AIMC Topic: Algorithms

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Volume-based complete automation for ultrasound fetal biometry: A pilot approach to assess feasibility, reliability, and perspectives.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
BACKGROUND: Detection algorithms targeting anatomic landmarks in three-dimensional (3D) ultrasound (US) volume (three-dimensional US) appear to be a relevant and easy-to-implement option to address junior and occasional operators' difficulties in pro...

Improvement of near-infrared spectroscopic assessment methods for the quality of Keemun black tea: Utilizing transfer learning.

Food research international (Ottawa, Ont.)
Keemun black tea, a renowned Chinese black tea, presents challenges in quality assessment due to variability in data across different years. To address this, we developed transfer learning algorithms using near-infrared spectral data. The qualitative...

Utilising Natural Language Processing to Identify Brain Tumor Patients for Clinical Trials: Development and Initial Evaluation.

World neurosurgery
BACKGROUND: Identifying patients eligible for clinical trials through eligibility screening is time and resource-intensive. Natural Language Processing (NLP) models may enhance clinical trial screening by extracting data from Electronic Health Record...

Asymmetric Convolution-based GAN Framework for Low-Dose CT Image Denoising.

Computers in biology and medicine
Noise reduction is essential to improve the diagnostic quality of low-dose CT (LDCT) images. In this regard, data-driven denoising methods based on generative adversarial networks (GAN) have shown promising results. However, custom designs with 2D co...

Deep learning-based segmentation of ultra-low-dose CT images using an optimized nnU-Net model.

La Radiologia medica
PURPOSE: Low-dose CT protocols are widely used for emergency imaging, follow-ups, and attenuation correction in hybrid PET/CT and SPECT/CT imaging. However, low-dose CT images often suffer from reduced quality depending on acquisition and patient att...

AI-driven framework to map the brain metabolome in three dimensions.

Nature metabolism
High-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the dissection of the complex metabolic landscape and heterogeneity from a thin tissue section. Currentl...

Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms.

European journal of medical research
OBJECTIVES: This study aimed to identify risk factors for diabetic retinopathy (DR) and develop machine learning (ML)-based predictive models using routine laboratory data in patients with type 2 diabetes mellitus (T2DM).

Utilizing machine learning algorithms for predicting Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI).

BMC psychiatry
BACKGROUND: Accurately diagnosing Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI) shows significant challenges as traditional diagnostic methods fail to meet expectations due to patient hesitance and non-psychiatric h...

CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

BMC genomics
BACKGROUND: Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regulatory motifs, and substantial computational efforts have been invested to find motifs. In recent yea...

A novel deep sequential learning architecture for drug drug interaction prediction using DDINet.

Scientific reports
Drug drug Interactions (DDI) present considerable challenges in healthcare, often resulting in adverse effects or decreased therapeutic efficacy. This article proposes a novel deep sequential learning architecture called DDINet to predict and classif...