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The Senhance Surgical System is intended to assist in the accurate control of laparoscopic instruments for visualization and endoscopic manipulation of tissue including grasping, cutting, blunt and sharp dissection, approximation, ligation, electrocautery, suturing, mobilization and retraction in laparoscopic colorectal surgery and laparoscopic gynecological surgery. The system is indicated for adult use. It is intended for use by trained physicians in an operating room environment in accordance with the Instructions for Use.

The Senhance Surgical System is a console-based, multi-arm surgical system which enables a surgeon to remotely control surgical instrumentation during minimally invasive surgery in the lower abdomen and pelvis. The capital equipment is comprised of three main sub-systems as follows:

• Cockpit The station where the surgeon inputs information through hand and eye movements to direct the motion of the arms in the surgical field.
• Manipulator Arms Independent mechanized support arms that interface with the endoscope ● and surgical instruments. The manipulator arms produce output movements based on the instructions from the surgeon at the cockpit. The system is configurable with up to three arms.
• Node A relay unit which connects the cockpit inputs to the manipulator arms in the system as configured, and converts and transmits the video signals to the 2D/3D monitor on the cockpit console.

The document is a 510(k) summary for the Senhance Surgical System (K171120), comparing it to the predicate device, the Intuitive Surgical da Vinci Si Surgical System IS3000 (K081137). The device is an endoscopic instrument control system.

Here's an analysis of the acceptance criteria and the studies performed:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in a quantitative manner for specific metrics like sensitivity, specificity, or AUC, as would be typical for diagnostic AI devices. Instead, the substantial equivalence is determined by comparing procedural endpoints of the Senhance System's clinical data to published literature data of the predicate device. The general acceptance criterion seems to be that the Senhance System is "as safe and effective as the predicate device for its intended use" based on these comparisons.

Here's a summary of the performance data presented, compared implicitly to the predicate device's published literature:

2. Sample Sizes and Data Provenance

• Test Set (Clinical Data):

  • Gynecological Laparoscopic Surgery: 150 patients. Prospective, non-randomized clinical trial. The country of origin is not explicitly stated, but clinical trials for FDA submissions are often multinational or US-based.
  • Colorectal Laparoscopic Surgery: 45 patients. Retrospective chart review (referred to as Real World Evidence or RWE). The country of origin is not explicitly stated.

• Training Set: The document does not mention separate "training sets" for an AI algorithm. The Senhance Surgical System is a robotic surgical system, not a diagnostic AI device requiring a machine learning training set in the typical sense. The development of the system itself involved various engineering tests, including software verification and validation, human factors/usability testing, bench testing, and animal testing.

3. Number of Experts and Qualifications for Ground Truth (Test Set)

• The clinical studies involved investigators/surgeons who performed the procedures and assessed adverse events. However, no specific number of experts used to establish a ground truth for a test set (e.g., for image annotations or diagnoses) is mentioned because this is a surgical system, not a diagnostic AI. The "ground truth" for the clinical studies was derived from the actual surgical outcomes, investigator assessments of device-related events, and standard clinical endpoints (e.g., blood loss, hospital stay, complications, reoperation).

4. Adjudication Method (Test Set)

• The document mentions that adverse events were considered "device related according to the investigator's assessment." This suggests that the investigators (surgeons/clinicians) made the primary determination. There is no mention of a formal multi-expert adjudication method (e.g., 2+1, 3+1 consensus) for the clinical outcomes or adverse events in the clinical data section.

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

• No MRMC comparative effectiveness study was done. This type of study is typically for diagnostic aids or image interpretation AI where multiple human readers interpret cases with and without AI assistance. The Senhance System is a surgical tool, not a diagnostic AI.

6. Standalone (Algorithm Only) Performance Study

• No standalone algorithm performance study, in the sense of a diagnostic AI operating independently without human interaction, was performed. The Senhance System is designed to be a human-in-the-loop robotic surgical assistant, where the surgeon controls the system. Its performance is intrinsically tied to human interaction.
• However, various non-clinical tests essentially establish the "standalone" engineering performance of the system components, such as:
  • Bench testing (mechanical performance, system latency, motion accuracy, force feedback, eye sensing)
  • Electrical safety and EMC testing
  • Software Verification and Validation
  • Biocompatibility testing
  • Cleaning, Disinfection, and Sterilization validation

7. Type of Ground Truth Used (Test Set)

• The "ground truth" for the clinical studies was clinical outcomes data and investigator assessments. This includes:
  • Surgical success (successful completion of procedures)
  • Intraoperative and post-operative complications
  • Adverse events (and investigator's assessment of device relatedness)
  • Blood loss, operative time, hospital length of stay
  • Conversion rates
  • Reoperation rates, readmission rates, mortality
  • Surgical margins (for colorectal cancer cases)

8. Sample Size for the Training Set

• As explained in Point 2, there isn't a traditional "training set" for an AI algorithm in this context. The system development and validation involved various engineering, preclinical (animal), and human factors studies. The sample sizes for these are:
  • Human Factors/Usability Engineering: 16 teams of 3 users (surgeon, surgical assistant, nurse) in a porcine wet lab environment.
  • Animal Testing (Pre-clinical Design Validation): 4 teams of trained subjects (surgeon, surgical assistant) performed tasks on a live porcine model.

9. How Ground Truth for Training Set was Established

• Again, no traditional "training set" ground truth. For the human factors and animal testing, the "ground truth" was established by:
  • Successful completion of use tasks in the simulated (human factors) and live porcine (animal) environments.
  • Observation by independent usability investigators to ensure use tasks were completed without preventable errors causing harm (human factors).
  • Assessment of essential user requirements and successful completion of surgical procedures (animal testing).
  • For the engineering tests (bench, electrical, software), the "ground truth" was established by compliance with established standards, specifications, and verified functionality (e.g., IEC standards, FDA guidance for software, mechanical performance specs).

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Endoservice Endoscopic Instruments & Accessories and Minimally Invasive GI and GU Devices are intended to be used by qualified physicians to provide access and visualization of internal structures such as bladder, urethra, kidneys, prostate and for manipulating soft tissue (grasping, cutting, coagulating, dissecting, and suturing).

Endoservice Endoscopic Instruments & Accessories are comprised of rigid, panoramic telescopes using rod lens technology. The body contact portions are composed of surgical grade stainless steel, which is commonly used in medical devices for a wide range of applications and has a long history of biocompatibility. The urological-gastroenterological accessories are composed of reusable handle and shaft assemblies and removable, reusable tip assemblies. The instruments are designed and manufactured specifically for the purpose of manipulating soft tissue, body cavities, hollow organs and canals (grasping, cutting, dissecting, coagulating and suturing). The Endoservice Endoscopic Instruments & Accessories and Minimally Invasive gastrointestinal GI and genitourinary GU Devices contain different components such as Scissors, Forceps, Connections (inner Rod, Sheath insulated), Handles, Sheaths, Endoscopes, Cystoscopes, Resectoscopes, Urethrotomy, Retractor, Forceps, Scissors, Punches, Tubes, Trocars, Trocar Sleeves, Sheat, Needle holder, and Inserts. The device is made out of following materials: Body: steel 1.4301; plastic PPSU Handle: adhesive hysol-resin Lens: saphir

The provided text describes a 510(k) submission for Endoservice Endoscopic Instruments & Accessories and Minimally Invasive GI and GU Devices. This type of submission aims to demonstrate that a new device is substantially equivalent to a legally marketed predicate device. Therefore, the "acceptance criteria" here refer to demonstrating this substantial equivalence through various tests, primarily non-clinical, rather than specific performance metrics against a disease outcome.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

For a 510(k) submission, the "acceptance criteria" are typically demonstrating equivalence in design, technical characteristics, intended use, indications for use, sterilization processes, and materials, as well as meeting established performance standards for safety and effectiveness.

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

The document does not explicitly state the "sample size" in terms of number of devices or components used for each non-clinical test. It lists the types of tests conducted (Bending Tests, Material Tests, Bench Tests, Validation Tests of Sterilization, Disinfection and Cleaning, Cytotoxicity Test, Microbiological Test). The data provenance is implied to be from internal testing conducted by Endoservice Optical Instruments GmbH (a German company), and it is retrospective in the sense that these tests were performed and results compiled for the 510(k) submission.

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

This information is not provided in the document. For a non-clinical 510(k) submission for instruments like these, "ground truth" as it's typically understood in diagnostic AI studies (e.g., expert consensus on medical images) is not directly applicable. The "ground truth" here is adherence to engineering standards and safety tests, which are objective measurements rather than subjective expert interpretations.

4. Adjudication Method for the Test Set

This information is not provided and is not typically relevant for non-clinical testing of medical instruments. Adjudication methods (like 2+1 or 3+1) are usually found in clinical studies involving interpretation of data where disagreements among reviewers need resolution.

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, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is relevant for diagnostic devices, especially those using AI, where human performance with and without assistance is measured. This submission is for direct-use endoscopic instruments, not a diagnostic AI system.

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

No, a standalone (algorithm-only) performance study was not done. This is not an AI or algorithm-based device. The device itself is the instrument used by a human physician directly.

7. The Type of Ground Truth Used

The "ground truth" for this submission are the objective measurements and results from the non-clinical performance tests conforming to recognized international standards (e.g., ISO 8600, ISO 10993). This includes:

• Material properties: Verifying that materials meet specified compositions and strength.
• Dimensional accuracy: Ensuring diameters and lengths are within acceptable ranges.
• Biocompatibility: Results of cytotoxicity tests.
• Sterilization efficacy: Validation of cleaning and sterilization processes.
• Functional integrity: Results from bending and bench tests demonstrating durability and performance.
• Microbiological tests: Confirming absence of microorganisms where required.

8. The Sample Size for the Training Set

This information is not applicable as the device is not an AI/ML algorithm requiring a training set. This is a conventional medical instrument.

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

This information is not applicable as there is no training set for this type of device.

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Instrumed Laparoscopes and Accessories are intended to be used by qualified physicians to provide access, illumination and visualization of internal structures and for manipulating soft tissue (grasping, cutting, coagulating, dissecting, and suturing) in a wide variety of diagnostic and therapeutic laparoscopic/urologic closed and minimally invasive procedures.

Instrumed Laparoscopes provide illumination and visualization in diagnostic procedures and in conjunction with laparoscopic instruments (accessories) operative laparoscopic procedures

This document describes a 510(k) premarket notification for Instrumed Laparoscopes and Accessories. The submission claims substantial equivalence to a predicate device, the Gimmi Laparoscope (K012660). Therefore, the study details you are asking for, which typically involve demonstrating performance against defined acceptance criteria, are not directly presented as they would be for a novel device requiring such proof.

Instead, the submission relies on demonstrating technical similarity and that the Instrumed instruments are "absolutely safe and effective for its intended use and do not raise any questions regarding safety and effectiveness" based on performance data conducted on the Instrumed instruments. However, the specific details or content of this "performance data" are not provided in this summary.

Given the nature of a 510(k) submission seeking substantial equivalence to a predicate device, a formal study demonstrating a device meets a specific set of acceptance criteria with detailed statistical analysis and power calculations, as you might expect for a new technology, is generally not required in the same way. The focus is on demonstrating that the new device is as safe and effective as a legally marketed predicate device.

Therefore, many of the specific questions about sample size, expert ground truth, MRMC studies, standalone performance, and training set details are not applicable or not explicitly provided in this summary document focused on substantial equivalence.

Here's an attempt to address your questions based on the provided text, indicating when information is not available:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample Size: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective). The text only mentions "presented data that was conducted on the Instrumed instruments."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not applicable/Not specified. A formal expert-driven ground truth establishment for a test set is not described as part of this substantial equivalence claim.

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

• Not applicable/Not specified.

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

• Not applicable. This is not an AI-assisted device, and no MRMC study is mentioned.

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

• Not applicable. This is a medical device (laparoscopes and accessories), not an algorithm or AI.

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

• Not explicitly stated. The "performance data" would likely have involved engineering tests, material testing, and potentially some form of clinical observation or simulated use, but the nature of the "ground truth" (e.g., benchmark values, successful function, lack of adverse events) is not detailed.

8. The sample size for the training set

• Not applicable/Not specified. This is not a machine learning or AI device that would typically have a "training set."

9. How the ground truth for the training set was established

• Not applicable/Not specified as there is no "training set" described.

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