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Mine 2.1 is a portable medical diagnostic-purpose X-ray generator that can be hand-held. The device uses an adjustable tube voltage and a fixed tube current for producing diagnostic x-ray images of extremities for both adults and pediatrics. It is intended to be used by a qualified and trained clinician on all patients. It is not intended to replace a radiographic system with variable tube current and voltage (kVp) which may be required for full optimization of image quality and radiation exposure for different exam types.

Mine 2.1, a portable X-ray generator, is radiation medical equipment that can only be used by professional radiologists. It controls and marks X-ray dose within the range of X-ray exposure limited by hardware. Also, it uses the algorithm of X-ray output for processing and control. This portable X-ray generator requires equipment for X-ray imaging in order to generate X-ray images. Small in size, this product is convenient to carry with, and suitable for being moved around. The main body can be compatibly used with a stand. When attached to a stand, it is easy to adjust positioning for medical imaging. MINE ALNU is programmed to be inoperable when the SSD is less than 40cm to the irradiation target.

The VL53L1X, a laser-ranging sensor, the fastest miniature Time-of-Flight (ToF) sensor on the market with accurate ranging up to 4 m and fast ranging frequency up to 50Hz. VL53L1X contains a laser emitter and corresponding drive circuitry. The laser output is designed to remain within Class 1 laser safety limits under all reasonably foreseeable conditions including single faults in compliance with IEC 60825-1: 2014.

The x-ray detectors, a necessary part of a complete imaging system, are not part of the current submission. The device is not intended to be used with a mechanical grid.

The provided text is a 510(k) summary for the MINE ALNU X-ray generator. It focuses on demonstrating substantial equivalence to a predicate device (Remex KA6) based on technical characteristics and adherence to safety standards. The document does not describe a study involving a comparison of an AI algorithm's performance against human readers, nor does it detail a standalone AI algorithm's performance.

Instead, the "performance testing" described in the document refers to engineering and safety performance of the X-ray generator itself, not an AI or diagnostic algorithm's accuracy. The "acceptance criteria" mentioned relate to electrical safety, electromagnetic compatibility, radiation leakage, and image quality of the X-ray generation hardware, not the diagnostic accuracy of an AI.

Therefore, many of the requested points regarding AI acceptance criteria, study design for AI evaluation, expert ground truth, MRMC studies, and training data are not applicable (N/A) to the content of this document.

Here's a breakdown based on the information provided:

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

The document discusses "acceptance criteria" in the context of the device's hardware performance and safety rather than an AI's diagnostic performance. The criteria are primarily related to compliance with various IEC standards and FDA's EPRC Performance Standard (21 CFR 1020.30 and 31).

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

The document describes non-clinical bench testing and compliance evaluations for the X-ray generator's hardware. It does not mention a "test set" in the context of diagnostic images or patient data for an AI algorithm.

• Test Set Sample Size: N/A (not an AI performance study with a test set of images)
• Data Provenance: N/A (no patient data or image data set discussed for AI evaluation)

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

N/A. This document is about the X-ray generator hardware, not an AI algorithm requiring expert-established ground truth for diagnostic accuracy.

4. Adjudication method for the test set

N/A.

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 submission does not involve an AI diagnostic algorithm.

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

N/A. The device is an X-ray generator, not a standalone AI algorithm.

7. The type of ground truth used

N/A. Ground truth in the context of diagnostic accuracy is not discussed. The "ground truth" for this device's performance is adherence to established engineering and safety standards.

8. The sample size for the training set

N/A. There is no mention of an AI training set.

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

N/A.

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The MINE is intended to be used by trained dental technicians as extra-oral x-ray source for producing diagnostic x-ray images using intra-oral image receptors or film. Its use is intended for both adult and pediatric subjects.

MINE, a portable dental X-ray system, operates on 11.1V DC supplied by a rechargeable LiPolymer battery pack, The X-ray tube head, controls and power source are assembled into a single hand-held enclosure. The package includes a battery charger.

The portable X-ray system. MINE, being composed of X-ray generator, controller, and beam limiting device is designed to diagnose tooth and jaw through generated and controlled X-ray. The operating principle of MINE starts from the generation of X-ray by high voltage electricity, which in turn penetrates tooth and jaw area after flowing through X-ray tube and produces X-ray images on X-ray receptors (i.e. chemical film or digital sensor)

This device contains a high frequency inverter that converts direct to alternating current, X-ray tube head, electrical protective devices, and other elements. The MINE produces sharp and clear images and prevents patients and dentists from radiation exposure with utilizing small dose of radiation.

The provided text is a 510(k) summary for the medical device "MINE," an extra-oral x-ray system. This document focuses on demonstrating substantial equivalence to a predicate device ("EXARO") rather than presenting a clinical study with detailed acceptance criteria and performance data for AI/human reader studies.

Therefore, the information requested in the prompt regarding acceptance criteria, study design for AI/human reader performance, sample sizes, expert qualifications, and ground truth establishment for such studies is not available in the provided text. The document explicitly mentions non-clinical testing and phantom images to demonstrate performance.

Here's a breakdown of what is available in relation to your prompt:

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

The document does not provide a table of acceptance criteria for a clinical performance study with defined metrics (e.g., sensitivity, specificity, AUC) for an AI or human reader outcome. Instead, it focuses on demonstrating compliance with safety and performance standards relevant to an X-ray device itself.

The "reported device performance" is primarily about the device's technical specifications and adherence to standards:

The document notes that "Phantom images were provided to demonstrate the overall performance of the device as part of a complete intraoral x-ray imaging chain." This suggests a technical performance evaluation, not a clinical efficacy study involving diagnostic accuracy.

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

• Not applicable / not provided. The document describes a technical device and its compliance with performance standards, primarily through non-clinical testing and phantom images. There is no mention of a "test set" for a clinical study involving diagnostic accuracy.

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

• Not applicable / not provided. As there is no clinical test set described, there's no information on experts establishing ground truth for diagnostic purposes.

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

• Not applicable / not provided.

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 / not provided. This device is an X-ray source, not an AI-powered diagnostic tool. The submission is for a traditional medical device, demonstrating substantial equivalence to another X-ray device based on technical specifications and safety standards.

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

• Not applicable / not provided.

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

• Not applicable / not provided. The device's performance was assessed through phantom images and compliance with electrical/radiation safety standards, not clinical ground truth.

8. The sample size for the training set:

• Not applicable / not provided. 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 / not provided.

In summary: The provided document is a 510(k) summary for an X-ray device, focusing on its technical specifications, safety, and substantial equivalence to a predicate device. It does not contain information about acceptance criteria or study results for AI performance, human reader studies, or associated ground truth establishment, as it's not relevant to the type of device being cleared.

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Mineral Oil is used as an overlay for culture of embryos, oocytes, and sperm in assisted reproduction technology (ART) and micromanipulation procedures.

Mineral Oil is used to cover medium during embryo, oocyte, and sperm culture in assisted reproduction technology (ART) and micro-manipulation procedures. Mineral Oil is recommended for use as an overlay for a small volume of medium to prevent evaporation stable osmolality and pH.

The material composition:

High Purity Paraffin Oil comes in two density types:

• Light type has a ratio between 0.8200 to 0.8400 g/mL (15°C) and a viscosity which is between 8.850 to 11.70mm²/s (37.78°C)
• Heavy type has a ratio between 0.8500 to 0.8700 g/mL (15°C) and a viscosity which is between 41.90 to 44.10mm²/s (37.78°C).

There are two types of products, Mineral Oil - Light and Mineral Oil - Heavy. Two different unit sizes (100mL and 50mL) are available for each type. Mineral oils are colorless, tasteless, clear oil fluids which do not produce fluorescence.

Both Mineral Oils (Heavy and Light) do not contact the ova or embryo during culture. Mineral Oil is filter sterilized and dispensed into sterile light-resistant glass bottles.

The provided text describes a 510(k) premarket notification for a medical device called "Mineral Oil" used in assisted reproductive technology. This is not the type of document that typically includes information about acceptance criteria and a study proving the device meets those criteria, as one would find for a software-based AI/ML medical device.

The document is a US FDA 510(k) clearance letter and summary for a physical product (Mineral Oil) which functions as a reproductive media supplement. It aims to demonstrate "substantial equivalence" to a predicate device rather than independently proving safety and efficacy through extensive clinical studies and AI performance metrics.

Therefore, many of the requested categories for acceptance criteria and study details for an AI/ML device are not applicable or cannot be extracted from this document. However, I can extract the "non-clinical performance data" which serves as the "acceptance criteria" and "device performance" for this specific product, as well as some details about its testing.

Here's a breakdown of the information that can be extracted, and where the requested AI/ML specific information is not available:

1. Table of Acceptance Criteria and Reported Device Performance & 7. The type of ground truth used:

The acceptance criteria for this non-AI/ML device are based on non-clinical performance tests designed to establish substantial equivalence concerning safety and effectiveness. The "ground truth" here is essentially the compliance with these established biological and chemical specifications.

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 exact sample sizes for each non-clinical performance test. It only states that tests were completed. Data provenance is not detailed beyond the manufacturer being "KITAZATO BioPharma Co., Ltd." in Japan. The tests are non-clinical (laboratory-based), not human data, so "retrospective or prospective" does not apply in the typical sense.

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

This is not applicable as the "ground truth" for these non-clinical tests is based on established laboratory standards (e.g., USP , ), chemical specifications, and biological assay outcomes, rather than expert interpretation of medical images or patient data.

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

Not applicable for non-clinical laboratory tests.

5. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

Not applicable. This is for a physical medical device (mineral oil), not an AI/ML device.

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

Not applicable. This is a physical product, not an algorithm.

8. The sample size for the training set:

Not applicable. This is not an AI/ML device that requires a training set. The "device" itself is the mineral oil which is manufactured according to specifications.

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

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

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MINERVA 58 ALLOY is indicated as a dental alloy for fabricating MOD inlays, crowns, bridges, precision milling bars and attachments, and partial dentures.

MINERVA 58 ALLOY is a high noble, gold-based dental alloy.

The provided document is a 510(k) summary for a dental alloy, MINERVA 58 ALLOY, and not an AI/ML device. Therefore, the requested information (acceptance criteria, study details, sample sizes, ground truth establishment, expert qualifications, adjudication methods, MRMC studies, standalone performance, and training set details) is not applicable to this document. The document primarily focuses on demonstrating substantial equivalence to a predicate device based on material composition and prior marketing history in Europe.

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Indicated clinical applications include use as repair of pulpal exposures.

Mineral Trioxide Aggregate (MTA) is a cement-like substance which seals off all pathways of communication between the root canal system and the external surface of the tooth. Indicated clinical applications include use as repair of pulpal exposures. The powder consists of fine hydrophilic particles which set in the presence of moisture. Hydration of the powder results in a colloidal gel which solidifies to a hard structure.

Mineral Trioxide Aggregate is biocompatible, and exhibits significantly better adaptation to dentinal walls than other materials. It possesses a high degree of sealability; the presence or absence of blood does not affect its sealing ability. Setting time after hydration and application is approximately three hours. In tests, its compressive strength at 21 days is about 70 Mpa. Use of MTA requires only the appropriate hydration of the powder to a viscous consistency. The mixture can then be placed over the exposure site and gently patted into place using a moistened cotton ball if necessary.

This document provides a summary for a 510(k) submission for a medical device, Mineral Trioxide Aggregate (MTA), a dental cement. It does NOT contain the details of an acceptance criteria study as typically understood in the context of an AI/ML or diagnostic device.

The information provided describes the device, its intended use, and states that it is substantially equivalent to other devices on the market. It highlights some performance characteristics like setting time and compressive strength, but these are descriptive properties of the material, not acceptance criteria from a formal study proving clinical effectiveness or diagnostic accuracy.

Therefore, most of the requested information cannot be extracted from this text. Here's a breakdown of what can and cannot be answered:

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

Missing Information/Not Applicable:
The document does not define specific, quantifiable acceptance criteria for clinical outcomes (e.g., success rate of pulpal exposure repair) or diagnostic performance (e.g., sensitivity, specificity, AUC), nor does it present data from a formal clinical study designed to meet such criteria. The "performance" values described are material properties from laboratory tests.

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

• Not provided. This document is a summary for a 510(k) submission, not a detailed study report. The "tests" mentioned (e.g., for compressive strength) are likely laboratory material tests, not clinical trials with a test set of patients.

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. There is no "test set" of patient data in the sense of a diagnostic or AI study mentioned. Ground truth as typically defined for such studies is not relevant here.

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

• Not applicable. See point 3.

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 dental material, not an AI/ML-driven diagnostic or assistive device for human readers.

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

• Not applicable. This is not an algorithm.

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

• Not applicable. The "ground truth" for a dental cement would be its measured physical and biological properties in laboratory and potentially animal studies, and then clinical outcomes in human trials. This document reports some physical properties ("compressive strength") from "tests" (implying lab measurements), but doesn't detail clinical ground truth or how it was established.

8. The sample size for the training set

• Not applicable. There is no "training set" as this is not an AI/ML device.

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

• Not applicable. See point 8.

In summary, this document fulfills the requirements of a 510(k) summary for a traditional medical device by describing its properties and claiming substantial equivalence. It does not provide the kind of detailed study information (especially concerning acceptance criteria for diagnostic performance, sample sizes, expert ground truth, or AI/ML study design) that would be expected for a submission involving software or AI.

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