).
  • For suturing, vaginal vault dehiscence at 42 days was the key outcome.
  • These are based on standard medical practice and follow-up.
• Animal Testing: Ground truth was based on animal health endpoints, test article performance, and local tissue response, likely determined through direct observation, physiological monitoring, and histopathology/gross examination of affected organs and surrounding tissue.
• Non-Clinical Bench Testing: Ground truth was established by adherence to pre-defined acceptance criteria against established engineering and safety standards (e.g., specific thresholds for motion accuracy, latency within acceptable range, stability criteria, pass/fail for biocompatibility and sterility tests).
• Human Factors Testing: Ground truth was the observation of tasks completion by surgical teams without a "pattern of use error or difficulties," judged by human factors experts and study evaluators against criteria for safe and effective interaction.

8. The Sample Size for the Training Set

The document does not explicitly provide information on a "training set" for an algorithm or AI model in the typical sense. The provided context describes verification and validation of a physical surgical system. "Training" refers to:

• Surgical Training: The sponsor developed a "Versius-specific use training program" for human users (surgeons and surgical teams). This is training for the human operators, not a data set to train an algorithm. There's no specific sample size for this human training program mentioned as a statistical "training set."
• Software Development: While software verification and validation (V&V) occurred, details about specific data sets used for training any internal algorithms (e.g., control algorithms) are not provided. The phrase "training set" is usually reserved for machine learning contexts, which isn't the primary focus here.

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

As there is no explicit "training set" for an AI algorithm mentioned, this question is not fully applicable. However, for the human training, the "ground truth" for proper device use is established by adherence to documented procedures, instructions for use, and safe practices defined by the manufacturer and medical standards, which are then reinforced and assessed through the training program and human factors validation.

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