View clinical trials related to Artificial Intelligence.
Filter by:Objectives: The study aimed to compare the success and reliability of an artificial intelligence application in the detection and classification of submerged teeth in orthopantomography (OPG). Methods: Convolutional neural networks (CNN) algorithms were used to detect and classify submerged molars. The detection module, which is based on the state-of-the-art Faster R-CNN architecture, processed the radiograph to define the boundaries of submerged molars. A separate testing set was used to evaluate the diagnostic performance of the system and compare it to the expert level. Results: The success rate of classification and identification of the system is high when evaluated according to the reference standard. The system was extremely accurate in performance comparison with observers. Conclusions: The performance of the proposed computer-aided diagnosis solution is comparable to that of experts. It is useful to diagnose submerged molars with an artificial intelligence application to prevent errors. Also, it will facilitate pediatric dentists' diagnoses.
The ageing of the population is accompanied by the problem of chronic pathologies and sometimes heavy dependence, requiring admission to Nursing Homes (NHs). Approximately 660,000 people currently live in NHs in France. One out of 3 NHs does not have a coordinating doctor, even though the law requires it, and access to care in these NHs is very unequal nationally and especially in the Limousin and Dordogne regions. Some Hospices may find themselves in a situation where there is no coordinating doctor and difficult access to General Practitioners (GPs) visiting a large area. This inequality of access to care results in a difference in care that can go as far as a loss of opportunity for residents who are immediately transferred to the emergency department (ED) with a risk of iatrogeny or delirium once in the ED or a risk of inappropriate hospitalization. Residents are hospitalized: - when the latter could have been avoided because the health care team, not knowing what attitude to adopt, prioritized hospitalization - Late because the resident waited for the attending physician to come, which resulted in a worsening of symptoms. The arrival of Artificial Intelligence (AI) is an opportunity to find new models of care organization that can mitigate medical desertification but also develop advanced practices in gerontology. For example, nurses will be able to intervene at a first level for early detection, better triage and early management of certain pathologies. The "MEDVIR society" AI, developed by a French company, is a medical decision support system with Artificial Intelligence and offers pre-diagnosis based on the information collected (medical and surgical history, concomitant treatments and symptoms). MEDVIR is a diagnostic aid tool and does not replace the doctor who remains at the end of the chain, the final decision-maker. Before research is conducted to integrate this technology into routine care, it is important to validate the diagnostic relevance of AI in the elderly, as it has been validated in the general population. This pilot feasibility study will then enable us to methodologically dimension a future project to evaluate the efficiency of this new care system in the management of elderly patients in medical deserts in France.
Artificial Intelligence may provide insight into exploring the potential covert association behind and reveal some early ocular architecture changes in individuals with hepatobiliary disorders. We conducted a pioneer work to explore the association between the eye and liver via deep learning, to develop and evaluate different deep learning models to predict the hepatobiliary disease by using ocular images.
In China, the number of children's medical services is still far behind the growing demand for children's health care. The phenomenon of children's parents queuing overnight for registration is no longer surprising. This is because of the increase in the number of children and the shortage of pediatric talents. In the department of pediatrics, the number of patients increases year by year, but pediatrician is short of supply from beginning to end. In addition to outpatient service, pediatricians in large hospitals also perform operations, scientific research and other tasks. As a result, many doctors have to give up their vacations, which makes them miserable and reduces their enthusiasm for work. The long queuing time also reduced the satisfaction of patients, resulting in the intensification of the conflict between pediatric doctors and patients. This research project aims to create a human-computer integrated system and develop a new diagnosis process embedded with artificial intelligence (AI). The function of AI system mainly includes 3 aspects. (1) The patient uses a mobile phone application embedded with AI that allows him to have check-up before see a doctor. The program will ask the patient a number of questions. Then, based on the patient's answers, AI will recommend a series of examination, all of which would be reviewed by the physician beforehand. After the patient pays for it, he could go straight to do the examination. So, next he could go to the doctor with the examination report which saves the patient the trouble of queuing. (2) At the same time, the AI system could also automate the medical history. The patient would complete self-help history collection in the spare time. The AI system collects the medical history and automatically import it to the doctor's computer. Doctors' main job is to modify the medical history generated by AI. To some extent, it lightens the burden of doctors. (3) During the visit, the AI system automatically captures the information in the patient's electronic medical record and generates the possible diagnosis. This process is of great help to junior doctors, and may serve as a cue. In short, this study is helpful to effectively reduce the waiting time of patients and greatly increase their medical experience. While reducing the work intensity of doctors, the outpatient procedure of our hospital has been effectively optimized to alleviate the shortage of pediatricians to some extent.
The aim of this study is to show the physiological changes during manic episode in bipolar mania how much they differentiate from remission and healthy control. Relation of audio-visual features as physiological changes and cognitive functions and clinical variables will be searched. The aim is to find biologic markers for predictors of treatment response via machine learning techniques to be able to reduce treatment resistance and give an idea for personalized treatment of bipolar patients.
Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low risk of harbouring cancer. Current practice is to send all these polyps for histopathological assessment by the pathologist. If an endoscopist would be able to correctly predict the histology of these diminutive polyps during colonoscopy, histopathological examination could be omitted and practise could become more time- and cost-effective. Studies have shown that prediction of histology by the endoscopist remains dependent on training and experience and varies greatly between endoscopists, even after systematic training. Computer aided diagnosis (CAD) based on convolutional neural networks (CNN) may facilitate endoscopists in diminutive polyp differentiation. Up to date, studies comparing the diagnostic performance of CAD-CNN to a group of endoscopists performing optical diagnosis during real-time colonoscopy are lacking. Objective: To develop a CAD-CNN system that is able to differentiate diminutive polyps during colonoscopy with high accuracy and to compare the performance of this system to a group of endoscopist performing optical diagnosis, with the histopathology as the gold standard. Study design: Multicentre, prospective, observational trial. Study population: Consecutive patients who undergo screening colonoscopy (phase 2) Main study parameters/endpoints: The accuracy of optical diagnosis of diminutive colorectal polyps (1-5mm) by CAD-CNN system compared with the accuracy of the endoscopists. Histopathology is used as the gold standard.
Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables real-time histological evaluation of gastric mucosal disease during ongoing endoscopy examination. However this requires much experience, which limits the application of pCLE. The investigators designed a computer-aided diagnosis program using deep neural network to make diagnosis automatically in pCLE examination and contrast its performance with endoscopists.
Our group, prior to the present study, developed a handcrafted predictive model based on the extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This method was validated in a small dataset containing only high-quality images. Artificial intelligence is expected to improve the accuracy of colorectal polyp optical diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.
Traditional school scoliosis screening approaches remains debatable due to unnecessary referal and excessive cost. Deep learning algorithms have proven to be powerful tools for the detection of multiple diseases; however, the application of such methods in scoliosis screening requires further assessment and validation. Here, the investigators develop an artificial system for the automated screening of scoliosis using disrobed back images, and conduct clinical trial to validate if the diagnostic system can offsetting the shortcomings of human doctors.
Visual development during early childhood is a vital process. Examining the visual acuity of children is essential for the early detection of visual abnormality, but performing such an assessment in children is challenging. Here, the investigators developed a human-in-the-loop artificial intelligence (AI) paradigm that combines traditional vision examination and AI with integrated software and hardware, thus making the vision examination easy to perform. The investigator also establish a entity intelligent visual acuity diagnostic system based on the paradigm, and conduct clinical trial to validate if the diagnostic system can offsetting the shortcomings of human doctors.