Clinical Trials About "Practical implementation artificial intelligence algorithms pulmonary auscultation examination" RSS

04:29 EDT 19th May 2019 | BioPortfolio

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Showing "Practical implementation artificial intelligence algorithms pulmonary auscultation examination" Clinical Trials 1–25 of 10,000+

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Automated Phonocardiography Analysis in Adults

Background: Computer aided auscultation in the differentiation of pathologic (AHA class I) from no- or innocent murmurs (AHA class III) via artificial intelligence algorithms could be a useful tool to assist healthcare providers in identifying pathological heart murmurs and may avoid unnecessary referrals to medical specialists. Objective: Assess the quality of the artificial intelligence (AI) algorithm that autonomously detects and classifies heart murmurs as either pathologi...

Simple, Mobile-based Artificial Intelligence AlgoRithms in the Detection of Diabetic ReTinopathy (SMART) Study

This is an observational cross sectional study aimed to evaluate the performance of the artificial intelligence algorithm in detecting any grade of diabetic retinopathy using retinal images from patients with diabetes.

Quality Improvement Intervention in Colonoscopy Using Artificial Intelligence

Quality measures in colonoscopy are important guides for improving the quality of patient care. But quality improvement intervention is not taking place, primarily because of the inconvenience and expense. To address the difficulty in intervention, we used artificial intelligence for measuring performance on colonoscopy quality indicators and feedback.

Validation of an Artificial Intelligence-based Algorithm for Skeletal Age Assessment

The purpose of this study is to understand the effects of using a Artificial Intelligence algorithm for skeletal age estimation as a computer-aided diagnosis (CADx) system. In this prospective real-time study, the investigators will send de-identified hand radiographs to the Artificial Intelligence algorithm and surface the output of this algorithm to the radiologist, who will incorporate this information with their normal workflows to make a diagnosis of the patient's bone age...

Tailored Drug Titration Through Artificial Intelligence

ARTERY is a randomized clinical trial that investigates the benefit of a predictive modeling artificial intelligence in improving the management of anti-hypertensive medication treatment.

Programming Cochlear Implant With Artificial Intelligence

This thesis project proposes to investigate the "state of the art" of the programming of the cochlear implant. In the center of audiophonologie Brussels, the classic 'manual programming' has been in use over 20 years and also the new way 'Artificial Intelligence programming'. The investigators want to compare, objectify, and control this new mode of programming. The study is planned over 4 years, in order to test, randomized, 15 subjects with manual programming and 15 other su...

Breast Ultrasound Image Reviewed With Assistance of Deep Learning Algorithms

This study evaluates a second review of ultrasound images of breast lesions using an interactive "deep learning" (or artificial intelligence) program developed by Samsung Medical Imaging, to see if this artificial intelligence will help the Radiologist make more accurate diagnoses.

Artificial Intelligence in Echocardiography

The goal of this study is to determine whether the Bay Labs artificial intelligence (AI) system can be used by minimally trained operators to obtain diagnostic quality echocardiographic images.

Association Analysis and Medical Machine Intelligence Study for Traditional Chinese Internal Medicine

This study aimed at 1. exploring the relationships of traditional Chinese medicine (TCM) syndrome and internal diseases, 2. investigating the relationships of TCM treatment and internal diseases, and 3. creating medical machine intelligence to help internal physician in clinical practice. Big data methods and artificial intelligence algorithms are used to assess the associations based on electronic case files of patients.

Multi-modal Imaging and Artificial Intelligence Diagnostic System for Multi-level Clinical Application

This study is to build an multi-modal artificial intelligence ophthalmological imaging diagnostic system covering multi-level medical institutions. We are going to evaluate this system in an evidence-based medicine view, taking diabetic retinopathy as an example. And clinical diagnostic criteria will be made based on this multi-modal artificial intelligence imaging diagnostic system. The study is designed as a cross-sectional study involving 1,000 normal individuals, 1,000 diab...

Validation of the Utility of Ophthalmology Intelligent Diagnostic System

The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. Application scenarios of the current medical algorithms are too simple to be generally applied to real-world complex clinical settings. Here, the investigators use "deep learning" and "visionome technique", an novel annotation method for artificial intelligence in medical, to create an automatic detection and classification system for four key clinical sce...

Detection of Cerebral Ischemia With Artificial Intelligence.

In patients undergoing planned surgery for carotid tromendarterendectomy, a non-invasive device that registers heart rate variability is attached. furthermore a non-invasive device that monitors cerebral oxygenation- near infrared spectroscopy is also attached. At times when surgeons clamps the carotid artery, there will be a moment with controlled cerebral ischemia. This will be registered by the devices. The information obtained will be used to teach artificial intelli...

The Evaluation of Surgical Decisions and Prognosis of the Radiomics and Watson Artificial Intelligence in Patients With Hepatocellular Carcinoma

The aim of this study was to evaluate the surgical decisions and prognosis of the radiomics and Watson artificial intelligence in patients with hepatocellular carcinoma.

Artificial Intelligence-assisted Glaucoma Screening (AIAGS)

This study will develop an artificial intelligence (AI) program for the screening of glaucoma.

Using Artificial Intelligence to Measure and Optimize Adherence in Patients on Anticoagulation Therapy.

AiCure uses artificial intelligence and visual recognition technology to confirm medication ingestion. The software is available as an app and downloaded onto a smart phone. The single-site, parallel-arm, randomized controlled trial will test the feasibility and impact of using the platform in a stroke population. End points: usability, consistent use of the device, and optimization of treatment.

Artificial Intelligence With Deep Learning and Genes on Cardiovascular Disease

An association study with large database from electronic medical record system, images, outcome analysis and genetic single nucleotide polymorphism variations by machine learning and artificial intelligence methods in a Taiwanese and Chinese medical center based population


Multi-center Database Registry to Study Thalamus Changes Using AI in MS

In this study the Investigator's propose to validate a newly developed approach, DeepGRAI (Deep Gray Rating via Artificial Intelligence), to simplify the calculation of thalamic atrophy in a clinical routine and allow academic and community neurologists to plan, perform, and publish novel and influential clinical research using data from clinical routine, by employing deep machine learning (DML) pattern recognition (PR) information through use of artificial intelligence (AI).

Spinal Cord and Artificial Intelligence

The aim of this study was to analyze using an artificial intelligence engine (IA) the influence of the pathophysiological environment (set parametric monitoring data, imaging, biology etc.) of acute spinal cord trauma on spinal pain. This project seeks to establish the principles of a new approach for studying spinal cord injury patients. It does not meet the usual criteria of clinical trials in so far as it does not test on patients the effect of a therapeutic

Elucid Labs AIDA™ - Labelled Image Acquisition Protocol

Diagnosis of melanoma involves physical examination of the lesion with many dermatologists adjunctively employing dermoscopes. The rate of misdiagnosis of melanoma remains significant, along with a high rate of referral to biopsy. Elucid Labs (Waterloo, Ontario) has developed a novel handheld, digital dermoscope with accompanying visualization and analysis software - the Artificial Intelligence Dermatology Assistant (AIDA™). Apart from collecting conventional demoscopic image...

Digital Auscultation Test - IPF Data Collection

The aim of this study is the data collection for patients with IPF and symptom matched controls to create a database of lung auscultation sounds and basic patient characteristics.

The Santa Cruz Diabetic Retinopathy Utilizing Artificial Intelligence Study

In this clinical trial, we plan to evaluate the usefulness of artificial intelligence (AI) software paired with a handheld retinal camera to compare diabetic retinopathy status in Bolivian patients as read by retina specialists versus the AI software.

Developing Smokers for Smoker (S4S): A Collective Intelligence Tailoring System

This study will advance computer tailoring by adapting machine learning collective intelligence algorithms that have been used outside healthcare by companies like Amazon and Google to enhance the personal relevance of the health communication.

Real-world Diagnostic Effectiveness of Artificial Intelligence Algorithm in Diabetic Retinopathy Screening

Recently, artificial intelligence algorithm has made great progress in the prediction of diabetic retinopathy based on fundus images,showing very high sensitivity and specificity. However,the real-world diagnosis effectiveness of deep learning model is still unclear. This study is designed to evaluate the clinical efficacy of such an algorithm in detecting referable diabetic retinopathy.

SYNERGY-AI: Artificial Intelligence Based Precision Oncology Clinical Trial Matching and Registry

International registry for cancer patients evaluating the feasibility and clinical utility of an Artificial Intelligence-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate clinical trial enrollment (CTE), as well as the financial impact, and potential outcomes of the intervention.

Development and Validation of a Deep Learning Algorithm for Bowel Preparation Quality Scoring

The purpose of this study is to develop and validate the performance of an artificial intelligence(AI) assisted Boston Bowel preparation Scoring(BBPS) system for evaluation of bowel cleanness, then testify whether this new scoring system can help physicians to improve the quality control parameters of colonoscopy in clinic practice.

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