Search Results

The Maestro System is intended to hold and position laparoscopes and laparoscopic instruments during laparoscopic surgical procedures.

The Moon Maestro System is a 2-arm system which utilizes software and hardware to provide support to surgeons for manipulating and maintaining instrument position. Motors compensate for gravitational force applied to laparoscopic instruments, while surgeon control is not affected. Conventional laparoscopic tools are exclusively controlled and maneuvered by the surgeon, who grasps the handle of the surgical laparoscopic instrument and moves it freely until the instrument is brought to the desired position. Once surgeon hand force is removed, the Maestro system reverts to maintenance of the specified tool position and instrument tip location.
This 510(k) is being is being submitted to implement 5G and WiFi capability to the previously cleared Maestro System (K242323). This modification is intended for data offload; only Telemetry and Event logs will be sent over 5G or WiFi. A PCCP is also implemented for the ScoPilot feature.

Here's a summary of the acceptance criteria and the study details for the Maestro System, based on the provided FDA 510(k) clearance letter. It's important to note that the document is focused on a modification to an already cleared device and a Predetermined Change Control Plan (PCCP), so some of the detailed information often found in initial submissions might be less explicit here.

Acceptance Criteria and Reported Device Performance

The document describes various performance tests without explicitly listing pass/fail acceptance criteria values. However, it indicates compliance with recognized standards and that validation activities were performed to pre-defined performance requirements for the ScoPilot feature. For the purpose of this summary, I'll extract the performance aspects mentioned.

Study Details

The primary study mentioned in this document relates to the validation of the ScoPilot ML model and the non-clinical bench studies for the overall system.

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

   • ML Model Validation (ScoPilot): An "independent testing dataset containing videos" was used. The specific number of videos or cases is not provided in this document.
   • Data Provenance: The document does not specify the country of origin or whether the data was retrospective or prospective. It only mentions using data "including brands unseen during training/tuning."

2. 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 the ScoPilot ML model validation.

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

   • This information is not provided in the document.

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:

   • A MRMC study or any study comparing human readers with and without AI assistance is not mentioned in this document. The device (Maestro System) is an instrument holder and positioner, and the ScoPilot feature assists in instrument tracking and positioning, not in diagnostic interpretation where MRMC studies are common.

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

   • Yes, performance testing for the ScoPilot ML model was done in a standalone manner, with the model being "trained and tuned" and then verified against "predefined performance requirements" on an "independent testing dataset." The performance metrics used were "lower bound of the 95%CI for AP and AR (Average Precision and Average Recall likely)."

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

   • For the ScoPilot ML model, the ground truth establishment method is not explicitly detailed. It would likely involve manual annotation of instrument tips and surgical tools within the video frames by qualified personnel to create the labels against which the algorithm's detection and tracking are evaluated.

7. The sample size for the training set:

   • This information is not provided in the document. The text mentions the ML model was "trained and tuned through a K-fold cross-tuning process."

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

   • This information is not explicitly detailed in the document, but it would align with the method used for the test set (likely manual annotation of surgical tools in video data). The PCCP mentions "Modification retraining the ML model with the addition of newly acquired data enables it to detect surgical instrument classes already claimed, and an increased variety of other brands in the video feed more accurately" and adding "surgical cautery hooks to the ML model class hooks as another surgical instrument class." This implies a process of labeling or annotating new data for these specific elements.

Ask a specific question about this device

The Maestro System is intended to hold and position laparoscopes and laparoscopic instruments during laparoscopic surgical procedures.

The Moon Maestro System is a 2-arm system which utilizes software and hardware to provide support to surgeons for manipulating and maintaining instrument position. Motors compensate for gravitational force applied to laparoscopic instruments, while surgeon control is not affected. Conventional laparoscopic tools are exclusively controlled and maneuvered by the surgeon, who grasps the handle of the surgical laparoscopic instrument and moves it freely until the instrument is brought to the desired position. Once surgeon hand force is removed, the Maestro system reverts to maintenance of the specified tool position and instrument tip location. This 510(k) is being submitted to implement the ScoPilot feature. ScoPilot is an on-demand, optional, ease-of-use feature of the Maestro System, allowing the laparoscope which is attached to a Maestro Arm to seamlessly follow a desired instrument tip. The surgeon remains in control of laparoscope positioning, without having to disengage from the instrument in their hand, helping maintain surgical flow and focus.

The provided text describes the Moon Surgical Maestro System, including its features and the testing performed for its 510(k) submission. However, the document does not contain a detailed table of acceptance criteria or the reported device performance against those criteria as would typically be found in a study summary with quantifiable results. It lists various tests performed but does not present the specific metrics and their outcomes in a structured format.

Therefore, I cannot fully complete the requested information for acceptance criteria and reported performance with quantitative data. I can, however, extract related information about the testing and ground truth establishment.

Here's an attempt to answer your questions based on the provided text, with limitations acknowledged:

1. Table of acceptance criteria and the reported device performance

The document states: "The ML model was trained and tuned through a K-fold cross-tuning process to optimize hyperparameters, until it reached our predefined performance requirements. An independent testing dataset containing videos was used to verify that the model performance (lower bound of the 95%CI for AP and AR) is compliant with our specification when using data including brands unseen during training/tuning."

While this indicates that performance requirements were predefined and that "AP" (presumably Average Precision) and "AR" (presumably Average Recall) were metrics, the specific numerical values for these "predefined performance requirements" (acceptance criteria) and the "compliant" reported performance are not detailed in the provided text.

Therefore, a table with specific numbers cannot be generated from the given information.

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

• Sample Size for Test Set: The document mentions "An independent testing dataset containing videos" was used. The specific number of videos or cases in this test set is not provided.
• Data Provenance: The document does not explicitly state the country of origin of the data or whether it was retrospective or prospective.

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

The document mentions "ScoPilot Vision Performance" as one of the tests. For the ML model validation, it states: "The ML model was trained and tuned... An independent testing dataset containing videos was used to verify that the model performance...". However, the document does not specify the number of experts or their qualifications used to establish the ground truth for the test set.

4. Adjudication method for the test set

The document does not describe any adjudication method (e.g., 2+1, 3+1, none) for the test set.

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

The document mentions "Human factors testing" and "Cadaver testing." However, there is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study evaluating how much human readers improve with AI vs. without AI assistance. The described "ScoPilot" feature is an "on-demand, optional, ease-of-use feature" that allows the laparoscope to follow a desired instrument tip, aiming to help "maintain surgical flow and focus." This implies a focus on a specific functionality rather than a broad comparative effectiveness study with human readers.

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

Yes, a standalone performance evaluation of the ML model was performed. The text states: "An independent testing dataset containing videos was used to verify that the model performance (lower bound of the 95%CI for AP and AR) is compliant with our specification when using data including brands unseen during training/tuning." This describes an algorithm-only evaluation.

7. The type of ground truth used

For the "ScoPilot Vision Performance" and ML model validation, the ground truth would likely involve annotated video frames where the "desired instrument tip" is precisely identified. The text mentions "detection and tracking of specified instrument tips." However, it does not elaborate on how these ground truth annotations (e.g., expert consensus, pathology, outcomes data) were generated. Given the nature of the device (laparoscopic instrument tracking), it would most likely be based on expert manual annotation of video frames.

8. The sample size for the training set

The document states: "The ML model was trained and tuned through a K-fold cross-tuning process to optimize hyperparameters..." The specific sample size (number of videos/frames) for the training set is not provided.

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

The document states "Machine Learning methodology used to develop software algorithm responsible for identifying tool tip." While it indicates that an ML model was trained to identify the tool tip, it does not explicitly state how the ground truth was established for this training set. Similar to the test set, it would logically involve expert annotation of video data to delineate the "tool tip."

Ask a specific question about this device

The intended use of the SOLOASSIST II is a robotic computer driven system whose function is to hold and position a rigid laparoscope / endoscope.

The SOLOASSIST II is indicated for use in minimally invasive interventions where a rigid laparoscope / endoscope is indicated for use. Surgeries, SOLOASSIST II is used, are laparoscopic cholecystectomy, laparoscopic hernia repair, laparoscopic appendectomy, laparoscopic pelvic lymph node dissection, laparoscopically assisted hysterectomy, laparoscopic & thorascopic, decompression fixation, wedge resection, lung biopsy, pleural biopsy, dorsal sympathectomy, pleurodesis, internal mammary artery dissection for coronary artery bypass grafting where endoscopic visualization is indicated and examination of the evacuated cardiac chamber during performance of valve replacement.

The users of the SOLOASSIST II are general surgeons, gynecologists, cardiac surgeons, thoracic surgeons and urologists.

The intended use of the DEXTER ENDOSCOPE ARM is a robotic computer driven system whose function is to hold and position a rigid laparoscope / endoscope.

The DEXTER ENDOSCOPE ARM is indicated for use in minimally invasive interventions where a rigid laparoscope / endoscope is indicated for use. Surgeries, DEXTER ENDOSCOPE ARM is used, are laparoscopic cholecystectomy, laparoscopic hernia repair, laparoscopic appendectomy, laparoscopic pelvic lymph node dissection, laparoscopically assisted hysterectomy, laparoscopic & thorascopic, 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.

The users of the DEXTER ENDOSCOPE ARM are general surgeons, gynecologists, cardiac surgeons and urologists.

The intended use of the ARTip solo is a robotic computer driven system whose function is to hold and position a rigid laparoscope / endoscope.

The ARTip solo is indicated for use in minimally invasive interventions where a rigid laparoscope / endoscope is indicated for use. Surgeries, ARTip solo is used, are laparoscopic cholecystectomy, laparoscopic hernia repair, laparoscopic appendectomy, laparoscopic pelvic lymph node dissection, laparoscopically assisted hysterectomy, laparoscopic, decompression fixation, wedge resection, lunq 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.

The users of the ARTip solo are general surgeons, gynecologists, cardiac surgeons and urologists.

DEXTER ENDOSCOPE ARM and ARTip solo + ARTip solo voice are based on the same concept as the already cleared SOLOASSIST II + Voice Control (K200473) and differ only in minor details. All 3 robotic arm systems emulate an arm operating in multiple degrees of freedom. The intended use of the three robotic arm systems is a robotic computer driven system whose function is to hold and position a rigid laparoscope / endoscope. The surgeon can control the arm with either a joystick, voice control, or by pressing a button on the distal end of the arm. Movement is controlled by 3 motorized axes. The endoscopic camera is registered in the TROCAR POINT, which serves as the pivot point. Starting from this zero point, the device independently calculates the required individual movements of the axes to achieve the desired overall movement. The system offers a wide range of motion, allowing a 360° panoramic view with the endoscope tilted up to 90° from the vertical. Despite their wide range of motion, all arms are lightweight and compact and are attached directly to the operating table with a quick-release clamp. Only a STERILE COVER is required for safe use.

The ARTip solo is functionally identical to the already cleared SOLOASSIST II (K200473), but differs only in the color and inscription of the covers and the visual design of the control panel on the arm.

The ARTip solo can also be controlled with the ARTip solo voice. This is also identical to the already cleared VOICE CONTROL (K200473) except for the color and inscription. The ARTip solo is not compatible with the VOICE CONTROL (K200473).

In addition, the ARTip solo can also be controlled with a joystick. It can only be controlled with the JOYSTICK-IH-IA or JOYSTICK-RH-IIA, which are specially made for the ARTip solo and are only compatible with it.

DEXTER ENDOSCOPE ARM is the trade name of the SOLOASSIST IID, which was developed within the SOLOASSIST II. The special feature of the DEXTER ENDSCOPE ARM is that it provides an interface to a customer-specific device and can be controlled by it.

The arm can also be controlled by the JOYSTICK LH and JOYSTICK RH.

The SOLOASSIST IID has its own CANTILEVER, JOINT, PROBE PIN and ENDOSCOPE CLAMP, which in combination with a tension sleeve hold the endoscope.

I am sorry, but the provided text does not contain the detailed information necessary to complete some sections of your request. Specifically, it lacks data on:

• Specific acceptance criteria for individual performance metrics.
• Sample sizes for the test set.
• Data provenance (country of origin, retrospective/prospective).
• Number and qualifications of experts for ground truth.
• Adjudication method.
• Multi-reader multi-case (MRMC) comparative effectiveness study, including effect size.
• Standalone algorithm performance.
• Type of ground truth for the test set.
• Sample size for the training set.
• How ground truth for the training set was established.

The provided text focuses on demonstrating substantial equivalence to a predicate device based on technological similarity and non-clinical testing. It states that new devices "can be classified as equally safe and effective as the predicate device" based on these tests, but does not provide specific performance metrics or detailed study designs for acceptance criteria.

Therefore, I cannot populate all the requested fields. However, I can provide the information that is present in the document which describes the testing and conclusions regarding the devices' safety and effectiveness.

Here's a summary of the available information based on your request:

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

The document does not explicitly list specific numerical acceptance criteria or quantitative performance metrics for the device. Instead, it refers to non-clinical tests carried out with predicate devices and additional tests for the new devices to "prove the safety and effectiveness with regard to the differences to the predicate device." The conclusion is that the new devices are "as safe and effective as the predicate device."

• Acceptance Criteria: Not explicitly stated with numerical values. The implicit acceptance criterion is likely to demonstrate equivalence in safety and effectiveness to the predicate device through successful completion of the listed non-clinical tests and software verification.
• Reported Device Performance: The document concludes that "The non-clinical tests have shown that the SOLOASSIST IID, ARTip solo voice and SOLOASSIST II + VOICE CONTROL are as safe and effective as the predicate device." No specific quantitative performance data (e.g., accuracy, precision, error rates) are provided.

Table of Acceptance Criteria and Reported Device Performance (Based on provided text, specific metrics are not detailed)

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

The document does not specify sample sizes for test sets, nor does it provide information on data provenance (country of origin, retrospective/prospective) as the tests described are non-clinical, likely bench testing.

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. The tests mentioned are non-clinical (e.g., temperature, lifetime, functional tests) and do not involve expert-established ground truth in the context of diagnostic interpretation.

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

Not applicable, as expert adjudication for ground truth is not mentioned for the non-clinical tests.

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. The device (SOLOASSIST IID / DEXTER ENDOSCOPE ARM, ARTip solo, ARTip solo voice, SOLOASSIST II, VOICE CONTROL) is described as a robotic computer-driven system to hold and position endoscopes, not an AI-assisted diagnostic or interpretative tool for human readers. Therefore, an MRMC study comparing human reader performance with and without AI assistance is outside the scope of this device's intended use and the provided documentation.

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

This refers to a standalone performance of the entire device system (robotic arm with its software and controls), not a standalone algorithm in the context of AI diagnostic performance. The non-clinical tests described (e.g., temperature, lifetime, functional tests for various components and control methods) represent tests of the device in its standalone (intended operational) capacity, without human intervention for evaluation of diagnostic output. The device itself is designed to be operated by a human surgeon.

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

For the non-clinical tests conducted, the "ground truth" would be established by engineering specifications, physical measurements, and functional requirements of the device and its components (e.g., a power supply providing the correct voltage, a quick-release connector functioning as designed, voice commands being correctly interpreted). It is not expert consensus, pathology, or outcomes data in the medical diagnostic sense.

8. The sample size for the training set

Not applicable, as the document describes a robotic surgical assistant system and associated controls, not an AI algorithm that requires a "training set" in the machine learning sense for diagnostic purposes. The software changes mentioned are related to bug fixes, parameter additions, and compatibility checks, not statistical model training.

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

Not applicable, for the same reason as point 8.

Ask a specific question about this device