scholarly journals S543 Validation of Novel Mobile App Using Artificial Intelligence to Capture and Objectively Characterize Stool Images in a Clinical Trial

2021 ◽  
Vol 116 (1) ◽  
pp. S247-S247
Author(s):  
Mark Pimentel ◽  
Ali Rezaie ◽  
Ruchi Mathur ◽  
Jiajing Wang ◽  
Asaf Kraus ◽  
...  
2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
R Avram ◽  
D So ◽  
E Iturriaga ◽  
J Byrne ◽  
R.J Lennon ◽  
...  

Abstract Background/Introduction TAILOR-PCI is the largest cardiovascular genotype-based randomized trial (NCT#01742117) investigating whether genotype-guided selection of oral P2Y12 inhibitor therapy improves ischemic outcomes after percutaneous coronary intervention (PCI). The TAILOR-PCI Digital Sub-Study tests the feasibility of extending original follow-up of 1 year to 2 years using state-of-the-art digital solutions. Deep phenotyping acquired during a clinical trial can be leveraged by extending follow-up in an efficient and cost-effective manner using digital technology. Purpose Our objective is to describe onboarding and engagement of participants initially recruited in a large, pragmatic, international, multi-center clinical trial to a digital registry. Methods TAILOR-PCI participants, within 23 months of their index PCI, were invited by letters containing a URL to the Digital Sub-Study website (http://tailorpci.eurekaplatform.org). These invitations were followed by phone calls, if no response to the letter, to determine reason for non-participation. A NIH-funded direct-to-participant digital research platform (the Eureka Research Platform) was used to onboard, consent and enroll participants for the digital follow-up. Participants were asked to answer health-related surveys at fixed intervals using the Eureka mobile app and desktop platform. To capture hospitalizations, participants could enable geofencing to allow background location tracking, which triggered surveys if a hospitalization was detected. Result(s) Letters were mailed to 893 of 929 eligible participants across 22 sites in the United States and Canada leading to 226 homepage visits and 118 registrations. There were 107 consents (12.0% of invited; mean age: 66.4±9.0; 19 females [18%]): 47 (44%) participants consented after the letter, 36 (34%) consented after the 1st call and 24 (22%) consented after a 2nd call. Among those who consented, 100 were eligible (7 did not have a smartphone) 81 downloaded the study mobile app and 73 agreed for geofencing (Figure 1). Among the 722 invited participants who were surveyed, 354 declined participation: due to lack of time (146; 20.2%), lack of smartphone (125; 17.3%), difficulty understanding (41; 5.7%), concern about using smartphone (34; 4.7%), concern of data privacy (14; 1.9%), concerns of location tracking (6; 0.8%) and other reasons (57; 7.9%). Conclusion Extended follow-up of a clinical trial using a digital platform is feasible but uptake in this study population was limited largely due to lack of time or a smartphone among participants. Based on data from other digital studies, uptake may also have been limited since digital follow-up consent was not incorporated at the time of consent for the main trial. Figure 1. Onboarding of the digital substudy Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Institute of Health (NIH), National Heart, Lung, and Blood Institute (NHLBI)


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tiancheng Xu ◽  
Youbing Xia

Acupuncture is gaining increasing attention and recognition all over the world. However, a lot of physical labor is paid by acupuncturists. It is natural to resort to a robot which can improve the accuracy as well as the efficacy of therapy. Several teams have separately developed real acupuncture robots or related technologies and even went to the stage of clinical trial and then achieved success commercially. A completed clinical practical acupuncture robot is not far from reach with the combination of existing mature medical robotic technologies. A hand-eye-brain coordination framework is proposed in this review to integrate the potential utilizing technologies including force feedback, binocular vision, and automatic prescription. We should take acupuncture prescription with artificial intelligence and future development trends into account and make a feasible choice in development of modern acupuncture.


2018 ◽  
Vol 40 (3) ◽  
pp. 623-629 ◽  
Author(s):  
W. Reid Thompson ◽  
Andreas J. Reinisch ◽  
Michael J. Unterberger ◽  
Andreas J. Schriefl

2020 ◽  
Vol 2 (10) ◽  
pp. e537-e548 ◽  
Author(s):  
Xiaoxuan Liu ◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
Melanie J Calvert ◽  
Alastair K Denniston ◽  
...  

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