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Artificial Intelligence - Europe PMC

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Last Updated: 10 January 2023

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Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections.

Antimicrobial resistance, which is widespread use of antimicrobial agents, is a significant barrier to these agents' effectiveness. Chronobiology is present in all biological organisms. The circadian clock is thought to have influenced Host responses to infections and pathogens activity. This paper discusses the topic of antimicrobial resistance and discusses some of the latest AI techniques. These studies contribute to the emergence of a new type of resistance and possibly overcome existing resistance, and may be a significant barrier to these agents' effectiveness. IMPORTANCE SECTION We recommend the development of a second-generation AI chronobiology-based strategy to help avoid further resistance and possibly overcome existing resistance and potentially overcome existing resistance.

Source link: https://europepmc.org/article/MED/36594558


Clinical value of artificial intelligence in thyroid ultrasound: a prospective study from the real world.

The diagnosis of AI, residents, and senior radiologists with and without AI data was analyzed. Increasing the unnecessary biopsy rate by up to 27% for nodules > 1. 5 cm was greatly improved by AI-assisted resident management, notably reducing the unnecessary biopsy rate by up to 27 percent for nodules > 1. 5 cm. In real-world medical practice, the AI-based approach can greatly improve the overall diagnostic outcome for less-experienced radiologists, increasing the detection of thyroid cancer u2264 1. 5 cm and minimizing unnecessary biopsies for nodules > 1. 5 cm. U2022 The AI system has reached a senior radiologist-like position in the diagnosis of thyroid disease diagnosis, and it could greatly enhance resident diagnostic accuracy. u2022 The AI-assisted approach greatly improved the AUC, precision, and sensitivity of the patients, resulting in an elevated detection of thyroid cancer while maintaining a similar generality to radiologists alone, leading to an increased detection of thyroid cancer.

Source link: https://europepmc.org/article/MED/36622410


Artificial intelligence based left ventricular ejection fraction and global longitudinal strain in cardiac amyloidosis.

Background: The determination of cardiac amyloidosis is aided by a left ventricular ejection fraction and global longitudinal strain. Methods We found 51 patients with confirmed CA, who underwent echocardiography before and/or at the time of CA diagnosis median time between observations 3. 87. Both the pre-CA and CA echoes for LVEF and GLS were found to be strongly related, respectively, on both the pre-CA and CA echoes for LVEF and GLS. In the pre-CA echo and 70% and 79% at CA diagnosis, respectively, the sensitivity and specificity of AI-derived indices for finding abnormal LVEF were 83% and 86%, respectively. In the pre-CA echo and 100% and 67% at the time of CA diagnosis, the sensitivity and specificity of AI-derived indices for detecting abnormal GLS were 82% and 86%.

Source link: https://europepmc.org/article/MED/36621915


Learning to Live with Strange Error: Beyond Trustworthiness in Artificial Intelligence Ethics.

Position papers on artificial intelligence ethics are often framed as attempts to devise technological and regulatory plans for achieving what is commonly described as trustworthy AI. The paper continues to develop the concept of strange coincidence, helping to sharpen the initial diagnosis of suspicious AI's inadequacy and to describe the use of AI in a novel epistemological sense. The paper concludes with a discussion of how strange behaviour puts pressure on traditional methods of assessing moral culpability, especially in the context of medicine.

Source link: https://europepmc.org/article/MED/36621773


Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review.

The aim of this paper is to investigate the reliability of artificial intelligence-based methods for grading fundus images in diabetic retinopathy screening. Accuracy reports published in English were included if they fulfilled the pre-specified inclusion criteria. Forty-three studies were included, evaluating 15 deep learning and 4 machine learning methodologies. In at least one QUADAS-2 domain, the majority of studies were found to have a significant or unclear risk of bias. Sensitivity for referable DR and higher grades was 85%, but specificity varied and was above 80% for all ML systems and in 6/31 studies testing DL systems. AI was more reliable but less specific than human graders, according to seven studies. Conclusions AI-based technologies are more sensitive than human graders and may be safer to use in clinical research, but have variable specificity.

Source link: https://europepmc.org/article/MED/36617971


Evaluating the potential of artificial intelligence in ulcerative colitis.

Introduction Diagnosis and therapeutic treatment of ulcerative colitis rely on a combination of endoscopic and histological scorings that are difficult to quantify objectively. Based on a literature search conducted on Pubmed, Embase, and Cochrane Library, we hope to give a concise and critical summary of the latest advancements in AI and UC. Expert opinion UUC management is changing, with AI affecting virtually every aspect of it, according to an expert. The collection, extraction, and organization of specific clinical data is the primary goal of AI in UCUC leadership.

Source link: https://europepmc.org/article/MED/36610437


Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles.

In patients undergoing single thawed embryo transfer cycles, we wanted to investigate the role of standardized preimplantation genetic testing for aneuploidy using artificial intelligence in patients undergoing single thawed euploid embryo transfer cycles. PGT-A was used by a single, large university-based fertility center with patients undergoing in vitro fertilization from February 2015 to April 2020, according to the PGT-A. The first experimental group included embryos that were analyzed by NGS using AI and machine learning. embryos were examined by AI 1. 0 and SNP analysis for the second group. Overall, those tested via AI 1. 0 showed a significantly elevated euploidy rate, reduced simple mosaicism rate, and reduced aneuploidy rate. Overall, those tested through AI 2. 0 showed a significant rise in euploidy prevalence and reduced simple mosaicism rate. When comparing AI 2. 0 to NGS, the aneuploidy rate was marginally reduced. In the AI 2. 0 category, the OP/LBR was significantly higher.

Source link: https://europepmc.org/article/MED/36609941


The Future of Cybersecurity with Artificial Intelligence (AI) and Machine Learning (ML)

Although artificial intelligence aids scientists in crime analysis, and understanding, it has a favourable effect on cyber defense, as shown by the fact that, although artificial intelligence aids with crime analysis, study, and recognition. In fact, the VPN industry learns from AI in the same way. Using a VPN on all of your devices could reduce the threat posed by machine learning in AI to user data privacy. According to Smart Data Collective, artificial intelligence has been apparently being investigated as a way to improve internet security for a considerable length of time. Two years ago, we predicted that AI and machine learning would have a major influence on cyber security's future.

Source link: https://europepmc.org/article/PPR/PPR593560


Artificial intelligence-based approaches to evaluate and optimize phytoremediation potential of in vitro regenerated aquatic macrophyte Ceratophyllum demersum L.

Water bodies that use aquatic plants or macrophytes are well established and are considered as a eco-friendly world over. Plants of C. demersum were exposed to 24, 72, 120 h, 0, 1. 0, 2. 0, 2. 0, and 4. 0 mg/L of cadmium in water. Cd uptake by plants, Cd uptake by plants, bioconcentration factor, and Cd removal from water were all found to have a significantly different relationship. The report revealed that Cd uptake by plants and BCF values increased with exposure time. Plant samples exposed to 2 mg/L Cd for 72 h were found to have the highest BCF value, with different exposure durations yielding Cd removal from the ranges of 93. 8 to 97 percent. These obtained results show that vitro regenerated C. demersum can be used for phytoremediation of Cd-contaminated aquatic environments.

Source link: https://europepmc.org/article/MED/36607572


Targeting trypanosomes: how chemogenomics and artificial intelligence can guide drug discovery.

Trypanosomatids, which cause human and animal neglected diseases, are protozoan parasites that cause human and animal neglected diseases. In this article, we explore how genomics is being used for drug discovery in trypanosomatids, how linking chemical and genomics data from these and other animals has aided prioritized the selection of candidate therapeutic goals and additional chemical starting points, as well as the growth of drug discovery pipelines into the period of artificial intelligence.

Source link: https://europepmc.org/article/MED/36606702

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions