A multireader, multicase retrospective study demonstrated that the use of an artificial intelligence–based tool significantly improved the average area under the receiving operating characteristic ... Purpose To evaluate the benefits of an artificial intelligence (AI)–based tool for two-dimensional mammography in the breast cancer detection process. Materials and Methods In this multireader, mul...

We propose a methodology for monitoring an artificial intelligence (AI) tool for breast cancer screening when deployed in clinical centers. An AI trained to detect suspicious regions of interest in the four views of a mammogram and to characterize their level of suspicion with a score ranging from one (low suspicion) to ten (high suspicion of malignancy) was deployed in four radiological centers across the US. Results were collected between April 2021 and December 2022, resulting in a dataset of 36,581 AI records. To assess the behavior of the AI, its score distribution in each center was compared to a reference distribution obtained in silico using the Pearson correlation coefficient (PCC) between each center AI score distribution and the reference. The estimated PCCs were 0.998 [min: 0.993, max: 0.999] for center US-1, 0.975 [min: 0.923, max: 0.986] for US-2, 0.995 [min: 0.972, max: 0.998] for US-3 and 0.994 [min: 0.962, max: 0.982] for US-4. These values show that the AI behaved as expected. Low PCC values could be used to trigger an alert, which would facilitate the detection of software malfunctions. This methodology can help create new indicators to improve monitoring of software deployed in hospitals.

Diffusion tensor imaging is being increasingly used as a means to elucidate the brain's fiber structure. High spatial resolution is needed to capture details of the anatomy for tractography. However, image deteriorating factors such as low contrast-to-noise ratio, partial volume effects and subject's displacements affect the analysis. We introduce a procedure that uses single subject acquisitions at multiple b-values followed by an image processing procedure that consists of: a tensor estimation in the log-Euclidean space, a tensor registration and rotation that corrects misalignment, and averaging through acquisitions aiming to diminish the effect of the image deteriorating factors mentioned above. By applying this pipeline in an animal model that reproduces our size-limiting factors, we obtain contrast-to-noise ratio improvements of 40.9%, 110.5% and 70.8% in isometric voxels of 0.2mm³, 0.3mm³ and 0.4mm³ respectively.