Search Results

The AutoLap System is indicated for General laparoscopic, Gynecologic and Urologic procedures for the purpose of holding and controlling the movement of a standard laparoscope or rigid endoscope within surgical cavities during minimally invasive laparoscopic surgery.

The AutoLap System is designed to hold and control and the movement of a laparoscope or rigid endoscope during laparoscopic procedures. The AutoLap System includes both software and hardware. Hardware components include a Processing Unit, which includes the system's electronics and software algorithms, a Base Unit and a Laparoscopic Unit that include motors and sensors. The Base Unit is mounted on the operating bed. An accessory cart that is used for placing the system's components during the procedure and their storage afterwards is also provided.

To maneuver the laparoscope to the desired position, the surgeon presses a single button, referred to as Command Unit, which is affixed to his/her hand or to the surgical instrument and transmits RF signals to the system. The movement of the laparoscope may be controlled as a simple joystick, enabling movements to up/down, left/right, zoom out directions or be guided by the movement of a designated tool within the field of view, enabling movement in oblique trajectories. Additionally, the system enables the surgeon to manually perform larger movements of the laparoscope using the Manual Activation Button affixed to the AutoLap system.

The provided text describes a 510(k) premarket notification for the AutoLap System, a device designed to hold and control the movement of a laparoscope during minimally invasive surgery. It details performance testing conducted but does not specify acceptance criteria in a quantitative table or the details of a study that directly proves the device meets such criteria for diagnostic accuracy or clinical effectiveness in the way an AI diagnostic device typically would.

However, based on the provided text, here's an attempt to structure the information according to your request, with the understanding that many specific details you asked for (e.g., sample sizes for test sets, number of experts for ground truth, MRMC studies) are not present in this type of FDA document for a device primarily focused on mechanical assistance.

Overview of the Device and Study Purpose:

The AutoLap System is a robotic laparoscope holder and controller. The premarket notification (K152848) is for a modified version of an already cleared device, with the primary changes being an added mode of movement control (guided by a designated tool within the field of view) and the ability to connect a separate monitor for parallel display. The "study" described is a series of performance tests to demonstrate the safety and effectiveness of the modified device.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in the format of a table with specific thresholds for sensitivity, specificity, accuracy, or similar metrics typically associated with AI diagnostic devices. Instead, the performance evaluation for this robotic system focuses on safety, functionality, and meeting engineering specifications.

Overall Conclusion of Performance Testing: "All performance testing demonstrates that the modified AutoLap System performs according to specifications and functions as intended."

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

• Test Set Sample Size: Not specified. The document refers to "bench testing" and "animal testing" but does not quantify the number of tests performed or the number of animal subjects.
• Data Provenance: The device manufacturer, M.S.T. Medical Surgery Technologies Ltd., is located in Israel. The testing was conducted to evaluate the modified device. It is implied these were prospective tests specifically conducted for this 510(k) submission.

3. Number of Experts Used to Establish Ground Truth and Qualifications

This information is not applicable and not provided in the document. The type of device (robotic laparoscope holder) and the nature of the testing (electrical, EMC, bench, animal for functionality) do not typically involve human experts establishing diagnostic "ground truth" for a test set in the way an AI diagnostic algorithm would. The "ground truth" for these tests would be objective measurements and observations of the device's physical and electronic performance against engineering specifications.

4. Adjudication Method for the Test Set

Not applicable and not specified. Adjudication methods like 2+1 or 3+1 are used for human-reviewed data to establish ground truth from multiple readers. This device's testing did not involve such a process.

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

• Was it done? No, an MRMC comparative effectiveness study was not performed.
• Effect Size of Human Readers Improve with AI vs. Without AI Assistance: Not applicable, as this is a robotic surgical assistant, not an AI diagnostic tool designed to assist human readers in image interpretation. The device assists the surgeon by holding and moving the laparoscope, thereby potentially freeing up a human assistant or improving camera stability, but its performance isn't measured in terms of improving human reader diagnostic accuracy.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Was it done? The "bench testing" and "animal testing" represent the closest equivalent to a standalone performance evaluation, as they assess the device's intrinsic mechanical and algorithmic capabilities. The description of these tests ("Image stability," "Image processing, system functionality, and performance to move the camera," "performance of the added mode of movement control") focuses on the device's direct output and function. However, it's not a standalone diagnostic performance study as would be conducted for an AI image analysis tool.

7. Type of Ground Truth Used

The "ground truth" for the tests performed was based on:

• Compliance with recognized electrical safety and EMC standards (ANSI/AAMI 60601-1:2005, IEC 60601-1-2:2007).
• Engineering specifications for image stability, system functionality, camera movement, and video display connection.
• Observed functional performance in an animal model for the new mode of movement control.

This is fundamentally different from ground truth derived from pathology, expert consensus, or outcomes data, which are typical for diagnostic imaging AI.

8. Sample Size for the Training Set

• Training Set Sample Size: Not applicable. The AutoLap System is a robotic device with integrated software and hardware. The document describes modifications to an existing device and its performance testing, but it does not refer to a machine learning model that requires a distinct "training set" in the conventional sense of AI development for diagnostic tasks. Its "training" is more akin to traditional software development and calibration processes, which are not quantified in typical dataset terms.

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

Not applicable, as there is no specific "training set" with associated "ground truth" in the context of machine learning model training described for this device. The software algorithms and controls are likely developed and validated through engineering principles and testing against defined operational specifications rather than through supervised learning with labeled data.

Ask a specific question about this device

The AutoLap System is indicated for General laparoscopic, Gynecologic and Urologic procedures for the purpose of holding and controlling the movement of a standard laparoscope or rigid endoscope within surgical cavities during minimally invasive laparoscopic surgery.

The AutoLap System is designed to hold and control the movement of a laparoscope or rigid endoscope during laparoscopic procedures. The AutoLap System includes both software and hardware. Hardware components include a Processing Unit, which includes the system's electronics and software algorithms, a Base Unit and a Laparoscopic Unit that include motors and sensors. The Base Unit is mounted on the operating bed. An accessory cart that is used for placing the system's components during the procedure and their storage afterwards is also provided. To maneuver the laparoscope to the desired position, the surgeon presses a single button, referred to as Command Unit, which is affixed to his/her hand or to the surgical instrument and transmits RF signals to the system. Additionally, the system enables the surgeon to manually perform larger movements of the laparoscope using the Manual Activation Button affixed to the AutoLap system. Movements can be performed in the left/right, up/down and zoom in/ zoom out directions.

This submission (K131375) describes the MST AutoLap System, a device designed to hold and control the movement of a laparoscope or rigid endoscope during minimally invasive laparoscopic surgery. The performance evaluation for this device primarily focuses on demonstrating its safety and meeting functional specifications, rather than clinical efficacy studies often seen with diagnostic AI.

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

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Description: The primary "test set" that involves a direct assessment of device functionality in a surgical context is described as an "animal study."
• Sample Size: Not explicitly stated as a number of animals (e.g., "5 pigs"), but refers to "domestic pigs" for "laparoscopic cholecystectomy and Nissen fundoplication."
• Data Provenance: Not specified, but implied to be prospective data collected specifically for this study during the animal procedures. Country of origin not stated.

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

• Number of Experts: "Several surgeons" performed the procedures in the animal study.
• Qualifications of Experts: Not specified. It can be presumed they are experienced surgeons performing these types of laparoscopic procedures. However, no details on their specific qualifications (e.g., years of experience, board certification) are provided.

4. Adjudication Method for the Test Set

The concept of "adjudication" (e.g., 2+1, 3+1) is typically associated with studies where multiple readers or experts independently assess cases and their agreement or disagreement needs to be resolved to establish ground truth outcomes.

In this context, for the animal study:

• Adjudication Method: Not applicable/not described in the traditional sense. The "ground truth" for complications and performance appears to be established directly by the "several surgeons" performing the procedures, likely based on their real-time observation and expert judgment during the surgeries. There's no mention of independent review or formal adjudication of findings after the fact by a separate panel.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

• MRMC Study: No, an MRMC comparative effectiveness study was not explicitly described. The animal study focused on observing the device's performance and safety during procedures, not on comparing surgeon performance with and without the AI (or automated assistance in this case) on multiple cases with multiple readers. There's no mention of an effect size for human reader improvement with AI assistance. This device is not primarily an "AI diagnostic assistance" tool but rather a robotic/automated surgical assistant.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was Done

• Standalone Performance: Not explicitly described as a standalone study for the entire system's clinical performance. The "bench testing" represents a form of standalone testing for the device's functional parameters (movement velocities, range, control, image stability, safety features) without a human-in-the-loop during that specific test. However, this isn't assessing the device's diagnostic or surgical outcome capability as a standalone AI, but rather its electromechanical adherence to specifications. The system is designed to be a human-in-the-loop assistant, so a true "algorithm only" clinical performance assessment wouldn't be directly applicable to its intended use.

7. The Type of Ground Truth Used

• Bench Testing: The ground truth for bench testing (e.g., movement velocities, range, safety features) would be the device's engineering specifications and physical measurements obtained from sensors or external measurement tools.
• Animal Study: The ground truth for the animal study was based on direct observation and expert judgment by the surgeons performing the procedures regarding the device's performance (movement control) and the occurrence (or absence) of operative complications related to its use. This is a form of expert consensus/clinical observation.

8. The Sample Size for the Training Set

The provided text does not contain any information about a specific "training set" or "sample size for training." This device is more of a robotic system with embedded software algorithms than a machine learning or AI algorithm that requires a separate, large training dataset to learn patterns from data. Its software likely relies on predefined control algorithms rather than adaptive learning from a dataset.

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

Since no training set is described in the provided information, there is no information on how ground truth for a training set was established.

Ask a specific question about this device