A New York City based large volume private practice radiology group conducted a quality assurance review that included an 18 month software evaluation in the breast center comprised of nine (9) specialist radiologists using an FDA cleared artificial intelligence software by Koios Medical, Inc as a second opinion for analyzing and assessing lesions found during breast ultrasound examinations. Over the evaluation period, radiologists analyzed over 6,000 diagnostic breast ultrasound exams. Radiologists used Koios DS Breast decision support software (Koios Medical, Inc.) to assist in lesion classification and risk assessment. As part of the normal diagnostic workflow, radiologists would activate Koios DS and review the software findings with clinical details to formulate the best management. Analysis was then performed comparing the physicians' diagnostic performance to the 18-month period prior to the introduction of the AI enabled software.
A fully automated artificial intelligence (AI)-based multispectral absorbance imaging system effectively classified function and potency of induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE) from patients with age-related macular degeneration (AMD). The finding from the system could be applied to assessing future cellular therapies, according to research presented at the 2018 ARVO annual meeting. The software, which uses convolutional neural network (CNN) deep learning algorithms, effectively evaluated release criterion for the iPSC-RPE cell-based therapy in a standard, reproducible, and cost-effective fashion. The AI-based analysis was as specific and sensitive as traditional molecular and physiological assays, without the need for human intervention. "Cells can be classified with high accuracy using nothing but absorbance images," wrote lead investigator Nathan Hotaling and colleagues from the National Institutes of Health in their poster.
Technique key to scale up manufacturing of therapies from induced pluripotent stem cells. Researchers used artificial intelligence (AI) to evaluate stem cell-derived "patches" of retinal pigment epithelium (RPE) tissue for implanting into the eyes of patients with age-related macular degeneration (AMD), a leading cause of blindness. The proof-of-principle study helps pave the way for AI-based quality control of therapeutic cells and tissues. The method was developed by researchers at the National Eye Institute (NEI) and the National Institute of Standards and Technology (NIST) and is described in a report appearing online today in the Journal of Clinical Investigation. NEI is part of the National Institutes of Health.
Bracco Diagnostics Inc., the U.S. subsidiary of Bracco Imaging S.p.A., a leading global company in the diagnostic imaging business, announced the results of an experimental artificial intelligence (AI) study of two gadolinium-based contrast agents (GBCAs) which found that ProHance (Gadoteridol) Injection, 279.3 mg/mL and Gadavist provided similar degree and pattern of contrast enhancement in brain magnetic resonance imaging (MRI) of patients with glioblastoma multiforme (GBM) previously enrolled in a large scale, multicenter, randomized, double blinded controlled clinical study (the TRUTH study).1 Full study results will be presented at the Radiological Society of North America (RSNA) Annual Meeting on Wednesday, December 4, in Chicago, IL. GBCAs are widely used imaging agents with a favorable safety profile. While recent research has shown that the gadolinium from these agents may remain in the body for months to years after injection,2 the American College of Radiology and the Food and Drug Administration agree that there are no known adverse clinical consequences associated with gadolinium retention in the brain based on the available data.3,4 Nevertheless, some practitioners have concerns, and questions have been raised over whether using a GBCA that retains less would come with a tradeoff in the effectiveness of the contrast enhancement. The purpose of this study was to use AI to determine the effectiveness of standard concentration ProHance (0.5mmol/ml) compared to double concentration Gadavist (1.0 mmol/ml), since animal studies have shown that Gadavist retains two to seven times more in the brain versus ProHance, at up to 4 weeks after injection5-6.
Understanding the needs of a variety of distinct user groups is vital in designing effective, desirable dialogue systems that will be adopted by the largest possible segment of the population. Despite the increasing popularity of dialogue systems in both mobile and home formats, user studies remain relatively infrequent and often sample a segment of the user population that is not representative of the needs of the potential user population as a whole. This is especially the case for users who may be more reluctant adopters, such as older adults. In this paper we discuss the results of a recent user study performed over a large population of age 50 and over adults in the Midwestern United States that have experience using a variety of commercial dialogue systems. We show the common preferences, use cases, and feature gaps identified by older adult users in interacting with these systems. Based on these results, we propose a new, robust user modeling framework that addresses common issues facing older adult users, which can then be generalized to the wider user population.