AIMC Topic: Neural Networks, Computer

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Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models.

Scientific reports
Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration and cognitive decline. Early diagnosis is challenging due to the complexity of sporadic ALS and the lack of a defined risk population. In this study, we...

Detection of human activities using multi-layer convolutional neural network.

Scientific reports
Human Activity Recognition (HAR) plays a critical role in fields such as healthcare, sports, and human-computer interaction. However, achieving high accuracy and robustness remains a challenge, particularly when dealing with noisy sensor data from ac...

Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture.

Clinical interventions in aging
BACKGROUND: Hip fractures have become a significant health concern, particularly among super-aged patients, who were at a high risk of postoperative pneumonia due to their frailty and the presence of multiple comorbidities. This study aims to establi...

A deep learning-based psi CT network effectively predicts early recurrence after hepatectomy in HCC patients.

Abdominal radiology (New York)
BACKGROUND: Hepatocellular carcinoma (HCC) exhibits a high recurrence rate, and early recurrence significantly jeopardizes patient prognosis, necessitating reliable methods for early recurrence prediction.

Multi-axis transformer based U-Net with class balanced ensemble model for lung disease classification using X-ray images.

Journal of X-ray science and technology
Chest X-rays are an essential diagnostic tool for identifying chest disorders because of its high sensitivity in detecting pathological anomalies in the lungs. Classification models based on conventional Convolutional Neural Networks (CNNs) are adve...

A novel interpretability framework for enzyme turnover number prediction boosted by pre-trained enzyme embeddings and adaptive gate network.

Methods (San Diego, Calif.)
It is a vital step to identify the enzyme turnover number (kcat) in synthetic biology and early-stage drug discovery. Recently, deep learning methods have achieved inspiring process to predict kcat with the development of multi-species enzyme-substra...

Accurate Estimation of Methemoglobin and Oxygen Saturation in Skin Tissue Using Diffuse Reflectance Spectroscopy and Artificial Intelligence.

Journal of biophotonics
In this paper, we present a noninvasive method for the accurate estimation of methemoglobin concentration. The proposed technique incorporates a novel machine learning model using the artificial neural network to detect methemoglobin and oxygen satur...

Comparative analysis of machine learning and deep learning algorithms for knee arthritis detection using YOLOv8 models.

Journal of X-ray science and technology
Knee arthritis is a prevalent joint condition that affects many people worldwide. Early detection and appropriate treatment are essential to slow the disease's progression and enhance patients' quality of life. In this study, various machine learning...

AI/ML modeling to enhance the capability of in vitro and in vivo tests in predicting human carcinogenicity.

Mutation research. Genetic toxicology and environmental mutagenesis
This study aimed to develop an in silico model for predicting human carcinogenicity using advanced deep learning techniques, specifically Graph Neural Networks (GNN), through a multitask learning (MTL) approach. The MTL framework leveraged auxiliary ...

GLASSR-Net: Glass Substrate Spectral Restoration Neural Network for Fourier Transform Infrared Microspectroscopy in the Fingerprint Region.

Analytical chemistry
Fourier transform infrared (FTIR) microspectroscopy has emerged as a pivotal pathological tool, offering informative spectral biomarkers for numerous diseases. However, the dependency on specialized infrared (IR) substrates limits effective and wides...