RH Logo
Hubs
About
Live
Leaderboard

Most Reputable Users

Author Profile Avatar
Patrick Joyce
5708
Author Profile Avatar
Kranti Rumalla
933
Author Profile Avatar
Kayla Burris
718
Author Profile Avatar
Colin Moser
1061
Author Profile Avatar
Ian Hilgart
234
Author Profile Avatar
Arye Lipman
336
Author Profile Avatar
Emeka A. Ezewudo
2673
Author Profile Avatar
Brian Armstrong
756
Author Profile Avatar
anuj mishra
355
Author Profile Avatar
Ida Rolf
238

Trending Papers in medicine

Trending
Today
239
Published: Nov 2020
Published: Nov 2020
  • The results indicate that mothers fed on a low protein diet during gestation and lactation produce male offspring with normal sperm morphology, concentration and motility but exhibiting an overall decrease of fertility when they reach adulthood.
  • This study used a mouse model to investigate the effects of maternal undernutrition on fertility in male progeny
Author Profile Avatar
Kayla Burris
Slide 1 of 1
  • Paper Preview Page 1
212
Authors: Tim Joda, Tuomas Waltimo, Nicole Probst-Hensch, Christiane Pauli-Magnus, Nicola U. Zitzmann
Published: Jan 2019
Authors: Tim Joda, Tuomas Waltimo, Nicole Probst-Hensch, Christiane Pauli-Magnus, Nicola U. Zitzmann
Published: Jan 2019
  • The interoperability of Health Data with accessible digital health technologies is the key to deliver value-based dental care and exploit the tremendous potential of AI.
Author Profile Avatar
Emeka A. Ezewudo
210
Authors: Tseng, Huan-Hsin, et al
Published: Jan 2020
Authors: Tseng, Huan-Hsin, et al
Published: Jan 2020
Author Profile Avatar
Emeka A. Ezewudo
Author Profile Avatar
Emeka A. Ezewudo
2
From Paper: AYUSH medicine as add-on therapy for mild category COVID-19; an open label randomised, controlled clinical trial
Authors: Rao, Anusha, et al
Published: Dec 2020
From Paper: AYUSH medicine as add-on therapy for mild category COVID-19; an open label randomised, controlled clinical trial
Authors: Rao, Anusha, et al
Published: Dec 2020
Author Profile Avatar
kolawole solomon
Author Profile Avatar
kolawole solomon
7
Authors: Dipti Itchhaporia
Published: Nov 2020
Authors: Dipti Itchhaporia
Published: Nov 2020
This review examines the current state and application of artificial intelligence (AI) and machine learning (ML) in cardiovascular medicine. AI is changing the clinical practice of medicine in other specialties. With progress continuing in this emerging technology, the impact for cardiovascular medicine is highlighted to provide insight for the practicing clinician and to identify potential patient benefits.
Author Profile Avatar
ALYSSA ENG
6
Authors: Wei Tang, Kenneth Jian, et al
Published: Dec 2020
Authors: Wei Tang, Kenneth Jian, et al
Published: Dec 2020
The advent of Artificial Intelligence (AI) has resulted in development of novel applications in a multitude of fields, such as in Medicine, to aid medical professionals in clinical diagnosis. Specifically, the field of Emergency Medicine has been of immense interest to researchers, with vast untapped potential for AI solutions to improve operational efficiencies and quality of healthcare. Aside from primary healthcare facilities, the Emergency Department serves as the first line of contact to patients, who often present with varying and undifferentiated symptoms. Several challenges faced by clinicians and patients alike, such as waiting times and diagnostic dilemmas, present opportunities for application of AI solutions. In this paper, we aim to summarise the applications of AI in the field of Emergency Medicine by reviewing recent developments in Emergency Department operations and in the clinical management of patients.
Author Profile Avatar
ALYSSA ENG
6
Authors: Ram D. Sriram, S. Sethu K. Reddy
Published: Aug 2020
Authors: Ram D. Sriram, S. Sethu K. Reddy
Published: Aug 2020
Author Profile Avatar
ALYSSA ENG
5
Authors: Jo, Yong-Yeon, et al
Published: Dec 2020
Authors: Jo, Yong-Yeon, et al
Published: Dec 2020
IntroductionEarly detection and intervention of atrial fibrillation (AF) is a cornerstone for effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have been developed, but they could not be applied in clinical practice owing to their lack of interpretability. We developed an explainable DLM to detect AF using ECG and validated its performance using diverse formats of ECG.MethodsWe conducted a retrospective study. The Sejong ECG dataset comprising 128,399 ECGs was used to develop and internally validated the explainable DLM. DLM was developed with two feature modules, which could describe the reason for DLM decisions. DLM was external validated using data from 21,837, 10,605, and 8528 ECGs from PTB-XL, Chapman, and PhysioNet non-restricted datasets, respectively. The predictor variables were digitally stored ECGs, and the endpoints were AFs.ResultsDuring internal and external validation of the DLM, the area under the receiver operating characteristic curves (AUCs) of the DLM using a 12‑lead ECG in detecting AF were 0.997–0.999. The AUCs of the DLM with VAE using a 6‑lead and single‑lead ECG were 0.990–0.999. The AUCs of explainability about features such as rhythm irregularity and absence of P-wave were 0.961–0.993 and 0.983–0.993, respectively.ConclusionsOur DLM successfully detected AF using diverse ECGs and described the reason for this decision. The results indicated that an explainable artificial intelligence methodology could be adopted to the DLM using ECG and enhance the transparency of the DLM for its application in clinical practice.
Author Profile Avatar
ALYSSA ENG
5
Authors: Sharib Gaffar, Addison S. Gearhart, Anthony C. Chang
Published: Oct 2020
Authors: Sharib Gaffar, Addison S. Gearhart, Anthony C. Chang
Published: Oct 2020
Author Profile Avatar
ALYSSA ENG
Load More Papers