AIMC Topic: Machine Learning

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Determination of minimum inhibitory concentrations using machine-learning-assisted agar dilution.

Microbiology spectrum
UNLABELLED: Effective policy to address the global threat of antimicrobial resistance requires robust antimicrobial susceptibility data. Traditional methods for measuring minimum inhibitory concentration (MIC) are resource intensive, subject to human...

Exploring the NRF2-TP53 Signaling Network Through Machine Learning and Pan-Cancer Analysis: Identifying Potential targets for Cancer Prognosis Related to Oxidative Stress.

Advanced biology
Oxidative stress (OXS) is closely related to tumor prognosis and immune response, while TP53 integrated with NRF2 is closely associated with the regulation of cancer-related OXS. Hence, constructing a TP53-NRF2 integrated OXS signature of pan-cancer ...

Encrypted Image Classification with Low Memory Footprint Using Fully Homomorphic Encryption.

International journal of neural systems
Classifying images has become a straightforward and accessible task, thanks to the advent of Deep Neural Networks. Nevertheless, not much attention is given to the privacy concerns associated with sensitive data contained in images. In this study, we...

A machine learning stacking model accurately estimating gastric fluid volume in patients undergoing elective sedated gastrointestinal endoscopy.

Postgraduate medicine
BACKGROUND: The current point-of-care ultrasound (POCUS) assessment of gastric fluid volume primarily relies on the traditional linear approach, which often suffers from moderate accuracy. This study aimed to develop an advanced machine learning (ML)...

Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform.

Scientific reports
Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it is complicated, time-consuming, and subjective. Computer-ai...

Enhanced skin burn assessment through transfer learning: a novel framework for human tissue analysis.

Journal of medical engineering & technology
Visual inspection is the typical way for evaluating burns, due to the rising occurrence of burns globally, visual inspection may not be sufficient to detect skin burns because the severity of burns can vary and some burns may not be immediately appar...

Comparative performance analysis of Boruta, SHAP, and Borutashap for disease diagnosis: A study with multiple machine learning algorithms.

Network (Bristol, England)
Interpretable machine learning models are instrumental in disease diagnosis and clinical decision-making, shedding light on relevant features. Notably, Boruta, SHAP (SHapley Additive exPlanations), and BorutaShap were employed for feature selection, ...

Early prediction of ventricular peritoneal shunt dependency in aneurysmal subarachnoid haemorrhage patients by recurrent neural network-based machine learning using routine intensive care unit data.

Journal of clinical monitoring and computing
Aneurysmal subarachnoid haemorrhage (aSAH) can lead to complications such as acute hydrocephalic congestion. Treatment of this acute condition often includes establishing an external ventricular drainage (EVD). However, chronic hydrocephalus develops...

Development of Machine Learning Algorithm to Predict the Risk of Incontinence After Robot-Assisted Radical Prostatectomy.

Journal of endourology
Predicting postoperative incontinence beforehand is crucial for intensified and personalized rehabilitation after robot-assisted radical prostatectomy. Although nomograms exist, their retrospective limitations highlight artificial intelligence (AI)'...