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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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