AIMC Topic: Humans

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MMPD-DTA: Integrating Multi-Modal Deep Learning with Pocket-Drug Graphs for Drug-Target Binding Affinity Prediction.

Journal of chemical information and modeling
Predicting drug-target binding affinity (DTA) is a crucial task in drug discovery research. Recent studies have demonstrated that pocket features and interactions between targets and drugs significantly improve the understanding of DTA. However, chal...

Research on predicting radiographic exposure time in imaging based on neural network prediction models.

Clinical neurology and neurosurgery
OBJECTIVE: To explore the anatomical and clinical factors that affect the radiographic exposure time in radial artery cerebral angiography and to establish a model.

Artificial intelligence in peptide-based drug design.

Drug discovery today
Protein-protein interactions (PPIs) are fundamental to a variety of biological processes, but targeting them with small molecules is challenging because of their large and complex interaction interfaces. However, peptides have emerged as highly promi...

Under-representation for Female Pelvis Cancers in Commercial Auto-segmentation Solutions and Open-source Imaging Datasets.

Clinical oncology (Royal College of Radiologists (Great Britain))
AIM: Artificial intelligence (AI) based auto-segmentation aids radiation therapy (RT) workflows and is being adopted in clinical environments facilitated by the increased availability of commercial solutions for organs at risk (OARs). In addition, op...

Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study.

JMIR medical informatics
BACKGROUND: Postpartum depression (PPD) is a prevalent mental health issue with significant impacts on mothers and families. Exploring reliable predictors is crucial for the early and accurate prediction of PPD, which remains challenging.

Responsible Design, Integration, and Use of Generative AI in Mental Health.

JMIR mental health
Generative artificial intelligence (GenAI) shows potential for personalized care, psychoeducation, and even crisis prediction in mental health, yet responsible use requires ethical consideration and deliberation and perhaps even governance. This is t...

An Explainable CNN and Vision Transformer-Based Approach for Real-Time Food Recognition.

Nutrients
BACKGROUND: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical fea...

Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images.

BMJ open ophthalmology
PURPOSE: To develop an artificial intelligence algorithm to automatically identify the anterior segment structures and assess multiple parameters of primary angle closure disease (PACD) in ultrasound biomicroscopy (UBM) images.

Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage.

Critical care (London, England)
BACKGROUND: Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation challenges. Early prediction of functional outcomes is essential for optimizing treatment strategie...

Towards a decision support system for post bariatric hypoglycaemia: development of forecasting algorithms in unrestricted daily-life conditions.

BMC medical informatics and decision making
BACKGROUND: Post bariatric hypoglycaemic (PBH) is a late complication of weight loss surgery, characterised by critically low blood glucose levels following meal-induced glycaemic excursions. The disabling consequences of PBH underline the need for t...