AIMC Topic: Deep Learning

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Deep learning radiomics of left atrial appendage features for predicting atrial fibrillation recurrence.

BMC medical imaging
BACKGROUND: Structural remodeling of the left atrial appendage (LAA) is characteristic of atrial fibrillation (AF), and LAA morphology impacts radiofrequency catheter ablation (RFCA) outcomes. In this study, we aimed to develop and validate a predict...

Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery.

Scientific reports
This study aims to predict the optimal imaging parameters using a deep learning algorithm in 3D heads-up vitreoretinal surgery and assess its effectiveness on improving the vitreoretinal surface visibility during surgery. To develop the deep learning...

Intelligent traffic congestion forecasting using BiLSTM and adaptive secretary bird optimizer for sustainable urban transportation.

Scientific reports
Traffic congestion forecasting is one of the major elements of the Intelligent Transportation Systems (ITS). Traffic congestion in urban road networks significantly influences sustainability by increasing air pollution levels. Efficient congestion ma...

Smart neural network and cognitive computing process for multi task nuclei detection segmentation and classification in breast cancer histopathology images.

Scientific reports
The detection, segmentation, and differentiation of benign and malignant nuclei from the histopathology images is a challenging task for the early diagnosis of breast cancer. Misinterpretation of True Negative (TN) and False Positive (FP) can generat...

Evaluation of spatial visual perception of streets based on deep learning and spatial syntax.

Scientific reports
Street visual quality improvement plays an important role in urban development. An important direction for street quality research lies in accurately perceiving the spatial quality of urban streets and exploring the connection with street constituent...

Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.

European journal of medical research
The rapid advancement of Machine Learning (ML) and Deep Learning (DL) technologies has revolutionized healthcare, particularly in the domains of disease prediction and diagnosis. This study provides a comprehensive review of ML and DL applications ac...

Predicting the Brain-To-Plasma Unbound Partition Coefficient of Compounds via Formula-Guided Network.

Journal of chemical information and modeling
Blood-brain barrier (BBB) permeability plays a crucial role in determining drug efficacy in the brain, with the brain-to-plasma unbound partition coefficient () recognized as a key parameter of BBB permeability in drug development. However, data are...

Toward Accurate PAH IR Spectra Prediction: Handling Charge Effects with Classical and Deep Learning Models.

Journal of chemical information and modeling
Polycyclic aromatic hydrocarbons (PAHs) play a crucial role in astrochemistry, environmental studies, and combustion chemistry, yet interpreting their infrared (IR) spectra remains challenging due to the similarity of spectral features of many molecu...

An advanced three stage lightweight model for underwater human detection.

Scientific reports
This study presents StarEye, a lightweight deep learning model designed for underwater human body detection (UHBD) that addresses the challenges of complex underwater environments. The proposed model incorporates several innovative components: a comp...

Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach.

Scientific reports
Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strategies using normal saline, Ringer's lactate, and albumin, and to determine which patient factors are...