View clinical trials related to Artificial Intelligence.
Filter by:It is a prospective, observational cohort study of patients with dense breast tissue. The study was based on the radiomics and other clinicopathological information of patients to establish the diagnostic system for breast disease by using artificial intelligence.
To improve the quality of mental health services, we will develop a robot that includes disease screening, diagnosis, treatment, and follow-up. The effectiveness of robots will be verified in a prospective, randomized, multi-center clinical trial. We assume that the robot will reduce the differences in the experience of doctors of different years and will improve mental health care across the country, and improve the uneven distribution of mental health resources through remote resource sharing.
This project's goal is to develop and test an application that uses Artificial Intelligence (AI) to improve consistency and quality of Radiation Treatment (RT) plans for prostate cancer. By understanding expert planner preferences in structure contouring and treatment planning, and combining this framework with planning data and outcomes amassed in NRG clinical trials, AI models may be trained to produce contours and treatment plans that are indistinguishable or even potentially deemed superior to those produced by individual experts. At the conclusion of this contract, the awardees will provide a software product which, when given the input of a description of desired anatomical target volumes and target doses along with a patient's CT scans, will generate target volumes and radiation treatment plans based upon a "gold standard" amalgamated from the input of multiple experts, thereby achieving desired doses to target volumes while meeting or exceeding the dose-volume constraints imposed by adjacent normal tissues.
This controlled-randomized trial compares the artificial intelligence Genius® system assisted (Genius+) to standard (Genius-) colonoscopy. The aim of this study was to evaluate the impact of Genius® system on ADR in routine colonoscopy. The secondary aims will be the impact of Genius® system on polyp detection rate (PDR), serrated polyp detection rate (SPDR), advanced neoplasia detection rate (ANDR), mean number of polyps (MNP), polyp type and localization, and operator type (according to basal ADR).
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the Artificial intelligence community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non- COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.
One fourth of colorectal neoplasias are missed during screening colonoscopies-these can develop into colorectal cancer (CRC). In the last couple of years, Artificial Intelligence Deep learning systems were introduced in the endoscopic setting to allow for real-time computer-aided detection/characterization (CAD) of polyps with high- accuracy. Few CADe (detection) and CADx (diagnosis, characterization) have been therefore proposed with this purpose. Because CAD systems are based on deep learning where the computer directly learns polyp recognition from supervised data without any human-control on the final algorithm, their outcome incorporates some unpredictability in the clinical setting that must be cautiously interpreted after its application. This means that the endoscopist may be presented with FP images that he would have never been selected in the first place as suspicion areas. These FPs may hamper the efficiency of CADe-colonoscopy. Additional time may be required to discriminate between an actual FP and a possible false negative result. An excess of FPs may reduce the motivation of the endoscopist for CADe, leading to its underuse in clinical practice. Although the indications of a CADe must always be interpreted by physician, FP may result in unnecessary polypectomy with related adverse events when used without appropriate training. Yet, there is a lack of information among quantity and quality of False Positive signals provided by the systems. From a post-hoc analysis of a Randomized Clinical Trial, in which we extracted and analysed a video library of CADe-colonoscopy (GI Genius) performed in our institution Humanitas Clinical and Research Hospital IRCCS we aimed that False positives by CADe are primarily due to artefacts from the bowel wall. Despite a high frequency, FPs from this CADe system resulted in a negligible 1% increase of the total withdrawal time as most of them were immediately discarded by the endoscopists.
This study; It will be carried out with the aim of developing the artificial intelligence method, which allows automatic determination of comfort levels of newborns.
The randomised clinical trial investigates the effect of using a clinical decision support system (CDSS) aiming to provide the patients and surgeons with greater transparency concerning the obtainable changes in function and health related quality of life (HRQoL) when patients are to decide if they should undergo hip- or knee replacement surgery.
Computer vision using deep learning architecture is broadly used in auto-recognition. In the research, the deep learning model which is trained by categorized single-eye images is applied to achieve the good performance of the model in blepharoptosis auto-diagnosis.
Slit-lamp images are widely used in ophthalmology for the detection of cataract, keratopathy and other anterior segment disorders. In real-world practice, the quality of slit-lamp images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of slit-lamp images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.