An artificial intelligence solution (AI) is proposed that utilizes very recent advances in machine learning and deep learning to improve Atom Trap Trace Analysis (ATTA) image analysis. The proposed solution shortens turnaround times and extends the capabilities of ATTA systems allowing a full accounting, across densities, of the nature and number of radionuclides in the images to be analyzed. The proposed AI solution extracts and identifies atoms of interest from noise, allowing identification and quantification where otherwise images may have been degraded by scattered light, partial or non-integer atoms, and spurious camera data events such as x-rays, cosmic rays, muons etc. The proposed machine learning/deep learning approach greatly reduces the effect of statistical uncertainties, providing clean, rapid, and accurate atom count rates. The proposed algorithm provides a performance level significantly greater than the current, traditional approach and promises an atom detection rate range of 50 - 5000 atoms / hr, as a minimum result. The AI solution can easily be integrated into the ATTA data acquisition process to guide and inform when counting is statistically acceptable and transfer easily to other, similar applications where region of interest and quantification in noisy datasets is needed.
