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The Disposable Enteral Feeding Sets is intended to be used for enteral feeding nutrition into gastrointestinal tract by connecting with nasogastric tube by gravity. The devices may include a bag to contain the feeding nutrition and/or a spike to connect to a prefilled container. The device is used for children, adolescents, and adults.

The proposed device, Disposable Enteral Feeding Sets, is intended to be used for enteral feeding nutrition into gastrointestinal tract by connecting with nasogastric tube by gravity. The devices may include a bag to contain the feeding nutrition and/or a spike to connect to a prefilled container. The device is used for children, adolescents and adults. The proposed device is provided sterile or non-sterile and single use.

The provided text is a 510(k) Pre-market Notification for a medical device called "Disposable Enteral Feeding Sets". The document largely focuses on demonstrating substantial equivalence to a predicate device and does not contain the detailed information typically found in a study description for evaluation of AI/algorithm performance.

Therefore, I cannot provide the requested information about acceptance criteria and a study proving a device meets acceptance criteria in an AI/algorithm context. The document describes standard performance testing for a physical medical device, not a software or AI device.

Here's why and what information can be extracted regarding general acceptance criteria and testing:

Why the requested information cannot be fully provided from this document:

• No AI/Algorithm Focus: The device is a "Disposable Enteral Feeding Set," which is a physical medical device for enteral nutrition. There is no indication that it incorporates any AI, machine learning, or complex algorithms that would require the kind of performance studies (e.g., standalone performance, MRMC studies, ground truth establishment by experts) typically associated with AI/ML-driven medical devices.
• "Acceptance Criteria" for Physical Device: The term "acceptance criteria" here refers to the performance specifications and safety standards for a physical product (e.g., leak integrity, flow rate, material biocompatibility, sterility), not diagnostic accuracy or clinical utility of an AI.
• "Study" for Physical Device: The "study" mentioned refers to non-clinical performance tests (chemical, physical, sterility, ENfit physical performance) conducted on the physical device to ensure it meets established safety and performance standards relevant to its physical function. It is not a clinical efficacy study or an AI performance study.

Information that can be extracted relevant to general performance and acceptance (for a non-AI physical device):

1. A table of acceptance criteria and the reported device performance:
   The document does not provide a specific table of acceptance criteria paired with reported device performance values. It states that "the test result showed that the aged proposed device met the acceptance criteria" for various tests. The specific acceptance criteria are implicitly those defined by the referenced international standards and internal procedures.

   Acceptance Criteria (Implied)	Reported Device Performance
   Compliance with ISO 20695:2020 Annex C & D	Met
   Compliance with ISO 8536-4:2022 Annex B	Met
   Compliance with ISO 8536-4:2020 Annex B.4	Met
   Compliance with ISO 80369-20:2015	Met
   Compliance with USP Sterility Tests	Met
   Final product inspection procedure specifications	Met
   Performance of bag part (e.g., integrity, capacity)	Met
   Performance of spike part (e.g., connection integrity)	Met
   Biocompatibility (for patient-contact material)	Met (no adverse effects)

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):
   The document does not specify the sample sizes for the non-clinical performance tests. The data provenance is implied to be from the manufacturer's testing in China, as the applicant is "Beijing L&Z Medical Technology Development Co., Ltd." These are non-clinical lab tests, not human data.

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. "Ground truth" in the context of expert consensus is relevant for AI/ML diagnostic or prognostic devices. For a physical device like an enteral feeding set, performance is evaluated against engineering specifications and international standards, not against expert human interpretations of data.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
   Not applicable for a physical device's non-clinical performance testing.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
   Not applicable. The device is a physical feeding set, not an AI or imaging device that would involve human readers or AI assistance.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
   Not applicable. There is no algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
   Not applicable. Performance is measured against physical and chemical standards and specifications (e.g., sterility, dimensional accuracy, material integrity, flow rates), not a ground truth derived from expert consensus, pathology, or outcomes data.

8. The sample size for the training set:
   Not applicable. This is a physical medical device, not an AI/ML model that requires training data.

9. How the ground truth for the training set was established:
   Not applicable.

In summary, the provided FDA 510(k) clearance letter and summary pertain to a physical medical device and therefore do not contain the information requested regarding AI/algorithm performance studies.

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The Disposable Infusion Bag for Parenteral Nutrition is for use in compounding and storage of parenteral nutrition solutions prior to and during administration to a patient using an intravascular administration set.

The device is not intended to store the fluids for 24 hours or greater.

The proposed device is for use in compounding and storage of parenteral nutrition solutions prior to and during administration to a patient using an intravascular administration set. The proposed device is provided sterile and single use.

The proposed device is available in 7 series due to different bag capacities, different tube locations and different tube materials.

The provided text describes the 510(k) premarket notification for the "Disposable Infusion Bag for Parenteral Nutrition" (K222622) and compares it to a predicate device (K101412). However, it focuses on non-clinical tests and does not include information about clinical studies with human readers or AI performance metrics. Therefore, several sections of your request cannot be fulfilled by the provided document.

Here's an analysis based on the available text:

1. Table of acceptance criteria and the reported device performance

The document lists performance standards met by the proposed device, which serve as acceptance criteria. The "reported device performance" is a statement that the device complied with these standards. Specific numerical results or detailed performance metrics are not provided in this summary.

Study Proving Device Meets Acceptance Criteria:

The study that proves the device meets the acceptance criteria consists of a series of non-clinical tests conducted by the manufacturer, Beijing L&Z Medical Technology Development Co., Ltd. These tests aimed to verify that the proposed device met all design specifications and complied with established international and US national standards relevant to medical devices, particularly for I.V. containers.

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

The document does not specify sample sizes for each non-clinical test performed. It generally states that "Non clinical tests were conducted," implying that an appropriate number of samples were tested to achieve statistical confidence for each standard.

• Sample sizes: Not specified in the provided summary.
• Data provenance: The tests were conducted by the manufacturer, Beijing L&Z Medical Technology Development Co., Ltd., which is located in Beijing, P.R. China. The tests are non-clinical, meaning they involve laboratory or bench testing rather than human subjects. They are inherently prospective in the context of device development and verification.

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

This section is not applicable as the described study is a non-clinical device performance test, not a study involving interpretation by human experts or ground truth in the context of diagnostic performance (e.g., radiology). The "ground truth" here is adherence to the specified technical and biological safety standards.

4. Adjudication method for the test set

This section is not applicable for the same reason as point 3. Adjudication methods are relevant for subjective interpretations, typically in clinical studies or when establishing ground truth from expert opinions.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This submission is for a physical medical device (infusion bag), not an AI-powered diagnostic or assistive technology. Therefore, there is no information on human reader improvement with or without AI assistance.

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

No, a standalone algorithm performance study was not done. This device is not an algorithm or AI system.

7. The type of ground truth used

The "ground truth" for this device's performance is defined by the established international and national standards (e.g., ISO 15747, ASTM F1886, ISO 10993 series, USP ) and the specific design specifications of the device. The compliance with these objective, measurable standards constitutes the "ground truth" that the non-clinical tests were designed to verify.

8. The sample size for the training set

Not applicable. This is a physical medical device, not an AI or machine learning algorithm. Therefore, there is no training set in the context of data used to train a model.

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

Not applicable. As there is no training set for an algorithm, there is no ground truth established for one.

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The Diode Laser Therapy Systems is intended for hair removal, permanent hair reduction on all skin types (Fitzpatrick skin type I-VI), including tanned skin. Permanent hair reduction is defined as the long-term, stable reduction in the number of hairs regrowing when measured at 6, 9, and 12 months after the completion of a treatment regime.

Diode Laser Therapy Systems is a surgical device, which is intended for hair removal, permanent hair reduction on all skin types (Fitzpatrick skin type I-VI); The Diode Laser Therapy Systems utilize a semiconductor diode with invisible infrared radiation as a laser source to emit 808nm wavelength laser which is absorbed by melanin. The laser power is delivered to the treatment area via laser handpiece. The emission laser is activated by a foot-switch. The Diode Laser Therapy Systems utilize the principle of photoepilation for hair removal. The Photoepilation is a technique for removal of unwanted hair by thermal destruction of the hair follicle and its reproductive system (stems cells). The heat is caused by selective absorption of electromagnetic radiation emitted by laser light sources. As melanin is the main chromophore existing in hair follicles, so the melanin could absorb the energy from the laser, which would result in temperature rapid increase, then the hair follicle and its reproductive system (stems cells) would be destroyed by increased high temperature without damage epidermis and the surrounding normal tissue.

The provided text is a 510(k) summary for a medical device (Diode Laser Therapy Systems) and does not contain information about acceptance criteria and a study proving the device meets those criteria, as typically found in clinical study reports with specific performance metrics (e.g., sensitivity, specificity, accuracy).

Instead, this document focuses on demonstrating substantial equivalence to a predicate device based on non-clinical testing and general safety standards.

Therefore, many of the requested fields cannot be directly populated from the provided text. However, I can extract information related to the device's technical specifications and the non-clinical tests performed to support its safety and performance.

Here's a breakdown of what can and cannot be answered based on the provided document:

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

The document does not specify formal "acceptance criteria" in terms of clinical performance metrics (like sensitivity, specificity, etc.) that the device must meet. Instead, the acceptance is based on demonstrating substantial equivalence to a predicate device through:

• Matching intended use and fundamental technology.
• Complying with recognized safety and performance standards (IEC, ISO).
• Showing that any differences in technical specifications do not raise new questions of safety or effectiveness.

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

• The document explicitly states: "No clinical study is included in this submission."
• Therefore, there is no "test set" in the context of clinical data, no sample size, and no data provenance relevant to clinical performance. The testing mentioned (non-clinical) refers to bench testing on the device hardware and materials.

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. No clinical test set with ground truth established by experts is mentioned.

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

• Not applicable. No clinical test set.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• Not applicable. This device is a laser therapy system, not an AI-assisted diagnostic or imaging device. No MRMC study was conducted.

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

• Not applicable. This device is not an algorithm. Performance was assessed through non-clinical bench testing of the physical device.

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

• For the non-clinical tests, the "ground truth" or reference for compliance is established by international standards (IEC, ISO) for electrical safety, laser safety, electromagnetic compatibility, and biocompatibility, as well as the technical specifications of the predicate device.

8. The sample size for the training set

• Not applicable. No AI model or training set is mentioned as this is a physical medical device.

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

• Not applicable. No AI model or training set is mentioned.

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The Diode Laser Hair Removal Machine is intended for hair removal permanent hair reduction on all skin types (Fitzpatrick skin type IVI), including tanned skin.

Permanent hair reduction is defined as the long-term, stable reduction in the number of hairs regrowing when measured at 6, 9, and 12 months after the completion of a treatment regime.

The proposed device, Diode Laser Hair Removal Machine, is a surgical device. It utilizes a semiconductor diode as a laser source (808nm). The laser power is delivered to the treatment area via a laser handpiece. The emission laser is activated by a footswitch.

The treatment can be applied on different Fitzpatrick skin type, including I (White), II (White with pigment), III (Yellow), IV (Yellow with pigment), V (Brown) and VI (Black); in addition, the treatment can also be applied to different parts of the body.

The provided text is a 510(k) summary for a Diode Laser Hair Removal Machine. This document outlines the rationale for establishing substantial equivalence to a predicate device, focusing on non-clinical performance and safety data rather than a detailed study evaluating specific acceptance criteria for AI performance or clinical outcomes directly.

Therefore, many of the requested details regarding acceptance criteria, study design, expert involvement, and ground truth establishment (especially pertaining to AI/ML device performance or human reader improvement) are not present in this document. This document describes a medical device, but it is not an AI/ML device, and thus the acceptance criteria and study description won't align with questions about AI performance, human-in-the-loop studies, or extensive clinical trials as typically seen for AI-driven diagnostic tools.

Here's a breakdown of what can be extracted and what is missing based on the provided text:

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

The document doesn't present "acceptance criteria" in the traditional sense of a specific performance threshold (e.g., accuracy > X%). Instead, it demonstrates through non-clinical testing that the device meets safety and performance standards relevant to a laser hair removal machine by comparing it to a predicate device and showing compliance with specific technical standards (e.g., IEC standards, ISO for biocompatibility).

The "performance" is more about compliance with technical specifications and safety standards. The table "Table 1 Comparison of Technology Characteristics" (Page 7) serves as the primary comparison point.

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

• Test set sample size: Not applicable in the context of clinical performance testing for a device like this. The "test set" primarily refers to the device itself and its components undergoing non-clinical bench testing.
• Data provenance: The testing was non-clinical (bench testing, electrical safety, biocompatibility, software V&V). The document does not specify geographic origin for these tests. It states "No clinical study is included in this submission" (Page 6).

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

• Not applicable as no clinical study or human reader interpretation for ground truth was performed or required for this 510(k) submission. Ground truth for non-clinical tests would be established by standard measurement equipment and lab protocols.

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

• Not applicable. This is not a study involving human reader interpretation or adjudication of medical images/data.

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, an MRMC study was not done. This is not an AI-assisted device.

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

• No, this refers to an AI/ML algorithm. This device is a laser hair removal machine, not an AI algorithm.

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

• For the non-clinical tests, the "ground truth" is based on engineering specifications, direct measurements of physical properties (e.g., wavelength, power output, dimensions), and established laboratory testing standards (e.g., ISO 10993 for biocompatibility, IEC 60601 for electrical safety). There is no "pathology" or "outcomes data" in this context as no clinical study was conducted.

8. The sample size for the training set:

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

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

• Not applicable. As above, this is not an AI/ML device.

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The Q-Switched Nd: YAG Laser Therapy System is indicated for use in tattoo removal, treatment of benign vascular lesions, treatment of benign pigmented lesions, incision, ablation, vaporization of soft tissue for general dermatology as follows:

1064nm: -Tattoo Removal Dark ink: blue and black. -Treatment of Benign Pigmented Lesions Nevus of ota.

532mm: -Tattoo Removal Light ink: red, Light ink: sky blue and green. - Treatment of Benign Vascular Lesions Port wine birthmarks: Telangiectasias; Spider angioma: Cherry angioma; Spider nevi. -Treatment of Benign Pigmented Lesions Cafe-au-lait birthmarks; Solar lentiginos; Senile lentiginos; Becker's nevi; Freckles; Nevus spilus.

The proposed device is a multi-wavelength, pulsed laser system designed for the treatment of benign pigmented lesions. The device can produce two wavelengths, 1064nm and 532nm, to treat different skin color. It consists of control panel module, main control module, laser power module, temperature and humidity control module and laser module. The physician is able to select the desired waveleneth and the related output energy via control panel.

The provided text describes a 510(k) submission for a Q Switched Nd: YAG Laser machine. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than providing clinical performance data against specific acceptance criteria for AI/ML devices. Therefore, much of the requested information regarding AI/ML device testing (e.g., sample size for test set, number of experts for ground truth, MRMC studies, standalone performance) is not applicable or available in this document.

However, I can extract information related to the device's technical specifications and the non-clinical tests performed to demonstrate its safety and effectiveness in comparison to the predicate device.

Here's the information as far as it can be extracted from the provided text:

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

For a 510(k) submission, the "acceptance criteria" are typically defined by the technical specifications and adherence to recognized standards, demonstrating performance comparable to a predicate device. The "reported device performance" is then the demonstrated compliance with these specifications and standards.

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

The document states: "No clinical study is included in this submission." The testing described is non-clinical (laboratory/bench testing) verifying device specifications and adherence to standards. Therefore, an explicit "test set sample size" for patient data and "data provenance" (country of origin, retrospective/prospective) are not applicable to this type of submission. The tests involved physical device characteristics and materials.

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

Not applicable, as no clinical studies with human "test sets" or expert-established "ground truth" were part of this 510(k) submission.

4. Adjudication method for the test set

Not applicable, as no clinical studies with expert adjudication were part 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

Not applicable. This device is a laser machine, not an AI-assisted diagnostic or therapeutic tool that would involve human readers or AI assistance.

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

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

7. The type of ground truth used

For the non-clinical tests, the "ground truth" implicitly refers to the established scientific and engineering principles and the requirements defined by the international standards (e.g., ISO 10993 for biocompatibility, IEC 60601 series for electrical and laser safety). Device characteristics were measured against predefined specifications that are deemed safe and effective in comparison to the predicate device.

8. The sample size for the training set

Not applicable. This is not an AI/ML device, so no "training set" in that context exists.

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

Not applicable for the same reason as above.

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