View clinical trials related to Artificial Intelligence.
Filter by:Colonoscopy is clinically used as the gold standard for detection of colon cancer (CRC) and removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. "Back-to-back" colonoscopies have indicated significant miss rates of 27% for small adenomas (< 5 mm) and 6% for adenomas of more than 10 mm in diameter. Studies performing both CT colonography and colonoscopy estimate that the colonoscopy miss rate for polyps over 10 mm in size may be as high as 12%. The clinical importance of missed lesions should be emphasized because these lesions may ultimately progress to CRC. Limitations in human visual perception and other human biases such as fatigue, distraction, level of alertness during examination increases recognition errors and way of mitigating them may be the key to improve polyp detection and further reduction in mortality from CRC. Recent advances in artificial intelligence (AI), deep learning (DL), and computer vision have permitted to develop several AI platforms which have already proved their efficacy in increasing adenoma detection during colonoscopy9,10. As a matter of fact, the improvement in detection due to AI systems is only related to the increased capacity of detecting lesions within the visual field, that is dependent on the amount of mucosa exposed by the endoscopist during the scope withdrawal. Increasing the mucosa exposure would theoretically be a complementary strategy to further improve polyps detection. A number of distal attachments have been tested to increase the mucosal exposure by flattening mucosal folds, including a transparent cap, cuff or rings. The additional diagnostic yield obtained by the second generation of cuff (Endocuff Vision; Olympus America, Center Valley, Pa, USA) was recently investigated by a meta-analysis of randomized controlled trials, showing a significant improvement in adenoma detection rate, and adenomas per colonoscopy, with a reduction in the mean withdrawal time without any increase in adverse events compared with standard high-definition colonoscopy without any distal attachment. In conclusion, technologies providing either mucosal image enhancement (Artificial Intelligence assisted colonoscopy) or mucosal exposure device (Endocuff Vision assisted colonoscopy) significantly improved adenoma detection rate (ADR). However, the diagnostic yield obtained by combining the different strategies is still unknown.
Digital health technologies (DHT) are increasingly developed to support healthcare systems around the world. However, they are frequently lacking evidence-based medicine and medical validation. There is considerable need in the western countries to allocate healthcare resources accurately and give the population detailed and reliable health information enabling to take greater responsibility for their health. Intelligent patient flow management system (IPFM, product name Klinik Frontline) is developed to meet these needs. In practice, IPFM is used for decision support in the triaging and diagnostic processes as well as automatizing the management of inflow of the patients. The core of the IPFM is a clinical artificial intelligence (AI), which utilizes a comprehensive medical database of clinical correlations generated by medical doctors. The study population of this research consists of patients from the Paediatric Emergency Clinic of Turku University Hospital (TUH). Data will be gathered during 6 months of piloting, after which the results will be analysed. Anticipated number of patients to the study is minimum of 500 patients, with objective to be 1 000. When attending to the hospital, patients or their guardians will report their demographics, background information and symptoms using structured IPFM online form. Results obtained from IPFM are blinded from the healthcare professional and IPFM does not affect professional's clinical decision making. The data obtained from IPFM online form and clinical data from the emergency department and TUH will be analysed after the data collection. The main aim of the research is to validate the use of IPFM by evaluating the association of IPFM output with 1) urgency and severity of the conditions; and 2) actual diagnoses diagnosed by medical doctors. The main hypotheses of the research are that 1) IPFM is safe and sensitive in evaluating the urgency of the conditions of arriving patients at the emergency department and that 2) IPFM has sufficient correlation of differential diagnosis with actual diagnosis made by medical doctor.
The present study is trying to find out whether artificial intelligence assisted follow-up strategy will improve secondary prevention in CABG patients. In addition, we will test whether rural patients may have more benefits under the new follow-up strategy based on the artificial intelligence device compared with urban patients.
Despite the progress made in the management of myocardial infarction (MI), the associated morbidity and mortality remains high. Numerous scientific data show that damage of the coronary microcirculation (CM) during a STEMI remains a problem because the techniques for measuring it are still imperfect. We have simple methods for estimating the damage to the MC during the initial coronary angiography, the best known being the calculation of the myocardial blush grade (MBG), but which is semi-quantitative and therefore not very precise, or more precise imaging techniques, such as cardiac MRI, which are performed 48 hours after the infarction and which make the development of early applicable therapeutics not very propitious. Finally, lately, the use of special coronary guides to measure a precise CM index remains non-optimal because it prolongs the procedure. However, the information is in the picture and this information could allow the development of therapeutic strategies adapted to the patient's CM. Indeed, the arrival of iodine in CM increases the density of the pixels of the image, this has been demonstrated by the implementation in 2009 of a software allowing the calculation of the MBG assisted by computer. But the performances of this software did not allow its wide diffusion. Today, the field of medical image analysis presents dazzling progress thanks to artificial intelligence (AI). Deep Learning, a sub-category of Machine Learning, is probably the most powerful form of AI for automated image analysis today. Made up of a network of artificial neurons, it allows, using a very large number of known examples, to extract the most relevant characteristics of the image to solve a given problem. Thus, it uses thousands of pieces of information, sometimes imperceptible to the naked eye. We hypothesize that a supervised Deep Learning algorithm trained with a set of relevant data, will be able to identify a patient with a pejorative prognosis, probably related to a microcirculatory impairment visible in the image.
Diminutive colorectal polyps (≤ 5 mm) represent most of the polyps detected during colonoscopy, especially in the rectum-sigmoid tract. The characterization of these polyps by virtual chromoendoscopy is recognized as a key element for innovative imaging techniques. As a matter of facts diminutive colorectal polyps are very frequent and, if located in the rectosigmoid colon, they present a very low malignant risk (0.3% of evolution towards advanced adenoma and up to 0.08% of evolution towards invasive carcinoma). The real-time characterization would allow to identify the lowest risk polyps (hyperplastic subtype), to leave them in situ or, if resected, not to send them for histological examination, allowing a huge saving in healthcare associated costs. Recently, the American Society for Gastrointestinal Endoscopy (ASGE) Technology Committee established the Preservation and Incorporation of Valuable endoscopic Innovations (PIVI) document, specific for real-time histological assessment for tiny colorectal polyps, to establish reference quality thresholds. Two performance standards have been developed to guide the use of advanced imaging: 1. for diminutive polyps to be resected and discarded without pathologic assessment, endoscopic technology (when used with high confidence) used to determine histology of polyps ≤ 5mm in size, when combined with the histopathology assessment of polyps > 5 mm in size, should provide a ≥ 90% agreement in assignment of post-polypectomy surveillance intervals when compared to decisions based on pathology assessment of all identified polyps; 2. in order for a technology to be used to guide the decision to leave suspected rectosigmoid hyperplastic polyps ≤ 5 mm in size in place (without resection), the technology should provide ≥ 90% negative predictive value (when used with high confidence) for adenomatous histology. Computer-Aided-Diagnosis (CAD) is an artificial intelligence-based tool that would allow rapid and objective characterization of these lesions. The GI Genius CADx was developed to help endoscopists in their clinical practices for polyps characterization.
Previous prospective randomized controlled study demonstrated higher accuracy rate of diagnosing early gastric cancers by Magnifying image-enhanced endoscopy than conventional white-light endoscopy. Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner. we developed a new computer-aided system to assist endoscopists in identifying early gastric cancers in magnifying optical enhancement images.
Cervical cancer, the fourth most common cancer globally and the fourth leading cause of cancer-related deaths, can be effectively prevented through early screening. Detecting precancerous cervical lesions and halting their progression in a timely manner is crucial. However, accurate screening platforms for early detection of cervical cancer are needed. Therefore, it is urgent to develop an Artificial Intelligence Cervical Cancer Screening (AICS) system for diagnosing cervical cytology grades and cancer.
Counting and classification of blood cells in a bone marrow smear and peripheral blood smear are essential to clinical hematology. To this date, this procedure has been carried out in a manual manner in the great majority of clinical settings. There is often inconsistency in the counting result between different operators largely due to its manual nature. There has not been an effective and standard method for blood smear preparation and automatic counting and classification. The recent advent of deep neural network for medical image processing introduced new opportunities for an effective solution of this long-standing problem. Numerous results have been published on the effectiveness of convolutional neural network in clinical image recognition task.
Nasopharyngeal carcinoma (NPC) occurs at a high frequency in southern China, northern Africa, and Alaska, with a reported incidence of 30 cases per 100 000 in Guangdong Province. Endoscopic examination and biopsy are the main methods used for detection and diagnosis of NPC. Early NPC patients achieve favourable prognoses after concurrent radiotherapy and chemotherapy in compassion with advanced NPC patients. Here, the investigators focused on the utility of artificial intelligence to detect early NPC, which based on white light imaging (WLI) and Narrow-band imaging (NBI) nasoendoscopic examination. Having access to this unique population provides an unprecedented opportunity to investigate the effect of intelligent system on diverse nasopharyngeal lesions detection and develop a novel Computer-Aided Diagnosis System.
This prospective, single-arm, multicenter, phase II trial enrolled 40 patients who underwent surgery after three cycles of neoadjuvant therapy with camrelizumab, nab-paclitaxel, and carboplatin. The MPR is the primary endpoint, and the pCR, the complete resection rate, the objective response rate, the disease-free survival, adverse events, and quality of life are the secondary endpoints. The exploratory endpoints will be used to establish a multiomics artificial intelligence system for neoadjuvant therapy effect prediction and decision-making assistance based on radiomics, metabolism, genetic, and clinic-pathological characteristics and to explore drug resistance mechanisms.