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The PrecisePath Radiofrequency Puncture Generator and Footswitch (optional accessory) is indicated for use in general surgical procedures to cut and coagulate soft tissues.

The PrecisePath Radiofrequency (RF) Puncture Generator is indicated for use in general surgical procedures to cut and coagulate soft tissue. It is designed to be used by trained medical professionals, to provide radiofrequency (RF) current for creating controlled punctures with compatible Baylis RF devices in a monopolar mode. This continuous or pulsed RF current is output at a fixed frequency within 450 kHz to 480 kHz.

The PrecisePath Footswitch (optional accessory) may be used as an alternative to the on/off button on the generator user interface by which the user can start and stop the delivery of RF energy.

The PrecisePath RF Puncture Generator is compatible with separately cleared Baylis radiofrequency devices and connector cables, off-the-shelf return (dispersive) electrodes that meet or exceed IEC 60601-2-2:2017, and the PrecisePath footswitch (optional accessory).

N/A

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The Precise Image is a reconstruction software application for a Computed Tomography X-Ray System intended to produce images of the head and body by computer reconstruction of x-ray transmission data taken at different angles and planes. These devices may include signal analysis and display equipment supports, components and accessories. Precise Image has been evaluated and available on preselected reference protocols for adult subjects. Precise Image is not indicated for use in pediatric subjects.

The CT system with Precise Image is indicated for head, whole body and vascular X-ray Computed Tomography applications. These scanners are intended to be used for diagnostic imaging.

Precise Image uses an Artificial Intelligence powered reconstruction that is designed for low radiation dose, provides lower noise, and improves low contrast detectability.

The proposed Precise Image is a reconstruction software application that may be used on a Philips whole-body computed tomography (CT) X-Ray System. Precise Image is a robust reconstruction software application, utilizing technological advancements in Artificial Intelligence and a Convolutional Neural Networks (CNN), When used, Precise Image generates CT images that provides an image appearance similar to traditional FBP images while reducing dose and improving image quality.

The implemented algorithm includes 5 user-adjustable settings to match the Radiologist's preference for dose reduction and image quality.

The proposed Precise Image reconstruction has been trained on and may be used on the currently marketed predicate device Philips Incisive CT System (K180015).

Here's a breakdown of the acceptance criteria and the study details for the Philips Precise Image device, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

1. Sample Size for Test Set and Data Provenance:

   • Sample Size: 55 image set pairs.
   • Data Provenance: The document states "Sample clinical images are provided with this submission," implying these are real clinical images. No specific country of origin is mentioned, nor is it explicitly stated if the data is retrospective or prospective. However, given they are "clinical images" and used for evaluation, it's highly likely they are retrospective images from existing clinical practice.

2. Number of Experts and Qualifications:

   • Number of Experts: 6 board-certified radiologists.
   • Qualifications: "board certified radiologists." No specific years of experience or subspecialty are provided.

3. Adjudication Method for the Test Set:

   • The document implies individual evaluations by each of the 6 radiologists on a Likert scale for various image attributes. It does not mention any explicit adjudication method (like 2+1 or 3+1 consensus) for the ground truth of the test set itself. The radiologists assessed "Diagnostic Confidence. Sharpness, Noise level. Image texture and Artifacts." The study compares the proposed device images against predicate device images, with the radiologists providing their individual assessment on these attributes.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • Was it done? Yes, a comparative image evaluation study was performed by 6 board-certified radiologists on 55 image set pairs.
   • Effect size of human readers improvement with AI vs without AI assistance: The document states that the evaluation was to "evaluate Diagnostic Confidence. Sharpness, Noise level. Image texture and Artifacts on a five point Likert scale" demonstrating "substantial equivalence to the currently marketed predicate device Philips Incisive CT (K180015)." It highlights improvements in low contrast resolution, noise reduction, and low contrast detectability of the device itself compared to the predicate/standard mode, but does not quantify human reader improvement (e.g., AUC, sensitivity, specificity) with AI assistance versus without it. The evaluation focused on image quality and characteristics, not diagnostic accuracy changes for the human reader.

5. Standalone (Algorithm Only) Performance Study:

   • Yes, the performance characteristics like "Low Contrast Resolution," "Noise Reduction and Low Contrast Detectability," and "Noise Power Spectrum" are measurements of the algorithm's output (the reconstructed image) and are done in a standalone manner without human intervention influencing these specific metrics. The clinical image evaluation by radiologists also assesses the output of the algorithm relative to the predicate.

6. Type of Ground Truth Used:

   • For the quantitative technical specifications (e.g., low contrast resolution, noise, NPS), the ground truth is based on phantom measurements (e.g., "20 cm Catphan phantom," "21.6 cm water-equivalent").
   • For the clinical image evaluation, the "ground truth" for comparison is the predicate device's images (Incisive CT and Brilliance iCT), with radiologists evaluating the attributes of the Precise Image compared to these established images. There is no mention of a separate, definitive, clinical ground truth (e.g., pathology, clinical outcomes) for the diagnosis from these images. The radiologists are evaluating image quality characteristics and comparing them.

7. Sample Size for the Training Set:

   • Not explicitly stated in the provided text. The document mentions, "The proposed Precise Image reconstruction has been trained on and may be used on the currently marketed predicate device Philips Incisive CT System (K180015)." However, it doesn't give a specific number of images or cases used for training.

8. How the Ground Truth for the Training Set was Established:

   • Not explicitly stated in the provided text. It mentions the device "has been trained on" the predicate device's data, implying that the established high-quality images from the predicate device likely served as a reference or ground truth for the AI training process to guide the AI in producing similar or improved image characteristics. However, the specific method of ground truth establishment for training data is not detailed.

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The Precise Position is intended for use with Philips Incisive CT systems. The following guided workflow.

• Patient orientation identification
• Surview range recommendation
• Automatic centering the patient anatomy
• Provide visual images of patient on the table

Precise position is indicated for use for CT imaging of the head, chest, abdomen, pelvis, and combination of those anatomies.

Patient population limitation: Patient younger than 16 years are not supported.

Precise Position is an optional feature to assist user for position the patient before the body examination such as CT scan. The purpose of this feature is to reduce the patient position time via the camera detection and calculation result. It includes automatic detect patient orientation, patient anatomy scan range and center of patient anatomy.

Precise Position including a camera with both color and depth function is installed in the ceiling of the scan room, in such a way to cover the entire patient on the patient table. The camera control and image data transmit via the high speed fiber and copper hybrid USB cable. The power supply of the camera is from the gantry. Precise position adopts the AI algorithm (Convolution Neural Network) to detect the joints of the patient body, and then identify surview start/end position and patient orientation. The algorithm can also support detect center of patient anatomy.

Limitation for Precise Position
There is no limitation for Precise Position except below items:
• Patients below the age of 16 are not supported.
• Decubitus orientations are not supported.

The Precise Position display results may get affected by the following conditions:
• When the patient is covered by sheet, blanket etc.,
• When the patient is not completely covered by the ceiling camera view, e.g. blocked by the gantry or out of camera's FOV etc.
• When the patient is wearing clothes that reflects light, e.g. plastic-like clothes.
• When the patient is wearing black clothes.
• When the patient is wearing thick clothes.
• When there are other people around the patient.

Here's an analysis of the acceptance criteria and the study proving the device meets them, 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

• Sample Size: Total 80 clinical scan positions.
  • 40 cases used with Precise Position.
  • 40 cases without the usage of Precise Position.
• Data Provenance: The document does not explicitly state the country of origin for the data or if it was retrospective or prospective. However, it mentions a "clinical evaluation… done by 5 Clinical experts" and volunteers not receiving radiation, implying a prospective study conducted for the purpose of this evaluation. The manufacturer is Philips Healthcare (Suzhou) Co., Ltd., which suggests the study likely occurred in China.

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

• Number of Experts: 5 Clinical experts.
• Qualifications: The specific qualifications (e.g., number of years of experience, specific specialty like "radiologist") are not explicitly stated beyond "Clinical experts."

4. Adjudication Method for the Test Set

• The document does not specify an explicit adjudication method (e.g., 2+1, 3+1). It states that the "thorough clinical evaluation of this feature is done by 5 Clinical experts," implying they collectively contributed to the evaluation, but the exact consensus or adjudication process is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• This was not a typical MRMC comparative effectiveness study in the sense of multiple readers interpreting cases with and without AI.
• Instead, it was a comparative study on operational efficiency and positioning accuracy. It compared:
  • Time taken for patient positioning by users with and without the Precise Position feature.
  • Accuracy of vertical (iso)center positioning by users with and without the Precise Position feature.
  • Consistency (standard deviation) of positioning by users with and without the Precise Position feature.
• Effect Size (Human Improvement with AI):
  • Time Reduction: Users achieved up to 23% time reduction in patient positioning with the "Precise Positioning workflow" (which includes the AI).
  • Vertical Position Accuracy: Users achieved an increase of up to 50% in vertical position accuracy with "Precise Position."
  • Positioning Consistency (Vertical and Horizontal): Users achieved an increase of up to 70% in consistency with "Precise Position."
    These metrics indicate the improvement in human operators' performance when assisted by the AI-powered Precise Position device.

6. If a Standalone (Algorithm Only) Performance Study Was Done

• The document does not explicitly state whether a standalone (algorithm only, without human-in-the-loop) performance study was conducted for the AI component of the Precise Position device. The clinical evaluation focuses on the human-with-AI system performance. The AI algorithm (Convolution Neural Network) is described as being used to detect joints and then determine positioning parameters, suggesting its performance is evaluated as part of the overall integrated system.

7. The Type of Ground Truth Used

• For the clinical evaluation, the ground truth for measuring time, accuracy, and consistency appears to be based on:
  • Direct measurements of time taken for positioning.
  • Measurements of offset in mm for vertical (iso) center position.
  • Standard deviation calculations for vertical (iso)center positioning and surview (horizontal) start position.
  • These measurements were likely compared against an ideal or intended positioning, which would be implicitly defined by the CT system's requirements and presumably verified by the clinical experts. It's not "pathology," "outcomes data," or a direct "expert consensus" on disease presence/absence, but rather a consensus on the correctness and optimal nature of the patient positioning parameters established by the device.

8. The Sample Size for the Training Set

• The document does not provide the sample size for the training set used for the AI algorithm (Convolution Neural Network). It only discusses the test set used for validating the combined human-AI system.

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

• The document does not describe how the ground truth for the training set of the AI algorithm was established. It only mentions that the AI algorithm (Convolution Neural Network) is used to detect "joints of the patient body" to then identify surview start/end position and patient orientation, and support detection of the center of patient anatomy. This implies annotation of patient body parts and anatomical landmarks in imaging data for training purposes, but specific details on its establishment are not provided.

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The Precise SHP Diode Laser is indicated for dentistry and oral soft tissue procedures of:

1. The removal of lesions, excision, incision, vaporization, hemostasis, and photocoagulation on soft tissue including abscess treatment, contouring, curettage, sulcular debridement, pulpotomy, frenectomy, troughing, and removal of inflamed edematous tissue.

2. Temporary relief of minor muscle and joint pain, stiffness, minor arthritis pain, temporary increase in local blood circulation, and temporary relaxation of muscles by means of topical elevated tissue temperature from infrared spectral emissions;

3. Light activation of bleaching materials for teeth whitening and laser-assisted whitening/bleaching of teeth.

The Precise SHP Diode Laser is a device for delivering laser energy to human soft tissue for a variety of surgical procedures and treatments. This energy is generated by solid-state diodes, which provide a consistent and reliable generation of laser energy at 810 ± 20nm for a maximum of 3 watts of energy output. The laser energy is delivered to surgical site by means of an optical fiber system, which allows for the safe transmission of laser energy to the site without creating undue risk to the patient or operatory staff by errant or collateral laser emissions. The device features some user definable settings, including a switchable 630nm aiming beam, adjustable power output for both the working beam and aiming beam, and continuous delivery or pulse delivery options.

The working end of the delivery fiber is contained within a metal handpiece with a disposable single-use tip. This handpiece system is incorporated into the device. The activation of the working beam diodes is completed by use of a foot-actuated switch.

This submission (K181601) is for a labeling change to the previously cleared Precise SHP Diode Laser (K123443). The key change is that the disposable applicator tips will now be delivered non-sterile, and the end-user will be responsible for sterilization. Therefore, the "device performance" in terms of clinical efficacy of the laser itself is not re-evaluated or specifically measured against acceptance criteria in this document. The focus of the "study" described here is to demonstrate that the new labeling and the end-user sterilization process do not negatively impact safety or effectiveness.

Here's an analysis based on the provided text, addressing your points:

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

Since this is a labeling change and not a re-evaluation of the laser's primary performance, the "acceptance criteria" are related to the safety and efficacy of the sterilization process for the tips and the laser's continued compliance with safety standards.

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: The document does not specify the exact sample size for the "verification and validation testing" performed on the tips for sterilization efficacy or performance. It simply states "Verification and validation testing indicate..."
• Data Provenance: Not specified. The document outlines a submission to the US FDA by a US-based company (CAO Group, Inc., West Jordan, Utah, U.S.A.). However, the location/country where the testing was performed is not mentioned, nor is it explicitly stated if the data is retrospective or prospective, though it would logically be prospective testing conducted specifically for this submission.

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

Not applicable. This submission concerns a labeling change and the effect of end-user sterilization on a device component. It does not involve establishing ground truth based on expert clinical or radiological reviews. The "ground truth" here is objective laboratory testing against established sterilization standards.

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

Not applicable. This type of adjudication method is used in studies involving human interpretation or decision-making (e.g., image reading). The testing performed here (sterilization efficacy) is objective laboratory 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

Not applicable. This document does not describe an MRMC study or any AI component.

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

Not applicable. This document does not describe any algorithm or AI performance.

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

The "ground truth" for the sterilization efficacy testing would be objective laboratory standards for sterilization, specifically achieving a Log 6 reduction in bioburden, as measured by standard microbiological assays. For tip performance, it would be instrumental measurements against predefined performance specifications.

8. The sample size for the training set

Not applicable. There is no "training set" as this is not an AI/machine learning study.

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

Not applicable. There is no "training set."

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The Elekta Medical Linear Accelerator is indicated to assist a licensed practitioner in the delivery of radiation to defined target volumes (e.g. lesions, arterio-venous malformations, malignant and benign tumors), whilst sparing surrounding normal tissue and critical organs from excess radiation for treatment that includes but is not limited to, malignant and benign brain tumors, brain metastases, spine lesions treated using SRS, squamous cell carcinoma of the head and neck, lung, breast, pancreatic, hepatic malignancies treated using SBRT, prostate, and bone metastases.

The Elekta Medical Linear Accelerator system is an image guided Radiation Therapy device to assist a licensed practitioner in the delivery of ionizing radiation to a defined target volume. The system consists of components of the accelerator, such as, beam shaping, with imaging and accessories for patient positioning and set-up to deliver therapeutic treatments. The Elekta Medical Linear Accelerator System is currently available in the following model variants – Precise Treatment System, Elekta Synergy Platform, Elekta Infinity and Versa HD.

This document (K182138) is a 510(k) Premarket Notification for a Medical Linear Accelerator, which is a radiation therapy device. The document primarily focuses on demonstrating substantial equivalence to a predicate device, rather than detailed performance study results against specific acceptance criteria for a new AI/software component.

Therefore, many of the requested details, such as specific acceptance criteria for AI performance, sample sizes for test/training sets, expert qualifications, ground truth establishment methods for a new AI, or MRMC studies, are not applicable or not provided in this document. This submission appears to be about software updates (control software, Integrity™) to an existing device, which mostly focuses on safety and existing functionality, rather than introducing a new AI-powered diagnostic or therapeutic capability that would require such extensive AI performance validation.

However, I can extract the information that is present and explain why other information is missing.

Acceptance Criteria and Device Performance (Based on the provided document)

Since this submission is for software changes to an existing medical linear accelerator and focuses on substantial equivalence, the "acceptance criteria" discussed are primarily related to general device safety, performance, and adherence to regulatory standards, rather than specific diagnostic or therapeutic efficacy metrics that would be evaluated for a novel AI algorithm.

Detailed Study Information (Based on the Provided Document):

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

   • As detailed in the table above, the acceptance criteria are generally qualitative and refer to adherence to regulatory standards (e.g., QSR, ISO standards, IEC standards) and the successful completion of verification and validation testing. The "reported device performance" is a confirmation that these standards were met and testing was successfully completed, leading to a determination of substantial equivalence.
   • Specific quantitative metrics for an AI algorithm's performance (e.g., AUC, sensitivity, specificity for a diagnostic task) are not present because the submission is for software updates to a linear accelerator, not a new AI diagnostic/therapeutic algorithm.

2. Sample sizes used for the test set and the data provenance:

   • N/A. This document describes "module, integration and system level verification" and "validation of the integrated system under clinically representative conditions." This is typical for a software update to an existing medical device. It does not mention a "test set" in the context of an AI algorithm's performance evaluation on a separate dataset of patient cases.
   • Data Provenance: Not specified as it's not a dataset-driven AI validation. The testing is described generally as "clinically representative conditions."

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

   • N/A. Ground truth establishment for an AI test set is not applicable here as the submission is for control software updates, not AI performance validation.
   • Validation was performed by "competent and professionally qualified personnel," but their specific number or qualifications are not provided for the purpose of "ground truth" establishment in an AI context.

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

   • N/A. Not applicable for this type of 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 type of study is not mentioned as it's not relevant to the nature of this submission (software update for a linear accelerator control system, not an AI assisting human interpretation).

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

   • N/A. No specific AI algorithm performance in a standalone capacity is evaluated or described. The software changes are to the control system of a device that assists a licensed practitioner.

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

   • N/A. The concept of "ground truth" in the context of an AI algorithm's performance is not discussed here. The validation described is against technical design specifications and safety/performance standards for a medical device's control system.

8. The sample size for the training set:

   • N/A. No AI training set is mentioned or implied for this submission. The software changes are described as "tighter error detection" and codebase merging, which likely comes from software development and testing cycles rather than machine learning training.

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

   • N/A. Not applicable as there is no mention of an AI training set. Grounds for validation are based on established engineering principles, regulatory standards, and clinical representativeness as opposed to a data-driven AI training methodology.

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The Ellipse PRECICE Trauma Nail System is indicated for open and closed fracture fixation, pseudoarthrosis, or mal-unions and non-unions of long bones.

The Ellipse PRECICE Trauma Nail System is composed of the PRECICE Trauma Nail (supplied sterile), locking screws, surgical instruments and an external remote controller (ERC). The Nail is available in various diameters, lengths and screwhole configurations to accommodate a variety of patient anatomies. The locking screws are also available in a variety of diameters and lengths. The PRECICE Trauma Nail is supplied sterile by gamma radiation while the locking screws and accessories are supplied non-sterile and must be sterilized prior to use. The Nail contains an enclosed rare earth magnet, telescoping lead screw/nut assembly, and planetary gearing. The Nail is supplied pre-distracted by 10 mm to allow for compression fracture reduction techniques.

The provided document is a 510(k) premarket notification for the "PRECICE Trauma Nail System". The document states that the device is substantially equivalent to a previously cleared device (K141447) and relies on the testing and data from that predicate device. Therefore, a direct study proving the device meets new acceptance criteria is not presented, as the submission is based on demonstrating equivalence.

Here's an analysis of the provided text, addressing your questions where information is available:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance results for this specific 510(k) submission (K142599). Instead, it states that "all testing that was performed on the predicate PRECICE Trauma Nail System is applicable" because there are "no changes to the design of the PRECICE Trauma Nail being made as a result of this submission."

However, the document lists the types of tests performed on the predicate device, which can be inferred as satisfying the acceptance criteria for those tests:

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

The document does not specify sample sizes for individual tests. It refers to "mechanical testing according to the methods outlined in the standard ASTM F1264-03." These standards typically define sample size requirements.

• Data Provenance: The data comes from tests performed on the predicate device (K141447). The document doesn't specify the country of origin of this data or if it was retrospective or prospective, but it's implied to be data collected during the development and clearance process of the predicate device.

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

This type of information (experts for ground truth) is typically relevant for studies involving subjective human assessment (e.g., image interpretation for AI algorithms). For physical medical devices undergoing mechanical, sterilization, or biocompatibility testing, ground truth is established by the specified test standards and methodologies themselves (e.g., ASTM, ISO standards), not by human expert consensus in the same way. Therefore, this information is not applicable in this context and is not provided in the document.

4. Adjudication Method for the Test Set

As explained above, for physical device testing against established standards, adjudication methods like 2+1 or 3+1 (common in clinical trials or diagnostic studies) are not applicable. The "ground truth" and "adjudication" are inherent in the test method's specifications and the objective measurements obtained.

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

No, an MRMC study was not done. This type of study is specifically for evaluating the effectiveness of a diagnostic tool, often an AI algorithm, by comparing human reader performance with and without its assistance. The PRECICE Trauma Nail System is an intramedullary fixation rod, a physical implant, and therefore, an MRMC study is not applicable to its evaluation.

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

No, a standalone algorithm performance study was not done. This is also specific to AI/software as a medical device. The PRECICE Trauma Nail System is a physical device, and therefore this type of study is not applicable.

7. The Type of Ground Truth Used

The "ground truth" for the predicate device's evaluation (and by extension, for this submission's substantial equivalence claim) is based on:

• Compliance with recognized industry standards: ASTM F1264-03 for mechanical testing, ANSI/AAMI/ISO 11137-2 for sterilization, and ISO 10993-1 for biocompatibility.
• Objective measurements and criteria: As defined within these standards for various performance parameters (e.g., strength, sterility assurance level, material compatibility).

8. The Sample Size for the Training Set

Not Applicable. The device is a physical medical implant, not an AI algorithm. Therefore, there is no "training set" in the context of machine learning.

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

Not Applicable. As there is no training set for an AI algorithm, there is no ground truth to establish for it.

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The Precise SHP Diode Laser is indicated for dentistry and oral soft tissue procedures of:

• 1. The removal of lesions, excision, incision, vaporization, ablation, hemostasis, and photocoagulation on soft tissue including abscess treatment, contouring, curettage, sulcular debridement, pulpotomy, frenectomy, gingivectomy, troughing, and removal of inflamed edematous tissue.
• 2. Temporary relief of minor muscle and joint pain, stiffness, minor arthritis pain, muscle spasm, temporary increase in local blood circulation, and temporary relaxation of muscles by means of topical elevated tissue temperature from infrared spectral emissions;
• 3. Light activation of bleaching materials for teeth whitening and laser-assisted whitening/bleaching of teeth.

The Precise SHP Diode Laser is a device for delivering laser energy to human soft tissue for a variety of surgical procedures and treatments. This energy is generated by solid-state diodes, which provide a consistent and reliable generation of laser energy at 810 ± 20nm for a maximum of 3 watts of energy output. The laser energy is delivered to surgical site by means of an optical fiber system, which allows for the safe transmission of laser energy to the site without creating undue risk to the patient or operatory staff by errant or collateral laser emissions. The device features some user definable settings, including a switchable 630nm aiming beam, adjustable power output for both the working beam and aiming beam, and continuous delivery or pulse delivery options. The working end of the delivery fiber is contained within a metal handpiece with a disposable single-use tip. This handpiece system is incorporated into the device. The activation of the working beam diodes is completed by use of a foot-actuated switch.

The provided document is a 510(k) summary for the Precise SHP Diode Laser. It outlines the device's characteristics, intended uses, and claims of substantial equivalence to predicate devices. However, it does not contain the specific acceptance criteria or details of a study that proves the device meets those criteria in the format of clinical performance metrics (e.g., sensitivity, specificity, accuracy, or other quantitative measures of effectiveness).

Instead, the document focuses on demonstrating substantial equivalence based on:

• Similar intended uses: Both the proposed device and predicates are for dentistry and oral soft tissue procedures, temporary pain relief, and teeth whitening.
• Similar technological characteristics: Wavelength, operating controls, laser delivery method, control systems, safety features, and performance monitoring.
• Conformity to standards: Compliance with regulatory standards like 21 CFR 1040.10, 1040.11, IEC 60601-2-22, IEC 60825-1, IEC 60601-1 (3rd Edition), IEC 60601-1-2, IEC 60601-1-4, and IEC 60601-1-6.
• Bench testing: A general statement about bench testing indicating the device met design criteria, satisfied performance requirements of 21 CFR 1010 and 21 CFR 1040, and that outputs were within requirements and safety features functioned correctly.

Therefore, I cannot provide a table of acceptance criteria and reported device performance in the requested format (e.g., sensitivity, specificity, accuracy) because this information is not present in the provided text. The document describes a bench testing study, but not a clinical study with detailed performance metrics.

Here's an analysis of what is provided, based on your requested categories:

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

Note: This table is an interpretation based on the provided text, which states the device "met the design criteria for essential performance, and satisfied the performance requirements indicated in 21 CFR 1010 and 21 CFR 1040. Device outputs were within performance requirements and all safety features and functions were operating correctly." Specific numerical or qualitative performance metrics beyond these general statements are not given.

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

• Sample Size: "an evaluation sample of the current device." The exact number of devices in this sample is not specified.
• Data Provenance: Not specified, but generally, bench testing for a 510(k) submission would be conducted by the manufacturer (CAO Group, Inc.) in a controlled lab environment. It is not patient or human data, so "country of origin" and "retrospective/prospective" are not applicable in a clinical sense.

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

• Not applicable. The study was bench testing, not a clinical study involving human assessment or ground truth established by experts. The "ground truth" would be the engineering specifications and regulatory requirements.

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

• Not applicable. This refers to clinical studies where different experts might disagree on an assessment. Bench testing typically involves objective measurements against predefined specifications.

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 was a bench test of a laser device, not an AI-based diagnostic tool. Therefore, MRMC studies and AI assistance are not relevant to this submission.

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

• Not applicable. This is not an algorithm, but a physical medical device (laser). The bench testing described essentially represents the "standalone" performance of the device against its specifications.

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

• The "ground truth" for the bench testing would be the engineering specifications and performance requirements defined by the manufacturer and relevant regulatory standards (e.g., maximum power output, wavelength accuracy, safety feature functionality).

8. The sample size for the training set

• Not applicable. This is not an AI device, so there is no training set in the machine learning sense. The "training" for the device would be its design and manufacturing process.

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

• Not applicable for the same reason as above.

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The Precise SHP Diode Laser is indicated for the removal of lesions, excision, incision, vaporization, ablation, hemostasis, and photocoagulation on oral soft tissue for the specific dental and oral surgery procedures of gingivectomy, freeectomy, operculectomy, contouring, biopsy, troughing, ulcer care, abscess care, sulcular debridement, soft tissue curettage, and removal of inflamed edematous tissue.

The Precise SHP Diode Laser is a device for delivering laser energy to human soft tissue for a variety of surgical procedures and treatments. This energy is generated by solid-state diodes, which provide a consistent and reliable generation of laser energy at 810 ± 20nm for a maximum of 3 watts of energy output. The laser energy is delivered to surgical site by means of an optical fiber system, which allows for the safe transmission of laser energy to the site without creating undue risk to the patient or operatory staff by errant or collateral laser emissions. The device features some user definable settings, including a switchable 630nm aiming beam, adjustable power output for both the working beam and aiming beam, and continuous delivery or pulse delivery options. The working end of the delivery fiber is contained within a metal handpiece with a disposable single-use tip. This handpiece system is provided with the device. The activation of the working beam diodes is completed by use of a foot-actuated switch.

The provided 510(k) summary for the "Precise SHP Diode Laser" does not contain information about acceptance criteria and a study proving those criteria are met in the way a diagnostic device approval would. This device is a surgical instrument (a diode laser), not a diagnostic algorithm. For such devices, the assessment criteria focus on manufacturing, safety, and performance specifications rather than diagnostic accuracy metrics.

Therefore, many of the requested points cannot be answered from the provided text, as they are not applicable to the type of device and regulatory submission presented.

Here's an analysis based on the information provided, and where information is missing for a diagnostic device context:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not present acceptance criteria in a quantitative table format with corresponding performance results similar to a diagnostic device. Instead, it refers to compliance with regulatory standards and general functional performance.

2. Sample Size for Test Set and Data Provenance

Not applicable in the context of a diagnostic test on patient data. The "test set" here refers to the device itself.

• Sample size: "Bench testing on an evaluation sample of the current device" - implies at least one device was tested.
• Data provenance: N/A (bench testing of the physical device).

3. Number of Experts and Qualifications for Ground Truth

Not applicable. Ground truth for a diagnostic device would involve independent clinical review or pathological confirmation. For a surgical laser, experts might design the testing protocols, but they aren't establishing a "ground truth" diagnosis.

4. Adjudication Method

Not applicable for a device performance test of a surgical laser.

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

Not applicable. This is not a diagnostic imaging device where human readers interact with AI.

6. Standalone Performance Study

Yes, in a sense. The "bench testing" described is a standalone evaluation of the device's physical and functional performance without human interaction beyond operating the device.

7. Type of Ground Truth Used

The "ground truth" for this device's performance is adherence to:

• Engineering design specifications.
• Performance requirements outlined in 21 CFR 1010 and 21 CFR 1040.
• Relevant international safety and performance standards (e.g., IEC 60601 series, IEC 60825-1).

8. Sample Size for Training Set

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

9. How Ground Truth for Training Set was Established

Not applicable. This device is not an AI/ML algorithm.

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The device is intended for use in obtaining biopsies from soft tissues such as liver, breast, kidney, prostate, spleen, lymph nodes and various soft tissue tumors. For breast biopsy this product is for diagnosis only - not for therapeutic use.

The PreciseCore™ Biopsy Device is a sterile, disposable device which features a stainless steel cutting cannula and stylet. The device is comprised of a plastic housing that contains the mechanically actuated components. The stylet of the device is stationary, therefore acquiring tissue without an advancement of the needle set beyond the visualized tip.

This 510(k) submission (K093399) for the PreciseCore™ Biopsy Device primarily focuses on demonstrating substantial equivalence to a predicate device through performance testing. However, it does not provide detailed acceptance criteria, specific performance metrics in a tabular format, or a comprehensive study report with the kind of information typically requested (e.g., sample sizes for different sets, ground truth establishment details, expert qualifications, MRMC studies).

Here's an analysis based on the provided text, highlighting what is and is not present:

Acceptance Criteria and Device Performance (Based on available information)

The document states: "Performance testing confirms that the quality of samples obtained with the PreciseCore™ Biopsy Device is equivalent to that of the predicate device." This implies the acceptance criterion was "equivalence in sample quality" to the predicate, but no specific metrics or thresholds are provided.

Missing Information (Based on the provided text):

The provided 510(k) summary does not contain the following details:

1. Sample sizes used for the test set and the data provenance: There is no mention of a specific clinical "test set" in terms of patient or tissue samples. The performance testing appears to be functional or mechanical, comparing the device's output (sample quality) to a predicate.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: This is not applicable as there isn't a described "test set" requiring expert ground truth establishment in the context of clinical images or diagnostic outcomes. The "quality of samples" likely refers to characteristics like length, integrity, or cellularity, evaluated by laboratory personnel or pathologists, but no specifics are given.
3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable for the reasons above.
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: This is not applicable. The device is a physical biopsy instrument, not an AI-powered diagnostic or imaging tool.
5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: This is not applicable, as it is a physical biopsy device, not an algorithm.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): While the "quality of samples" would likely refer to histopathological evaluation, the specific method of establishing this ground truth for the comparison is not detailed.
7. The sample size for the training set: Not applicable, as this is not an AI/ML device requiring a training set.
8. How the ground truth for the training set was established: Not applicable.

Summary of Study (Based on provided text):

The submission relies on a "Performance Testing Summary" to demonstrate substantial equivalence.

• Study Design/Purpose: To confirm that the quality of samples obtained with the PreciseCore™ Biopsy Device is equivalent to that of the predicate device (K994272 – Promex Automated Core Biopsy Device, Inrad® AccuCore Single Action Core Biopsy Device, and US Biopsy SABD).
• Methodology (General): "Performance testing" was conducted. While no specifics are given, for a biopsy device, this typically involves mechanical testing (e.g., penetration force, firing reliability) and potentially benchtop testing on tissue phantoms or ex vivo tissue to assess core length, integrity, and cellular yield when compared to the predicate device.
• Data Provenance: Not specified, but likely laboratory/benchtop testing rather than clinical data from a specific country.
• Result: The testing "confirms that the quality of samples obtained with the PreciseCore™ Biopsy Device is equivalent to that of the predicate device."

Conclusion:

The K093399 submission successfully demonstrated substantial equivalence for the PreciseCore™ Biopsy Device by asserting that its performance testing confirmed sample quality equivalent to its predicate. However, it lacks the detailed breakdown of acceptance criteria, specific performance metrics, and clinical study information (like sample sizes, ground truth establishment, expert qualifications, or MRMC studies) that would be expected for an AI/ML device or a device requiring detailed clinical validation data. This is typical for 510(k)s for predicate-based medical devices where the equivalence is often shown through functional, mechanical, and benchtop testing rather than extensive clinical efficacy trials.

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The Preciset DAT Plus I calibrators are designed for the calibration of the Roche assays for drugs of abuse in human urine on automated clinical chemistry analyzers.

The Preciset DAT Plus II calibrators are designed for the calibration of the Roche assays for drugs of abuse in human urine on automated clinical chemistry analyzers.

The Cfas DAT Qualitative Plus calibrator is designed for the qualitative calibration of the Roche assays for drugs of abuse in human urine on automated clinical chemistry analyzers.

Roche Preciset DAT Plus I calibrators contain a mixture of 10 different drugs, prepared by the quantitative addition of drug or drug metabolite to drug-free human urine. Drugs included are amphetamines, barbiturates, benzodiazepines, cannabinoids, cocaine, methadone, methaqualone, opiates, phencyclidine and propoxyphene. The calibrator set contains up to six levels for each drug contained in bottles 1-6. Bottle 1 is negative (drug free) human urine, followed by bottles 2-6 containing increasing amounts of each drug in a multi-analyte mixture.

Roche Preciset DAT Plus II calibrators contain a mixture of 4 different drugs, prepared by the quantitative addition of drug or drug metabolite to drug-free human urine. Drugs included are amphetamines, benzodiazepines. cannabinoids, and opiates. The calibrator set contains up to six levels for each drug contained in bottles 1-6. Bottle 1 is negative (drug free) human urine, followed by bottles 2-6 containing increasing amounts of each drug in a multianalyte mixture.

Roche Cfas DAT Qualitative Plus calibrator contains a mixture of 10 different drugs, prepared by the quantitative addition of drug or drug metabolite to drug-free human urine. Drugs included are amphetamines, barbiturates, benzodiazepines, cannabinoids, cocaine, methadone, methaqualone, opiates, phencyclidine, and propoxyphene. The calibrator set contains a single level for each drug in a drug mixture.

This 510(k) premarket notification is for calibrators used with drug abuse assays, not a diagnostic device with performance metrics like sensitivity and specificity. Therefore, many of the requested categories (e.g., expert consensus, MRMC studies, standalone performance) are not applicable in the traditional sense for this type of device.

This submission focuses on demonstrating substantial equivalence to already marketed calibrators by Roche. The "acceptance criteria" here are implicitly meeting the performance characteristics and intended use of the legally marketed predicate devices.

Here's an attempt to structure the available information according to your request, with significant caveats for the non-applicability of certain criteria:

Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

1. Table of Acceptance Criteria and Reported Device Performance

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

• N/A (for traditional diagnostic device testing): This submission is for calibrators, not a diagnostic test that processes patient samples to generate a "test set" in the conventional sense. The "test" here involves comparing the composition and intended use of the new calibrators to existing, cleared predicate calibrators.
• Data Provenance: The calibrators themselves are "prepared by the quantitative addition of drug or drug metabolite to drug-free human urine." This suggests an in-house manufacturing process rather than external data provenance like country of origin or clinical data collection. The submission doesn't specify any external "testing" data from a large population.

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

• Not Applicable: For calibrators, "ground truth" isn't established by expert consensus on clinical images or patient data. The "ground truth" concerning the calibrators' composition is established by the manufacturing process – the quantitative addition of specified drug concentrations to drug-free human urine. Analytical techniques would verify these concentrations during manufacturing and quality control.

4. Adjudication Method for the Test Set

• Not Applicable: There is no clinical "test set" and thus no need for an adjudication method for disagreements among experts, as would be common in diagnostic imaging or clinical assessment studies.

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

• No: MRMC studies are used to assess the impact of a diagnostic device (often AI-based) on human reader performance. This submission is for calibrators, which are reagents used to set the measurement scale for an assay, not a diagnostic tool read by humans.

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

• Not Applicable: Calibrators do not have an "algorithm" or standalone "performance" in the way an AI diagnostic device would. Their function is to provide known reference points for an assay. Their performance is related to their stability, accuracy of stated concentrations, and ability to correctly calibrate the associated assays, which would be validated during manufacturing and quality control, not as a standalone "algorithm."

7. The Type of Ground Truth Used

• Manufacturing Specifications / Quantitative Addition: The "ground truth" for these calibrators is their precisely defined chemical composition. They are "prepared by the quantitative addition of drug or drug metabolite to drug-free human urine." This means the concentration of each drug in each level of the calibrator is precisely known and controlled during the manufacturing process.

8. The Sample Size for the Training Set

• Not Applicable: Calibrators are physical reference materials, not algorithms that require a "training set" of data.

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

• Not Applicable: As above, there is no training set for this type of device.

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