View clinical trials related to Artificial Intelligence.
Filter by:With the emergence of advanced technology to date in the artificial intelligence (AI), computer aided diagnosis has gradually gained its popularity in the field of healthcare. Particularly, in the clinical practice of coronary artery disease diagnosis, the application of AI could be of great implication in alleviating the shortage of medical sources. To evaluate the effectiveness and safety of the AI-based coronary CT angiographic analysis software (RuiXin-CoronaryAI) for diagnosis of coronary artery stenosis, a retrospective, multi-center, cross-over designed, blinded, sensitivity superiority and specificity non-inferiority clinical trial will be conducted.
Idiopathic pulmonary fibrosis (IPF), non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive, irreversibly incapacitating pulmonary disorders with modest response to therapeutic interventions and poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence (AI)-assisted digital lung auscultation could constitute an alternative to conventional subjective operator-related auscultation to accurately and earlier diagnose these diseases. Moreover, lung ultrasound (LUS), a relevant gold standard for lung pathology, could also benefit from automation by deep learning.
Erythrocyte morphology analysis is a key step in the diagnosis flowchart of anemia. It is performed on a peripheral blood smear after May Grümwald Giemsa staining. In the context of hemolytic anemias for example, it allows the recognition of therapeutic emergencies such as sickle cell disease crisis, malaria-induced hemolysis and thrombotic microangiopathy, the latter being characterized by the presence of schistocytes and justifying an immediate clinical care. However, cytological analysis of erythrocyte morphology requires pre-analytical interventions (smear spreading + staining), the quality of which determines the accuracy of the result. Moreover, it requires a good cytological expertise and may be sometimes subjective. For several years, alternative methods for erythrocyte morphology evaluation have been developed, based on automated hematology machines or automated microscopy. Nevertheless, none of them has yet proven itself in comparison with cytology, especially in the diagnosis of thrombotic microangiopathies. By combining the advantages of flow cytometry and microscopy, flow imaging appears to be a promising technology for the diagnosis of anemias: it does not require any pre-analytical intervention, does not require any spreading and analyzes a large number of events. Moreover, it can be coupled with artificial intelligence via the generation of an apprenticeship by the constitution of a large image data base, which then allows the recognition of the different red blood cells morphologies without human eyes. The objective of this study is to build a data base containing the main red blood cell morphologies relevant in anemia, and to validate it through a comparison in anemic patients of erythrocyte morphological assessment either directly on whole blood by flow imaging or routinely by cytological analysis of peripheral blood smear after by a trained operator.
The application of artificial intelligence in image recognition of cervical lesions diagnosis has become a research hotspot in recent years. The analysis and interpretation of colposcopy images play an important role in the diagnosis，prevention and treatment of cervical precancerous lesions and cervical cancer. At present, the accuracy of colposcopy detection is still affected by many factors. The research on the diagnosis system of cervical lesions based on multimodal deep learning of colposcopy images is a new and significant research topic. Based on the large database of cervical lesions diagnosis images and non-images, the research group established a multi-source heterogeneous cervical lesion diagnosis big data platform of non-image and image data. Research the lesions segmentation and classification model of colposcopy image based on convolutional neural network, explore the relevant medical data fusion network model that affects the diagnosis of cervical lesions, and realize a multi-modal self-learning artificial intelligence cervical lesion diagnosis system based on colposcopy images. The application efficiency of the artificial intelligence system in the real world was explored through the cohort, and the intelligent teaching model and method of cervical lesion diagnosis were further established based on the above intelligent system.
This is a pragmatic, double-blind, randomized, controlled trial, to evaluate the effect of implementing a CADs system within the routine clinical practice of Canadian healthcare institutions. The main hypothesis of this study is that the ADR in the operating room equipped with the GI genius CADe system will be significantly higher than the ADR in the ordinary operating room.
This is an retrospective and prospective multicenter study to develop and validate an artificial intelligent (AI) aided diagnosis, therapeutic effect assessment model including chronic kidney disease (CKD) and dialysis patients starting from April 2009, which is based on ophthalmic examinations (e.g. retinal fundus photography, slit-lamp images, OCTA, etc.) and CKD diagnostic and therapeutic data (routine clinical evaluations and laboratory data), to provide a reliable basis and guideline for clinical diagnosis and treatment.
This study aimed to develop a deep-learning model to automatically classify pulmonary nodules based on white-light images and to evaluate the model performance. Besides, suitable operation could be chosen with the help of this model, which could shorten the time of surgery.
Colonoscopy is the gold standard for colorectal screening. The diagnostic accuracy of colonoscopy highly depends on the quality of inspection of the colon during the procedure. To increase detection new polyp detection systems based on artificial intelligence (AI) have been developed. However, these systems still depend on the ability of the endoscopist to adequately visualize the complete colonic mucosa, especially to detect smaller and more subtle lesions, or lesions hidden behind folds in the colon. With this study we want to combine a device to flatten the folds in the colon combined with an artificial intelligence system to further improve the detection rate of lesions during colonoscopy.
The purpose of this study is to highlight the usefulness of artificial intelligence and machine learning to develop computer algorithms that will achieve with great reliability, speed and accuracy the automatic extraction and processing of large volumes of raw and unstructured clinical data from electronic medical files.
This research study aims to bring an artificial intelligence system to screen for diabetic retinopathy (DR) along with referral tracking systems to the screening unit in Uthai Hospital in Phra Nakhon Sri Ayutthaya to assess the effectiveness of screening and follow-up of patients referred to Phra Nakhon Sri Ayutthaya Hospital. It will be compared with the existing screening system and follow up with regular referral by personnel