AIMC Topic: Adult

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Enhancing meningioma tumor classification accuracy through multi-task learning approach and image analysis of MRI images.

PloS one
BACKGROUND: Accurate classification of meningioma brain tumors is crucial for determining the appropriate treatment plan and improving patient outcomes. However, this task is challenging due to the slow-growing nature of these tumors and the potentia...

Identifying melanoma among benign simulators - Is there a role for deep learning convolutional neural networks? (MelSim Study).

European journal of cancer (Oxford, England : 1990)
IMPORTANCE: Early detection of cutaneous melanoma (CM) is crucial for patient survival, yet avoiding overdiagnosis remains essential. Differentiating CM from benign melanoma simulators (MelSim) is challenging due to overlapping features. Deep learnin...

High-resolution mapping of alcohol-related brain connectivity in adults using 7T fMRI and multivoxel pattern classification.

Psychiatry research. Neuroimaging
BACKGROUND: Emerging evidence suggests that alcohol use disrupts large-scale brain network interactions, particularly within the triple network model-comprising the Salience Network (SN), Default Mode Network (DMN), and Frontoparietal Network (FPN). ...

Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study.

Scientific reports
Cervical cancer is a leading cause of death from malignant tumors in women, and accurate evaluation of occult lymph node metastasis (OLNM) is crucial for optimal treatment. This study aimed to develop several predictive models-including Clinical mode...

Deep Learning-aided H-MR Spectroscopy for Differentiating between Patients with and without Hepatocellular Carcinoma.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Among patients with hepatitis B virus-associated liver cirrhosis (HBV-LC), there may be differences in the hepatic parenchyma between those with and without hepatocellular carcinoma (HCC). Proton MR spectroscopy (H-MRS) is a well-established...

FGDN: A Federated Graph Convolutional Network framework for multi-site major depression disorder diagnosis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The vast amount of healthcare data is characterized by its diversity, dynamic nature, and large scale. It is a challenge that directly training a Graph Convolutional Neural Network (GCN) in a multi-site dataset poses to protecting the privacy of Majo...

Human-alignment influences the utility of AI-assisted decision making.

Scientific reports
Whenever an AI model is used to predict a relevant (binary) outcome in AI-assisted decision making, it is widely agreed that, together with each prediction, the model should provide an AI confidence value. However, it has been unclear why decision ma...

Serum peptide biomarkers by MALDI-TOF MS coupled with machine learning for diagnosis and classification of hepato-pancreato-biliary cancers.

Scientific reports
This study aimed to investigate the potential of peptide mass fingerprints (PMFs) of the serum peptidome using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), in combination with machine learning algorithm...

Unveiling social determinants of health impact on adverse pregnancy outcomes through natural language processing.

Scientific reports
Understanding the role of Social Determinants of Health (SDoH) in pregnancy outcomes is critical for improving maternal and infant health yet extracting SDoH from unstructured electronic health records remains challenging. We trained and evaluated na...

Exploring the feasibility of AI-based analysis of histopathological variability in salivary gland tumours.

Scientific reports
This study uses artificial intelligence (AI) for differentiation between salivary gland tumours (SGT) using digitised Haematoxylin and Eosin stained whole-slide images (WSI). Machine learning (ML) classifiers were developed and tested using 320 scann...