AIMC Topic: Algorithms

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Machine Learning Algorithms for Predicting Fatty Liver Disease.

Annals of nutrition & metabolism
BACKGROUND: Fatty liver disease (FLD) has become a rampant condition. It is associated with a high rate of morbidity and mortality in a population. The condition is commonly referred as FLD. Early prediction of FLD would allow patients to take necess...

Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography.

The British journal of ophthalmology
AIMS: To validate a deep learning (DL) algorithm (DLA) for 360° angle assessment on swept-source optical coherence tomography (SS-OCT) (CASIA SS-1000, Tomey Corporation, Nagoya, Japan).

Knowledge graphs and their applications in drug discovery.

Expert opinion on drug discovery
INTRODUCTION: Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems b...

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Molecular diversity
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design a...

Risks of feature leakage and sample size dependencies in deep feature extraction for breast mass classification.

Medical physics
PURPOSE: Transfer learning is commonly used in deep learning for medical imaging to alleviate the problem of limited available data. In this work, we studied the risk of feature leakage and its dependence on sample size when using pretrained deep con...

Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data.

The Lancet. Digital health
BACKGROUND: Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular events, infection, and graft failure. Few clinical tools are available to identify patients at risk ...

Application of network link prediction in drug discovery.

BMC bioinformatics
BACKGROUND: Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the f...

Combining Progressive Rethinking and Collaborative Learning: A Deep Framework for In-Loop Filtering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, we aim to address issues of (1) joint spatial-temporal modeling and (2) side information injection for deep-learning based in-loop filter. For (1), we design a deep network with both progressive rethinking and collaborative learning me...

Time-aware deep neural networks for needle tip localization in 2D ultrasound.

International journal of computer assisted radiology and surgery
PURPOSE: Accurate placement of the needle is critical in interventions like biopsies and regional anesthesia, during which incorrect needle insertion can lead to procedure failure and complications. Therefore, ultrasound guidance is widely used to im...

Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review.

International journal of medical informatics
BACKGROUND AND OBJECTIVES: Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients at risk of developing infection and subsequent sepsi...