AIMC Topic: Algorithms

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Cost-effectiveness of novel diagnostic tools for idiopathic pulmonary fibrosis in the United States.

BMC health services research
OBJECTIVES: Novel non-invasive machine learning algorithms may improve accuracy and reduce the need for biopsy when diagnosing idiopathic pulmonary fibrosis (IPF). We conducted a cost-effectiveness analysis of diagnostic strategies for IPF.

Artificial Neural Network - Multi-Objective Genetic Algorithm based optimization for the enhanced pigment accumulation in Synechocystis sp. PCC 6803.

BMC biotechnology
BACKGROUND: Natural colorants produced by the cyanobacterium include carotenoids, chlorophyll a and phycocyanin. The current study used the Synechocystis sp. PCC 6803 to examine how abiotic stress conditions, such as low temperature as well as high l...

Bioactivity of Juglans regia kernel extracts optimized using response surface method and artificial neural Network-Genetic algorithm integration.

Scientific reports
In this study, the biological activities of the extracts obtained under optimum extraction conditions of the kernel part of Juglans regia L. were determined. Two different methods, Response Surface Method (RSM) and Artificial Neural Network-Genetic A...

Effectiveness of machine learning methods in detecting grooming: a systematic meta-analytic review.

Scientific reports
This study presents a systematic review (SR) and meta-analysis (MA) on the use of machine learning (ML) methods for detecting online grooming, a form of manipulation and child sexual abuse. The SR identified 33 studies from IEEE, Web of Science, Scop...

Machine learning-based prediction models for renal impairment in Chinese adults with hyperuricaemia: risk factor analysis.

Scientific reports
In hyperuricaemic populations, multiple factors may contribute to impaired renal function. This study aimed to establish a machine learning-based model to identify characteristic factors related to renal impairment in hyperuricaemic patients, determi...

HADCN: a hierarchical ascending densely connected network for enhanced medical image segmentation.

Medical & biological engineering & computing
Medical image segmentation is a key component in computer-aided diagnostic technology. In the past few years, the U-shaped architecture-based hierarchical model has become the mainstream approach, which however often fails to provide accurate results...

Quantitative multislice and jointly optimized rapid CEST for in vivo whole-brain imaging.

Magnetic resonance in medicine
PURPOSE: To develop a quantitative multislice chemical exchange saturation transfer (CEST) schedule optimization and pulse sequence that reduces the loss of sensitivity inherent to multislice sequences.

Liver lesion segmentation in ultrasound: A benchmark and a baseline network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate liver lesion segmentation in ultrasound is a challenging task due to high speckle noise, ambiguous lesion boundaries, and inhomogeneous intensity distribution inside the lesion regions. This work first collected and annotated a dataset for l...

A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI.

Medical image analysis
Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysical models is a challenging inverse problem, due to factors such as va...

A novel data-driven approach for Personas validation in healthcare using self-supervised machine learning.

Journal of biomedical informatics
OBJECTIVE: Persona validation is a challenging task, often relying on costly external validation methods. The aim of this study was the development of a novel method for Personas validation based on data already available during their creation.