scholarly journals Predicting of patient’s enrollment to multicentral international clinical trials of II–III phase

2021 ◽  
Vol 8 (1) ◽  
pp. 73
Author(s):  
Svyatoslav S. Milovanov
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Retrospective Review ◽  
Final Report ◽  
Study Success ◽  
Stage Design ◽  
Multicenter Trials ◽  
Embedded Value ◽  
International Multicenter

<p class="abstract">Many international multicenter trials ending by fail due to fail to recruitment of patients. Objective was to find the integrated and simple value for the assessing the possibility of recruitment in particular sites on the feasibility stage. Design consisted of retrospective review of the selected parameters since feasibility stage to final report of studies conducting in a few sites since 2008 to 2017 year. It was also selected empirical range of the sites. Collection of data was done from the private experience of authors. The integrated and simple values for the assessment of the possibility of recruitment in particular sites on the feasibility stage were calculated. Recruitment of patient as an integrated part of site’s work could be calculated as the beginning of running study on particular site which can lead to increasing of study success and could be used as an embedded value in artificial intelligence for both virtual and real studies. We titled this “calculated type of site on enrollment (CTSE)”.</p>

scholarly journals The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

2021 ◽  
Author(s):  
Francesco Piccialli ◽  
Vincenzo Schiano di Cola ◽  
Fabio Giampaolo ◽  
Salvatore Cuomo
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Digital Transformation ◽  
Daily Activities ◽  
Commercial Vehicles ◽  
Human Skills ◽  
To Come ◽  
Complex Scenario ◽  
The Impact

AbstractThe first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

scholarly journals Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data

2021 ◽  
Vol 14 ◽  
pp. 263177452199062
Author(s):  
Benjamin Gutierrez Becker ◽  
Filippo Arcadu ◽  
Andreas Thalhammer ◽  
Citlalli Gamez Serna ◽  
Owen Feehan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ulcerative Colitis ◽  
Clinical Trials ◽  
Deep Learning ◽  
Mayo Clinic ◽  
Automated System ◽  
Phase Iii ◽  
Plain Language ◽  
Data Set ◽  
Endoscopic Videos

Introduction: The Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading. Methods: Here we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics the assessment done in practice by a gastroenterologist, that is, traversing the whole colonoscopy video, identifying visually informative regions and computing an overall Mayo Clinic Endoscopic Subscore. The proposed deep learning–based system has been trained and deployed on raw colonoscopies using Mayo Clinic Endoscopic Subscore ground truth provided only at the colon section level, without manually selecting frames driving the severity scoring of ulcerative colitis. Results and Conclusion: Our evaluation on 1672 endoscopic videos obtained from a multisite data set obtained from the etrolizumab Phase II Eucalyptus and Phase III Hickory and Laurel clinical trials, show that our proposed methodology can grade endoscopic videos with a high degree of accuracy and robustness (Area Under the Receiver Operating Characteristic Curve = 0.84 for Mayo Clinic Endoscopic Subscore ⩾ 1, 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 2 and 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 3) and reduced amounts of manual annotation. Plain language summary Patient, caregiver and provider thoughts on educational materials about prescribing and medication safety Artificial intelligence can be used to automatically assess full endoscopic videos and estimate the severity of ulcerative colitis. In this work, we present an artificial intelligence algorithm for the automatic grading of ulcerative colitis in full endoscopic videos. Our artificial intelligence models were trained and evaluated on a large and diverse set of colonoscopy videos obtained from concluded clinical trials. We demonstrate not only that artificial intelligence is able to accurately grade full endoscopic videos, but also that using diverse data sets obtained from multiple sites is critical to train robust AI models that could potentially be deployed on real-world data.

Clinical Trials and Machine Learning: Regulatory Approach Review

2021 ◽  
Vol 16 ◽  
Author(s):  
Diego Alejandro Dri ◽  
Maurizio Massella ◽  
Donatella Gramaglia ◽  
Carlotta Marianecci ◽  
Sandra Petraglia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Trials ◽  
Patient Safety ◽  
Drug Discovery ◽  
Clinical Applications ◽  
Clinical Development ◽  
Regulatory Approach ◽  
National Competent Authorities ◽  
Review Current

: Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has significantly contributed to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has constantly been growing. Moreover, it is now also impacting National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed, or generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient safety, health protection, and fostering immediate access to effective treatments.

scholarly journals Artificial intelligence in clinical research

2016 ◽  
Vol 3 (4) ◽  
pp. 187 ◽  
Author(s):  
Veerabhadra Sanekal Nayak ◽  
Mohammed Saleem Khan ◽  
Bharat Kumar Shukla ◽  
Pranjal R. Chaturvedi
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Survival Rate ◽  
Drug Development ◽  
Clinical Research ◽  
Business Sector ◽  
Planning Phase ◽  
Pancreatic Malignancy ◽  
Present Technique ◽  
Critical Interest

<p>Envision dedicating fifteen years to a critical interest and emptying staggering amount of funds into it, at the same time confronting a disappointment rate of 95 percent. That is the crippling reality for pharmaceutical organizations, which toss billions of dollars consistently toward medications that possible won't work – and after that do a reversal to the planning phase and do it once more. Today's medications go to the business sector after an extensive, very costly process of drug development. It takes anywhere in the range of 10 to 15 years, here and there significantly more, to convey a medication from introductory revelation to the hands of patients – and that voyage can cost billions up to 12 billion, to be correct. That is just a lot to spend, and excessively yearn for patients to hold up. Patients can hardly wait 15 years for a lifesaving drug, we require another productive focused on medication revelation and improvement process. Artificial Intelligence, can significantly reduce the time included, and also cut the expenses by more than half. This is made conceivable through a totally distinctive way to deal with medication revelation. With the present technique, for each 100 medications that achieve first stage clinical trials, only one goes ahead to wind up a genuine treatment. That is stand out percent, it's an unsustainable model, particularly when there are ailments, for example, pancreatic malignancy which has a normal five-year survival rate of 6%.</p>

A Two-Stage Design for Choosing among Several Experimental Treatments and a Control in Clinical Trials

Biometrics ◽  
1989 ◽  
Vol 45 (2) ◽  
pp. 537 ◽  
Author(s):  
Peter F. Thall ◽  
Richard Simon ◽  
Susan S. Ellenberg
Keyword(s):  
Clinical Trials ◽  
Two Stage ◽  
Stage Design ◽  
Two Stage Design ◽  
Experimental Treatments
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