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Aesthetic Body Contouring

The Vitruvian Liposaber™ consists of a control unit with an interconnecting cable to power and control a handpiece. The handpiece employs a motor intended to imitate the surgeons repetitive back and forth motions used during the liposuction procedure. The handpiece holds the cannula and the vacuum tubing. The handpiece is controlled by the user through the interaction with the control unit. The control unit controls the speed of the motor in the handpiece which provides the reciprocating motion of the cannula. The speed of the motor is variable between 3000 and 5000 RPM. The nominal speed is 4000 which can be adjusted using the controls on the handpiece between 3000 and 4000 RPM. Adjustments higher than 4000 must be performed through the user interface on the control unit.

The provided FDA 510(k) summary for the Vitruvian Liposaber pertains to a suction lipoplasty system, which is a physical device used for aesthetic body contouring. This document focuses on demonstrating substantial equivalence to a predicate device, primarily through non-clinical performance data and comparison of technical specifications, rather than clinical studies involving human patients or AI performance metrics.

Therefore, the requested information regarding AI acceptance criteria, specific study designs (MRMC, standalone), human reader performance improvement, multi-expert ground truth establishment, training and test set sample sizes for AI, and adjudication methods is not applicable to this device and its 510(k) submission.

The document explicitly states: "No clinical testing was required to support the medical device as the indications for use are equivalent to the predicate device. These types of devices, including the predicate devices, have been on the market for years. The non-clinical testing details in this submission support the substantial equivalence of the device." This confirms that the approval is based on equivalence to existing, commonly understood devices, not on novel AI performance.

Below is a summary of the acceptance criteria and device performance as demonstrated in this 510(k) in the context of the device's technical specifications and non-clinical testing.

Acceptance Criteria and Reported Device Performance (Non-Clinical) for Vitruvian Liposaber™

The Vitruvian Liposaber™ is a physical medical device (suction lipoplasty system). Its acceptance criteria and performance are demonstrated through comparison to a predicate device and non-clinical testing, as opposed to AI performance metrics.

1. Table of Acceptance Criteria (as implied by comparison to predicate and non-clinical testing) and the Reported Device Performance:

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

• Sample Size: Not applicable in the context of an AI test set. Performance was demonstrated through non-clinical engineering tests of the device prototypes, material testing, and comparative analysis to a predicate device.
• Data Provenance: The data arises from in-house engineering testing, bench testing, and laboratory studies conducted by the manufacturer, Black & Black Surgical, Inc. This is inherent to hardware device approval processes. No patient data (retrospective or prospective) was used for performance validation as no clinical study was conducted. The country of origin of the data is implicitly the USA, where the company is based and the testing would likely have occurred or been commissioned.

3. Number of Experts Used and Qualifications for Ground Truth and 4. Adjudication Method:

• Not applicable. This device is not an AI/software product requiring expert ground truth or adjudication for image interpretation or diagnosis. The "ground truth" for this device's performance is objective measurements from engineering tests (e.g., motor speed, temperature, dimensions, electrical safety compliance) and comparison to established predicate device specifications.

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

• Not applicable. MRMC studies are specific to evaluating AI in diagnostic imaging where human readers interpret cases with and without AI assistance. This device is a surgical instrument.

6. If a Standalone (algorithm only without human-in-the-loop performance) was done:

• Not applicable. This is not an AI algorithm. Its "performance" is its mechanical and electrical function.

7. The Type of Ground Truth Used:

• The "ground truth" for this device's approval is based on engineering specifications, compliance with recognized electrical and safety standards (e.g., IEC 60601-1, ISO 10993-1), and documented performance of a legally marketed predicate device. It relies on objective physical measurements and adherence to established regulatory and engineering principles.

8. The Sample Size for the Training Set:

• Not applicable. This is a hardware device; there is no "training set" in the context of machine learning. The design and manufacturing processes are iterative, informed by design inputs and testing, rather than a data-driven training paradigm.

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

• Not applicable. There is no training set or ground truth in the AI sense for this device. The design and validation are based on engineering principles, material science, and safety standards.

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The Pedicle Screw System is intended to provide immobilization of spinal segments in skeletally mature patients as an adjunct to fusion in the treatment of the following acute and chronic instabilities of thoracic, lumbar, and sacral spine: degenerative disc disease (DDD) (defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies); spondylolisthesis; trauma (i.e., fracture or dislocation); spinal stenosis; tumor; pseudoarthrosis; and failed previous fusion.

The Pedicle Screw System consists of longitudinal rods, polyaxial screws, and transverse connectors. It is manufactured from Ti-6Al-4V alloy conforming to ASTM F136.

This document describes the FDA 510(k) clearance for Black Box Medical LLC's Pedicle Screw System (K201751). It does not contain information about acceptance criteria or a study proving the device meets those criteria.

This submission is a declaration of substantial equivalence to a predicate device (Stowe Pedicle Screw System, K181554) and explicitly states: "Performance data is not included in this submission. The design changes being included for review are the same changes implemented and approved under K181554. No additional changes have been made for this submission that would require additional performance testing."

Therefore, I cannot provide the requested information regarding acceptance criteria and the study that proves the device meets them because this document indicates that such information was not part of this specific 510(k) submission (K201751). The clearance is based on substantial equivalence to a previously cleared device.

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The FUSE Pedicle Screw System is intended to provide immobilization of spinal segments in skeletally mature patients as an adjunct to fusion in the treatment of the following acute and chronic instabilities of thoracic, lumbar, and sacral spine: degenerative disc disease (DDD) (defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies); spondylolisthesis; trauma (i.e., fracture or dislocation); spinal stenosis; tumor; pseudoarthrosis; and failed previous fusion.

The FUSE Pedicle Screw System consists of longitudinal rods and polyaxial screws. It is manufactured from Ti-6Al-4V alloy conforming to ASTM F136.

This document is a 510(k) premarket notification for the FUSE Pedicle Screw System and does not contain information regarding acceptance criteria, device performance tables, sample sizes, expert qualifications, or ground truth establishment relevant to an AI/ML medical device.

The document states: "Performance data is not included in this submission. The design changes being included for review do not present a new worst-case configuration that would require additional performance testing." This indicates that the submission relies on the substantial equivalence to a predicate device (ACME Talon Pedicle Screw System, K071824) rather than new performance studies for the FUSE Pedicle Screw System itself.

Therefore, I cannot provide the requested information based on the provided text.

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The Stowe Pedicle Screw System is intended to provide immobilization of spinal segments in skeletally mature patients as an adjunct to fusion in the treatment of the following acute and chronic instabilities of thoracic, lumbar, and sacral spine: degenerative disc disease (DDD) (defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies); spondylolisthesis; trauma (i.e., fracture or dislocation); spinal stenosis; tumor; pseudoarthrosis; and failed previous fusion.

The STOWE Pedicle Screw System consists of longitudinal rods, polyaxial screws, and transverse connectors. It is manufactured from Ti-6Al-4V alloy conforming to ASTM F136.

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

Important Note: The provided document is an FDA 510(k) clearance letter for a medical device (Stowe Pedicle Screw System), not a study report for an AI/ML powered device. Therefore, much of the requested information regarding acceptance criteria, performance studies, expert involvement, and ground truth for an AI/ML device is not present in this document. The document describes a traditional medical device and its substantial equivalence to a predicate device.

Acceptance Criteria and Study Information (Based on the Provided Document)

As this document pertains to a traditional medical device (spinal pedicle screw system) and not an AI/ML-powered one, the concepts of "acceptance criteria" and "study" as they relate to AI/ML performance (e.g., sensitivity, specificity, F1-score) are not applicable.

Instead, for this type of medical device, "acceptance criteria" would refer to demonstrating substantial equivalence to a previously cleared predicate device, typically through comparisons of:

• Intended Use
• Indications for Use
• Technological Characteristics (design, materials, dimensions)
• Performance (mechanical, biocompatibility, sterilization, etc. if changes warrant new testing)

The "study" that proves the device meets these criteria is a comparative analysis against the predicate device, often supported by engineering analysis, materials testing, and sometimes bench testing or animal studies if there are significant design changes. However, the document explicitly states:

"Performance data is not included in this submission. The design changes being included for review do not present a new worst-case configuration that would require additional performance testing."

This indicates that the "study" for this particular 510(k) relied primarily on the technical characteristics and equivalence to the predicate device rather than new, extensive performance data.

Here's an attempt to populate the table and answer the questions based on the document's context:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not applicable. This is not a study involving patient data or an algorithm's test set. The "test set" here would refer to the device itself and its components, and the "testing" would be engineering analysis or bench testing. No specific sample sizes for such testing are detailed beyond the statement that no additional performance testing was required.
• Data Provenance: Not applicable.

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

• Number of Experts: Not applicable. For this type of device submission, "ground truth" as it relates to clinical outcomes or image interpretation (which would require experts) is not the primary focus of the 510(k) for substantial equivalence. The expertise involved would be in engineering, materials science, and regulatory affairs to assess the device's design and compare it to the predicate.
• Qualifications of Experts: Not specified in the document, but would typically involve engineers and regulatory professionals at the manufacturing company and the FDA.

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

• Adjudication Method: Not applicable. This term usually refers to resolving discrepancies in expert labeling or diagnoses for AI training/testing. For a traditional device, substantial equivalence is determined by regulatory review based on presented data and comparisons.

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

• MRMC Study: No. This type of study is relevant for AI-powered devices affecting human interpretation.

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

• Standalone Performance: No. This is not an AI algorithm.

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

• Type of Ground Truth: For this type of device, the "ground truth" for demonstrating safety and effectiveness is largely established by:
  • Regulatory standards: Adherence to material specifications (e.g., ASTM F136).
  • Predicate device's established safety and effectiveness: The historical performance and clearance of the ACME Talon Pedicle Screw System (K071824) serve as the benchmark.
  • Engineering principles and biomechanical testing (if performed): To ensure the device performs as intended under expected loads. However, the document explicitly states no additional performance testing was needed for this submission.

8. The sample size for the training set

• Sample Size: Not applicable. This is not an AI/ML device requiring a training set.

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

• Ground Truth Establishment: Not applicable. This is not an AI/ML device requiring a training set.

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Aesthetic body contouring. This Infiltration Pump is used to cause a flow of fluid from an IV bag into patient in a manner controlled manually by a health care professional.

The B89055 Vitruvian Infiltration Pump is intended to be used for aesthetic body contouring. Normal use includes general tumescent infiltration. It has a self-adjusting power converter which works with either 100V or 240V currents, and includes a pneumatic foot-switch and selfadjusting tubing guide to prevent tubing slippage during operation. It has a fully adjustable flow rate between 0 mL/min and 475 mL/min.

The Vitruvian Infiltration Pump is a pre-assembled device consisting of:

• Housing: ABS Plastic
• Power Operation: 100-240VAC, 50-60Hz, 1.8-1.0A
• Head: 3 roller peristaltic action
• Control: Knob used to set mL/min
• Display: LED
• Foot Switch: Air Powered ON/OFF
• Power Cord: Yes

The infiltration pump does not contact the patient.

This document is an FDA 510(k) premarket notification for the Vitruvian Infiltration Pump, which is a medical device. The purpose of a 510(k) is to demonstrate that the new device is "substantially equivalent" to a legally marketed predicate device. This process typically relies on comparing indications for use, technological characteristics, and performance data to the predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Vitruvian Infiltration Pump are primarily demonstrated through compliance with electrical safety standards and performance testing related to its function, particularly flow rate calibration. The document states compliance with general design and safety standards, and specifically mentions performance testing for flow rate.

It's important to note that for a 510(k), the "acceptance criteria" are typically demonstrating substantial equivalence to a predicate, which often involves showing the new device performs as intended and meets relevant safety standards, rather than defining specific statistical endpoints for a comparative clinical study against a disease state.

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

The document does not specify a separate "test set" in the context of clinical data for algorithmic performance or diagnostic accuracy. The performance testing mentioned is for the device's functional characteristics (e.g., electrical safety, flow rate calibration). The data provenance for this functional testing is stated as "Performed at Manufacturing Facility." No patient-specific data or country of origin for such data is provided, as it is not a study involving patient outcomes or diagnostic outputs.

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

Not applicable. This is not a study assessing diagnostic accuracy or requiring expert-derived ground truth. The "ground truth" for the performance testing cited (electrical safety, flow rate calibration) would be the established engineering standards and specifications.

4. Adjudication Method for the Test Set

Not applicable. As noted above, this is not a study involving diagnostic accuracy and expert adjudication. The "adjudication" for the performance tests would be whether the device passed or failed the specified standard's requirements.

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 Vitruvian Infiltration Pump is an infiltration pump for aesthetic body contouring; it is not an AI-powered diagnostic or interpretive device that would involve human readers.

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

Not applicable. The Vitruvian Infiltration Pump is a medical device, not a standalone algorithm.

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

For the performance testing mentioned (IEC standards, flow rate calibration), the ground truth is established by the technical specifications and requirements of those international and national standards. For example, a flow rate calibration would involve comparing the pump's output to accurately measured fluid volumes over time, with the "ground truth" being the true measured flow rate.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires a training set of data.

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

Not applicable. This is not an AI/ML device that requires a training set of data.

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The IDSS-ForceTriad Control Module is designed for integration in the IDSS(Integrated Digital Surgical Suite) and enables the Covidien ForceTriad Energy Platform to be controlled remotely.

The IDSS ForceTriad Control Module is an additional function to the IDSS, which is an integrated operating room system controlling video displays, observation cameras, audio video equipment, teleconferencing and the routing of video and images from multiple sources to multiple destinations via a touch screen interface. With the IDSS ForceTriad Control Module, operation room staff is able to control the ForceTriad setup from the touch panel location rather than using the ForceTriad unit itself.

The provided FDA 510(k) summary (K153205) describes the IDSS-ForceTriad Control Module. This device is an accessory that enables remote control of the Covidien ForceTriad Energy Platform within an Integrated Digital Surgical Suite (IDSS). The primary purpose of this submission is to demonstrate substantial equivalence to a predicate device, not to showcase the performance of an AI/ML algorithm that interprets medical images or data.

Therefore, many of the requested criteria regarding AI/ML performance studies, such as effect size of human readers with AI assistance, standalone algorithm performance, number of experts for ground truth, and training set information, are not applicable to this document.

Here's an analysis of the provided information, focusing on the context of a medical device accessory and its validation:

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

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

Not applicable. This device is a control module, not a diagnostic or AI image analysis system that would typically use a "test set" of medical images or patient data. The testing is focused on software functionality and risk management.

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. As noted above, this is a control module, not an AI/ML system requiring expert-adjudicated ground truth for medical data.

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

Not applicable. This is not an AI/ML system requiring adjudication of medical cases.

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 filing concerns a control module for an energy platform, not an AI-assisted diagnostic or imaging device. There is no mention of human readers or AI assistance in the context of clinical interpretation.

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

Not applicable. This is not an AI algorithm. Its function is to remotely control an existing medical device.

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

For the software validation and risk analysis, the "ground truth" would be related to the correct and expected functionality of the software according to its design specifications, user requirements, and safety standards (e.g., ISO 14971:2007). This is established through internal design documents, functional specifications, and compliance with regulatory standards, rather than clinical outcomes or expert consensus on medical findings.

8. The sample size for the training set

Not applicable. This is not an AI device that involves a "training set" in the machine learning sense.

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

Not applicable. This is not an AI device.

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Aesthetic body contouring.

The Vitruvian Aspirator is a pre-assembled device consisting of:

• . Housing: Powder Coated Steel
• . Vacuum Pump: Dual ½ hp dual voltage rocking piston motor with Maximum Suction flow rate of 125PSI, 6.4 cfm, 27.5in HG(711 mm of Hg)
• . Vacuum Meter Display: The device displays the vacuum levels digitally. The software does not control the unit in any way and does not accept any input.
• . Power Operations: 120/230 volt 50/60hz
• . Control: Pressure control valve located on the face of the device with rotary control knob with both LED numerical and Color Graphic LED display
• . Footswitch: Dual activated air powered
• IV Pole .
• . Filter: Bacterial filter having a pore size of 0.3 microns or less.

The provided document describes a 510(k) submission for the "Vitruvian Ultimate Aspirator." This product is a suction lipoplasty system, which is a medical device and not an AI/ML software device. Therefore, the questions related to AI/ML specific aspects like training data, inference, expert adjudication, etc., are not applicable.

Here's the information derived from the document regarding the acceptance criteria and performance testing for this physical medical device:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are based on compliance with recognized medical device standards and functional performance testing. The reported performance indicates compliance with these criteria.

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

The document does not specify a "test set" in the context of data for AI/ML. Instead, performance testing was conducted on the physical device itself. The provenance of this testing is indicated as the "Manufacturing Facility."

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

Not applicable. This is not an AI/ML device requiring expert ground truth for image interpretation or diagnosis.

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

Not applicable. This is not an AI/ML device requiring adjudication.

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 a physical medical device, not an AI assistance tool for human readers.

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

Not applicable. This is a physical medical device, not an algorithm.

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

For the functional aspects, the "ground truth" or reference for performance is established by the specified industry standards (e.g., IEC 60601 series) and the functional specifications of the predicate device. For the vacuum display calibration, the "acceptable criteria range" serves as the ground truth.

8. The sample size for the training set

Not applicable. This is a physical medical device, not an AI/ML system requiring a training set.

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

Not applicable.

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IDSS SLC allows the control of surgical lighting during a surgical procedure from the IDSS, which is a centralized interface for voice, video, audio, observational camera and teleconferencing for the operating room.

IDSS SLC is an additional function of the IDSS, which is an integrated operating room system that controls video displays, observation cameras, audio video equipment, teleconferencing and the routing of videos and images from multiple sources to multiple destination via touch screen interface.

This document, a 510(k) Summary for the IDSS SLC device, does not contain a study proving the device meets acceptance criteria as typically understood for AI/ML medical devices.

The IDSS SLC is described as an additional function of an integrated operating room system that controls surgical lighting, video displays, observation cameras, audio/video equipment, and teleconferencing. Its primary function is to allow control of surgical lighting during a procedure from a centralized interface.

Instead of a typical performance study with acceptance criteria for an AI/ML diagnostic or prognostic device, this document describes a summary of nonclinical testing focused on the device's functional integrity, safety, and equivalence to predicate devices.

Here's a breakdown of the information that can be extracted, and what is not applicable (N/A) given the nature of this device and document:

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

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

• N/A. This document describes nonclinical engineering and safety testing of a control system, not a study involving patient data or a test set in the traditional sense for AI/ML performance evaluation. The "test set" would be the device itself and its components undergoing various engineering validation tests.

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)

• N/A. Ground truth for clinical interpretation or diagnostic accuracy is not relevant here. The "ground truth" for this device's performance would be whether it accurately implements its programmed controls, adheres to safety standards, and functions reliably. This would be established by qualified engineers and testers.

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

• N/A. Adjudication methods like 2+1 or 3+1 are used for resolving disagreements among human readers in interpreting clinical data. This is not relevant for the type of testing described.

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

• N/A. The IDSS SLC is a control system for surgical lights and other OR equipment. It is not an AI-assisted diagnostic or interpretative device that would be subject to an MRMC study.

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

• N/A. Not an AI algorithm in the context of clinical interpretation. The "standalone" performance would refer to the IDSS SLC system's ability to operate its functions as designed, which is covered by the validation testing.

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

• The "ground truth" for this device's performance is its conformance to engineering specifications, functional requirements, and established safety standards (e.g., EN 60601-1). This ground truth is based on predefined technical standards and the expected behavior of a physical control system.

8. The sample size for the training set

• N/A. This device does not use a training set for machine learning. The "training" for such a system would involve software development, component testing, and integration testing, which are distinct from AI model training.

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

• N/A. As there is no training set mentioned in the context of AI/ML, this question is not applicable.

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The Black and Black Surgical monopolar electrodes and forceps are reusable devices intended for use in surgical procedures (such as general, urologic, thoracic, plastic and reconstructive, gynecologic, arthroscopic) where the surgeon desires monopolar radio-frequency electrosurgical energy to cut and/or coagulate soft tissue.

The Black and Black monopolar forceps and electrodes are handheld, reusable devices designed to deliver monopolar RF electrosurgical energy to a surgical site to cut and/or coagulate tissue. The surgeon activates the desired electrosurgical mode from the sterile field by using buttons on the device. Three electrosurgical modes are available: Cut: cuts tissue with minimal coagulation / hemostasis; Hemostasis with Division: divides tissue with controlled hemostasis; Coag: coagulates bleeding vessels to promote hemostasis.

Based on the provided text, a detailed description of acceptance criteria, device performance, and the study methodologies is not available. The document is a 510(k) summary for a medical device (electrosurgical cutting and coagulation forceps and electrodes), which primarily focuses on establishing "substantial equivalence" to predicate devices rather than proving performance against specific acceptance criteria through a dedicated study.

Here's a breakdown of what can and cannot be extracted from the given text:

Acceptance Criteria and Device Performance:

The document does not explicitly state quantitative acceptance criteria or a specific study designed to "prove" the device meets these criteria in the typical sense of a clinical trial or performance study with defined metrics.

Instead, the core of this 510(k) submission is to demonstrate substantial equivalence to already marketed predicate devices. This means the new device is deemed safe and effective because it shares similar technological characteristics, intended use, and operational principles with existing devices that have already undergone FDA clearance.

Therefore, a table of acceptance criteria and reported device performance cannot be generated from this document because it does not exist in this context. The "performance" being demonstrated is conceptual substantial equivalence to predicate devices, not measured against specific numerical targets.

Study Information (Based on what's typically implicit in a 510(k) for substantial equivalence):

Given the nature of a 510(k) for "substantially equivalent" devices, a traditional standalone performance study with a test set, ground truth experts, and statistical analysis as described in the prompt is generally not required or presented in detail in the summary.

Here's what can be inferred or stated based on the provided text, with acknowledgments for what is missing:

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

   • Cannot be generated from the provided text. The document focuses on "substantial equivalence" to predicate devices rather than specific measurable performance against defined acceptance criteria.

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

   • Not applicable/Not provided. This type of information would be relevant for a clinical or performance study. In a 510(k) focused on substantial equivalence for a device like electrosurgical instruments, the "test set" is often conceptual, referring to the engineering design, materials, and functional tests demonstrating that the device operates as intended and similarly to predicates. There's no mention of human subject data or data provenance.

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 provided. Ground truth (e.g., expert consensus, pathology) is typically established in studies evaluating diagnostic or predictive devices. For an electrosurgical device, safety and functional performance tests are more relevant, often conducted by engineers and technicians, not clinical experts establishing "ground truth" on patient data.

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

   • Not applicable/Not provided. This is relevant for studies involving human interpretation or data adjudication, which is not the primary focus of this 510(k) submission.

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 device is an electrosurgical tool, not an AI-assisted diagnostic or interpretive system. Therefore, an MRMC comparative effectiveness study involving human "readers" and AI assistance is not relevant and was not conducted.

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

   • No. This device is a physical electrosurgical tool, not an algorithm or software. It requires a human surgeon for its operation ("human-in-the-loop" is inherent to its intended use). Therefore, a standalone algorithm-only performance assessment is not applicable.

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

   • Not applicable in the typical sense. For this type of device, "ground truth" often refers to engineering specifications, material properties, electrical output measurements, and functional testing results (e.g., ability to cut and coagulate tissue in a controlled laboratory setting). The document does not detail specific "ground truth" methodologies.

8. The sample size for the training set:

   • Not applicable/Not provided. This device is not an AI/ML model that requires a "training set."

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

   • Not applicable/Not provided. As above, no training set for an AI/ML model is involved.

Summary of Device and its "Proof" of Meeting Criteria:

The Black and Black Electrosurgical Cutting and Coagulation Forceps and Electrodes are characterized as handheld, reusable devices designed to deliver monopolar RF electrosurgical energy for cutting and/or coagulating tissue. They offer three modes: Cut, Hemostasis with Division, and Coag.

The study that "proves" the device meets criteria (implicit in a 510(k) context) is the comparison to legally marketed predicate devices. The document states:

• "The Black and Black Surgical monopolar forceps and electrodes are substantially in function and intended use to the Colorado MicroDissection Needle (K033232), Valleylab Bayonet Forceps, Conmed Bayonet Straight Tip with Cable, Snowden-Pencer Endo-Plastic Electrosurgical Dissector. Snowden-Pencer Monopolar Insulated Forceps, and Olsen Hand Activated Monopolar Forceps."
• "The Black and Black monopolar forceps and electrodes have the same basic technological characteristics as the predicate devices noted above."

The FDA's letter further confirms this method: "We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices..."

Therefore, the "proof" is the demonstration of substantial equivalence to existing, cleared devices, implying that if the predicates are safe and effective, then a substantially equivalent device with the same technological characteristics and intended use will also be safe and effective. The specifics of how this equivalence was demonstrated (e.g., through bench testing, material comparisons, electrical safety tests) are not detailed in this summary but would have been part of the full 510(k) submission.

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