AIMC Topic: Neural Networks, Computer

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Predictive efficacy of machine-learning algorithms on intrahepatic cholestasis of pregnancy based on clinical and laboratory indicators.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
OBJECTIVES: Intrahepatic cholestasis of pregnancy (ICP), a condition exclusive to pregnancy, necessitates prompt identification and intervention to improve the perinatal outcomes. This study aims to develop suitable machine-learning models for predic...

Automatic identification of clinically important species by artificial intelligence-based image recognition: proof-of-concept study.

Emerging microbes & infections
While morphological examination is the most widely used for identification in clinical laboratories, PCR-sequencing and MALDI-TOF MS are emerging technologies in more financially-competent laboratories. However, mycological expertise, molecular biol...

Enhancing differentiation between unipolar and bipolar depression through integration of machine learning and electroencephalogram analysis.

Journal of affective disorders
To enhance the differentiation between unipolar depression (UPD) and bipolar depression (BPD), this study integrates machine learning and deep learning models with electroencephalography (EEG) data and clinical features. Utilizing Python for data pre...

Breast cancer early detection and molecular subtype prediction by combination of Raman spectroscopy with deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Breast cancer is one of the most common tumors in women, and early screening can significantly reduce mortality rates. Meanwhile, accurately identifying HER2-positive and HER2-negative subtypes of breast cancer is critical for helping doctors determi...

Detection of common bile duct dilatation on magnetic resonance cholangiopancreatography by deep learning.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: This study aims to detect common bile duct (CBD) dilatation using deep learning methods from artificial intelligence algorithms.

ReSCU-Nets: Recurrent U-Nets for segmentation of three-dimensional microscopy data.

The Journal of cell biology
Segmenting multidimensional microscopy data requires high accuracy across many images (e.g., time points or Z slices) and is thus a labor-intensive part of biological image processing pipelines. We present ReSCU-Nets, recurrent convolutional neural n...

Analysis of adaptive systems based on Driver's workload.

Applied ergonomics
This study examined workload classification models and their application in adaptive in-vehicle systems. A meta-analysis of 31 studies assessed how predictor types (e.g., physiological data), experimental settings (simulator vs. on-road), and device ...

Metasurface-enhanced terahertz imaging for glioblastoma in orthotopic xenograft mouse model combined with neural network decision making.

Biosensors & bioelectronics
Terahertz (THz) optical sensing and imaging offer significant potential in a range of biological and medical applications owing to their low-energy, non-ionizing nature, and ultra-broadband spectral information, which includes numerous molecular fing...

A Salient Object Detection Network Enhanced by Nonlinear Spiking Neural Systems and Transformer.

International journal of neural systems
Although a variety of deep learning-based methods have been introduced for Salient Object Detection (SOD) to RGB and Depth (RGB-D) images, existing approaches still encounter challenges, including inadequate cross-modal feature fusion, significant er...

Training a deep learning model to predict the anatomy irradiated in fluoroscopic x-ray images.

International journal of computer assisted radiology and surgery
PURPOSE: Accurate patient dosimetry estimates from fluoroscopically-guided interventions (FGIs) are hindered by limited knowledge of the specific anatomy that was irradiated. Current methods use data reported by the equipment to estimate the patient ...