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The Intuitive Surgical Endoscopic Instrument Control System (da Vinci Surgical System, Model IS5000) shall assist in the accurate control of Intuitive Surgical Endoscopic Instruments including rigid endoscopes, blunt and sharp endoscopic dissectors, scalpels, forceps/pick-ups, needle holders, endoscopic retractors, electrocautery and accessories for endoscopic manipulation of tissue, including grasping, cutting, blunt and sharp dissection, approximation, ligation, electrocautery, suturing, and delivery and placement of microwave and cryogenic ablation probes and accessories, during urologic surgical procedures, general laparoscopic surgical procedures, gynecologic laparoscopic surgical procedures and general thoracoscopic surgical procedures. The system is indicated for adult use.

It is intended to be used by trained physicians in an operating room environment in accordance with the representative, specific procedures set forth in the Professional Instructions for Use.

Contraindication:

Use of the force feedback needle driver is contraindicated in hysterectomy and myomectomy due to the risk of vaginal bleeding requiring hospital readmission and/or the need for additional procedures. The use of non-force feedback needle drivers is recommended for suturing in these procedures.

The da Vinci Force Feedback Instruments are 8 mm, multi-use, endoscopic instruments intended for use with the da Vinci Surgical System, Model IS5000. These instruments have been designed with an articulating wrist that gives surgeons natural dexterity and range of motion, and have the ability to render forces measured at the instrument tip to the hand controls at the system Console.

The changes that are the basis for this submission are increasing the number of surgical uses (lives) and reprocessing cycles for the subject da Vinci Force Feedback Mega SutureCut Needle Driver and da Vinci Force Feedback ProGrasp Forceps compared to their respective predicate devices cleared in K232610, as summarized in Table 1 below.

The provided text describes a 510(k) premarket notification for the Intuitive Surgical da Vinci Surgical System (IS5000) and specifically for the da Vinci Force Feedback Instruments. The submission focuses on increasing the number of surgical uses and reprocessing cycles for two specific instruments: the da Vinci Force Feedback Mega SutureCut Needle Driver and the da Vinci Force Feedback ProGrasp Forceps.

However, the document does not contain acceptance criteria or detailed results of a study proving the device meets specific performance metrics in terms of clinical effectiveness or accuracy compared to a ground truth. The performance data section broadly states that "Reliability (Life) Testing" was conducted to demonstrate substantial equivalence to predicate devices, but it does not provide quantitative acceptance criteria or detailed study results typical of AI/ML device evaluations.

Therefore, many of the requested items (e.g., acceptance criteria table with reported performance, sample sizes for test sets, expert details for ground truth, adjudication method, MRMC study details, standalone performance, ground truth type, training set details) cannot be extracted from the provided text. The submission is for a modification to an existing surgical instrument regarding its durability and reprocessing cycles, not for a new AI/ML diagnostic or assistive device that would typically involve such detailed performance metrics against a ground truth.

Based on the provided information, here's what can be answered:

1. A table of acceptance criteria and the reported device performance

• Acceptance Criteria: Not explicitly stated in terms of quantitative performance metrics (e.g., sensitivity, specificity, accuracy). The acceptance criteria implicitly revolve around the instruments maintaining their functionality and safety over an increased number of uses and reprocessing cycles.
• Reported Device Performance:

  Device	Parameter	Predicate Devices (K232610)	Subject Devices (K241284)
  da Vinci Force Feedback Mega SutureCut Needle Driver	Number of Uses	1	4
  	Number of Reprocessing Cycles	3	6
  da Vinci Force Feedback ProGrasp Forceps	Number of Uses	1	4
  	Number of Reprocessing Cycles	3	6
  Note: The document states "All testing passed and supports the subject devices' increased number of uses and reprocessing cycles," implying that the devices met the internal criteria for these parameters.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not specified. The document mentions "sufficient reprocessing and simulated surgical use cycles" but does not provide specific sample sizes for instruments tested. Data provenance regarding country of origin or retrospective/prospective nature is also not mentioned.

3. 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 testing described is "Reliability (Life) Testing" involving benchtop performance measurements, visual inspection, and functional performance evaluation. It does not involve expert-established ground truth in the context of clinical interpretation or diagnosis.

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

• Not applicable/Not specified. This type of adjudication method is used for clinical interpretation ground truth, not for mechanical and functional performance testing.

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. This is not an AI/ML diagnostic device that would typically involve a MRMC study.

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

• Not applicable. This device is a surgical instrument, not an algorithm.

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

• The "ground truth" for this performance testing would be the engineering specifications and functional requirements for durability, mechanical integrity, and sensor performance over the specified number of uses and reprocessing cycles. This is based on internal testing protocols rather than clinical ground truth (like pathology or outcomes data).

8. The sample size for the training set

• Not applicable. The device is a surgical instrument with mechanical and sensor performance testing, not an AI/ML algorithm that requires a training set.

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

• Not applicable.

In summary, the provided document details a 510(k) clearance for increased durability of existing surgical instruments, not a new AI/ML device requiring clinical performance metrics against an established ground truth. Therefore, most of the requested information regarding AI/ML study design and results is not present.

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