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Artificial Intelligence - ClinicalTrials.gov

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Last Updated: 10 January 2023

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Use of Artificial Intelligence-Guided Echocardiography to assIst cardiovascuLar Patient managEment

Rural and remote Australia's rural population and the prevalence of rheumatic heart disease in the Aboriginal population are disproportionate problems in rural and remote Australia relative to the rest of the country, due in part to an ageing rural population and the occurrence of rheumatic heart disease in the Aboriginal population. Late diagnoses can result in avoidable hospital admissions and expenditures to the Australian health system. The study will be conducted in four regions in RRA, from ii Nepean Hospital to Dubbo Hospital and Western NSW, with a HF risk factor and recruited by clinic and community outreach in four areas, including ii Princess Alexandra Hospital, Roma, Charleville and Western Queensland, and the Royal Perth Hospital in collaboration with the Royal Perth Hospital and the Derbarl Yerrigan Health Service. Approximately 1200 people at risk of HF and VHD will be tested and followed up.

Source link: https://clinicaltrials.gov/ct2/show/NCT05558605


AD HOC Trial: Artificial Intelligence-Based Drug Dosing In Hepatocellular Carcinoma

This research will test the hypothesis that a novel combination of three drugs, in conjunction with individually tailored doses, can be safely administered and lead to improved clinical outcomes in patients with hepatocellular carcinoma compared to the normal course of care. Individualization of dosing can be achieved by using Phenotypic Personalized Medicine to enhance treatment success in patients of hepatocellular carcinoma and minimize toxicity while still minimizing toxicity.

Source link: https://clinicaltrials.gov/ct2/show/NCT05669339


Application of Artificial Intelligence in Early Detection of Eye Complications in Diabetics: A Randomized Clustered Trial in Hail, Saudi Arabia

Recognizing the high incidence of type 2 diabetes mellitus among adults, the use of a nonmydriatic fundus camera with AI in eye exams is cost-effective in eye exams as it improves adult adherence to eye screening. The primary aim of the trial will be to determine the suitability of AI devices in terms of fundus cameras in the early detection of diabetic retinopathy and macular oedema among diabetic patients attending primary care centers. To what extent does the use of artificial intelligence-based eye care at a primary care center contributes to a high incidence of macular oedema, according to the study. To what extent can the use of artificial intelligence-based eye care at a primary care clinic be helpful in achieving a high incidence of retinopathy? OBJECTIVE OB:Economy: To determine the success of applying AI-based eye care at primary care centers in diabetics with a high incidence of macular oedema and retinopathy. Specific Goals: Aim 1: To determine the proportion of diagnosed cases of macular oedema in the intervention group versus the control group at the primary care center. This is a six-month clustered randomised trial that will enroll patients with type II diabetes who are attending primary eye care clinics at primary care centers in Hail city. Participants : The participants will be type II diabetic patients of both genders of the selected primary care centers, regardless of their duration of disease and the types of drugs currently used.

Source link: https://clinicaltrials.gov/ct2/show/NCT05655117


Demonstration of an Artificial Intelligence Based Closed Loop Glucose Control System as a Therapeutic Modality in Type 1 and Type 2 Diabetic Patients

In the intensive care unit setting, tight glucose control is impossible to achieve. The investigators hypothesize that a closed loop glucose control system based on artificial intelligence will enhance glucose control that was previously achieved by the existing open loop manual approaches, and that this enhanced glucose monitoring could improve the outcomes of critically ill patients, including those with COVID-19. In a clinical research center setting, this Earl Feasibility Study will determine whether a prototype artificial intelligence-based closed loop glucose control device, called FUSION, will provide safe and effective glucose monitoring in subjects with type 1 and type 2 diabetes. Subjects with type 1 diabetes have been chosen as safe and effective glucose monitoring is impossible to achieve in these subjects during meal challenges. Subjects with type 2 diabetes have been selected because they are insulin resistant, which makes their insulin sensitivity profile similar to that of ICU patients. Two Dexcom continuous glucose monitors, the AI-based glucose measurement software run on an all-in-one medical computer, and two syringe pumps will be included in the prototype FUSION system that will be used in this research. In addition, both the FUSION system and each individual CGM unit's average glucose value will be compared to the Nova StatStrip system for comparison between systems using the Surveillance Error grid.

Source link: https://clinicaltrials.gov/ct2/show/NCT05644730


Reference Values and Clinical Screening Test of Diffuse Noxious Inhibitory Controls (DNIC) Using Deep Learning and Artificial Intelligence

To determine references values for facial expressions of pain management systems in healthy and chronic pain patients, as well as chronic pain sufferers. The reference values will be established by a non-parametric technique for a standard conditioned pain modulation protocol, in which two'stimuli tests' of the same length and nature will be applied before and after the application of another 'conditioning stimulus'. The degree of the DNICs will determine the perceived pain difference between the 1st and the 2nd stimuli assessments. The best predictors of a CPM deficit will be determined by logistic regression analysis.

Source link: https://clinicaltrials.gov/ct2/show/NCT04896827

* 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