Search for: All records

IEEE 

Recent developments in the use of artificial intelligence in the diagnosis and monitoring of glaucoma are discussed. To set the context and fix terminology, a brief historic overview of artificial intelligence is provided, along with some fundamentals of statistical modeling. Next, recent applications of artificial intelligence techniques in glaucoma diagnosis and the monitoring of glaucoma progression are reviewed, including the classification of visual field images and the detection of glaucomatous change in retinal nerve fiber layer thickness. Current challenges in the direct application of artificial intelligence to further our understating of this disease are also outlined. The article also discusses how the combined use of mathematical modeling and artificial intelligence may help to address these challenges, along with stronger communication between data scientists and clinicians. 

Purpose . Paired-agent molecular imaging methods, which employ coadministration of an untargeted, “control” imaging agent with a targeted agent to correct for nonspecific uptake, have been demonstrated to detect 200 cancer cells in a mouse model of metastatic breast cancer. This study demonstrates that indocyanine green (ICG), which is approved for human use, is an ideal control agent for future paired-agent studies to facilitate eventual clinical translation. Methods. The kinetics of ICG were compared with a known ideal control imaging agent, IRDye-700DX-labeled antibody in both healthy and metastatic rat popliteal lymph nodes after coadministration, intradermally in the footpad. Results. The kinetics of ICG and antibody-based imaging agent in tumor-free rat lymph nodes demonstrated a strong correlation with each other ( r  = 0.98, p < 0.001 ) with a measured binding potential of −0.102 ± 0.03 at 20 min postagent injection, while the kinetics of ICG and targeted imaging agent shows significant separation in the metastatic lymph nodes. Conclusion. This study indicated a potential for microscopic sensitivity to cancer spread in sentinel lymph nodes using ICG as a control agent for antibody-based molecular imaging assays. 

We introduce the notion of Quality of Indicator (QoI) to assess the level of contribution by participants in threat intelligence sharing. We exemplify QoI by metrics of the correctness, relevance, utility, and uniqueness of indicators. We build a system that extrapolates the metrics using a machine learning process over a reference set of indicators. We compared these results against a model that only considers the volume of information as a metric for contribution, and unveiled various observations, including the ability to spot low-quality contributions that are synonymous to free-riding.