View clinical trials related to Artificial Intelligence.
Filter by:In this study, the investigators proposed a prospective study about the effectiveness of speech and image recognition-based system in improving reporting quality during colonoscopy for colonoscopy report quality in endoscopists. The participants would be divided into two groups. For the collected colonoscopy videos, group A would record their observations with the assistance of the artificial intelligence system. The artificial intelligence assistant system can automatically capture bowel segment images and prompt abnormal lesions. Group B would complete the endoscopy report without special prompts. After a period of washout period, the two groups switched, that is, group A without AI assistance and group B with AI assistance to complete the colonoscopy report. Then, the completeness of the colonoscopy report, the completeness of capturing anatomical landmarks and detected lesions, the completeness of structured description, the accuracy of lesion reporting, the time for reporting and the satisfaction with the reporting system are compared with or without AI assistance.
To conduct an single blinded, non-randomized, prospective, single center trial to validate the performance of a novel state-of-the-art Artificial Intelligence model (AI-Model) for colorectal lesion detection during routine diagnostic colonoscopy and to evaluate its feasibility in daily endoscopy. Consecutive patients referred for a screening, surveillance or diagnostic colonoscopy will be included
In this study, we proposed a prospective study about the effect of the automatic surveillance system on surveillance rate of colorectal postpolypectomy patients. The enrolled patients were divided into group A with intelligent surveillance system, group B with manual reminder, and group C with natural state. The surveillance among the three groups were compared.
The study has an initial short retrospective component but is predominately a prospective study with two main parts. Initially during a 1 month period whilst reporters are familiarising themselves with the software two local databases will be reviewed by the AI software: - A training set of 100 chest X-rays (CXR) some of which contain nodules and is used as a training tool with previously documented radiologist performance. - A set of previously reported radiographs in patients referred by the reporter for CT, ground truth created from the prior CT report and review by two radiologists if required. This will allow comparison of stand-alone radiologist and AI performance This is followed by a 6 month period involving multiple groups of reporters and approximately 20,000 cases looking at the impact of an AI system which assesses 10 abnormalities on chest X-ray and reporting on the sensitivity for detection of lesions and its impact on reporter confidence. Specifically the investigators would look at: - Missed finding by AI, but detected by reporter - Correctly detected finding by AI - Missed finding by the reporter but detected by AI - Finding detected by AI but disputed by the reporter ■ AI's impact on - Radiological report - Further recommended imaging - Altering patient management - improvement in report confidence as perceived by reporter A subsequent 3 month period looking at the impact of AI produced worklists on report turnaround times and the patient pathway from chest X-ray to CT. the investigators would specifically look at: - number of nodules detected - number of CXRs recommended for follow up CT - time taken from CXR to CT - number of lung cancers detected after CT[1] - Time to report, measured as previously from PACS and reporting software data The population to be studied will be all patients over 16 years of age referred by their General Practitioner to Hull University Hospitals NHS Trust for a chest radiograph and any chest radiograph performed in the Hull Royal Infirmary ED radiology for patients over 16 years of age during the 6 month study period. The ED department images patients from the emergency department and in-patients within the hospital. All radiographs will be reviewed initially without review of the AI information and then using the additional images. Reporters will mark the effect of the AI on their decision. All disagreements between the reporter and the AI will be reviewed by senior reporters and a consensus decision made.
Diabetic retinopathy is frequent, potentially severe with visual threat, health costly and represents a major public health problem. However, screening compliance for retinopathy remains too low in France, approximately 40% patients with diabetes laking diabetic retinopathy screening for at least 2 years. DIABeyeIA is a prospective pilot study evaluating the effectiveness and acceptability of diabetic retinopathy screening in 11 pharmacies in Normandy (north of France) using a non-mydriatic portable retinophotograph enhanced by artificial intelligence software. The main goal of this work is to evaluate a potential increase rate of diabetic retinopathy screening, compared to the actual rate (64% in France). Secondary goals are faisability, satisfaction and economical considerations for implementation of such a new screening program.
By introducing artificial intelligence into Chinese medicine tongue diagnosis, we collated and collected tongue images, anxiety and depression scales and gastroscopy reports, mined and analysed the correlation between tongue images and bile reflux and anxiety and depression and constructed a prediction model to analyse the possibility of predicting bile reflux and anxiety and depression in patients based on tongue images.
This study combines artificial intelligence with tongue images, by collating and collecting tongue images and diagnostic and pathological results of gastroscopic diseases, mining and analysing the correlation between tongue images and OLGA, OLGIM stages, Correa sequences and constructing prediction models, to deeply investigate the relationship between tongue images and precancerous diseases, precancerous lesions and gastric cancer.
Based on the portable slit-lamp connected to the smartphone, the artificial intelligence machine learning algorithm is used to establish a cataract screening model by prospectively collecting the anterior segment photographic data, and a portable slit lamp intelligent screening referral model based on the smartphone connection is also established.
The development of sepsis prediction model in line with Chinese population, and extended to clinical, assist clinicians for early identification, early intervention, has a good application prospect. This study is a prospective observational study, mainly to evaluate the accuracy of the previously established sepsis prediction model. The occurrence of sepsis was determined by doctors' daily clinical judgment, and the results of the sepsis prediction model were matched and corrected to improve the clinical accuracy and applicability of the sepsis prediction model.
A variety of diseases in the Department of Rheumatology, Immunology, Nephrology, and Gastroenterology can cause eye lesions, and medications can also bring various adverse reactions, which can seriously reduce the quality of patients' daily life, bring additional economic burdens, and even threaten the lives of patients. This study aims to recruit patients from the aboved-mentioned departments and conduct a cross-sectional and cohort study. On one hand, we plan to compare the epidemiological characteristics of ocular lesions of systemic diseases and eye adverse drug effects in patients with rheumatology, immunology, nephrology and gastroenterology, and summarized some epidemiological indices such as prevalence, high-risk factors, etc. On the other hand, we plan to develop an artificial intelligence model after collecting certain case data. By selecting risk factors related to the occurrence of ocular lesions, we aim to train models that can predict the ocular manifestations of systemic diseases and medications.