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A sparse and wide neural network model for DNA sequences.
Neural Netw
December 2024
Norwegian University of Science and Technology, Trondheim, Norway; Jinhua Institute of Zhejiang University, Hangzhou, China.
Accurate modeling of DNA sequences requires capturing distant semantic relationships between the nucleotide acid bases. Most existing deep neural network models face two challenges: (1) they are limited to short DNA fragments and cannot capture long-range interactions, and (2) they require many supervised labels, which is often expensive in practice. We propose a new neural network model called SwanDNA to address the above challenges.
View Article and Find Full Text PDFA clinical pilot trial of an artificial intelligence-driven smart phone application of bowel preparation for colonoscopy: a randomized clinical trial.
Scand J Gastroenterol
December 2024
Department of Gastroenterology, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China.
Background: High-quality bowel preparation is paramount for a successful colonoscopy. This study aimed to explore the effect of artificial intelligence-driven smartphone software on the quality of bowel preparation.
Methods: Firstly, we utilized 3305 valid liquid dung images collected mobile phones as training data.
Patient and practitioner perceptions around use of artificial intelligence within the English NHS diabetic eye screening programme.
Diabetes Res Clin Pract
December 2024
Population Health Research Institute, St George's School of Health and Medical Sciences, City St George's, University of London, London, UK.
Aims: Automated retinal image analysis using Artificial Intelligence (AI) can detect diabetic retinopathy as accurately as human graders, but it is not yet licensed in the NHS Diabetic Eye Screening Programme (DESP) in England. This study aims to assess perceptions of People Living with Diabetes (PLD) and Healthcare Practitioners (HCP) towards AI's introduction in DESP.
Methods: Two online surveys were co-developed with PLD and HCP from a diverse DESP in North East London.
Diagnostic Accuracy of Ambient Mass Spectrometry with Blood Plasma in a Murine Glioma Model Using Machine Learning.
World Neurosurg
December 2024
Department of Neurosurgery, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Chuo, Yamanashi, Japan.
Objective: Malignant glioma progresses rapidly and shows poor prognosis, but clinically applicable blood plasma-based biochemical tumor markers remain lacking. This study aimed to develop a diagnostic system using probe electrospray ionization mass spectrometry (PESI-MS) and a machine-learning logistic regression model to detect plasma changes at various time points in a murine glioma model.
Methods: We used a syngeneic intracranial orthotopic murine model with GL261 glioma cells.
Development and validation of a multiplex chip-based droplet digital PCR method for detecting CNVs in 7q11.2 and 22q11.2 regions.
Clin Chim Acta
December 2024
Beijing Key Laboratory for Molecular Diagnostics of Cardiovascular Diseases, Center of Laboratory Medicine, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China; State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Copy number variations (CNVs) in the 7q11.2 and 22q11.2 chromosomal regions are major contributors to genetic disorders such as Williams-Beuren syndrome and 22q11.
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