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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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Effect of AI-assisted caries annotation on dental students' performance in caries detection on panoramic radiographs

Dental caries remains one of the most prevalent oral diseases globally. The integration of artificial intelligence (AI) into dental radiographic interpretation, particularly for caries detection, has expanded rapidly. AI-assisted caries annotation may help dental students identify carious lesions on panoramic radiographs-a commonly used diagnostic tool for evaluating teeth and surrounding structures-thereby improving diagnostic accuracy, efficiency, and confidence. This study aimed to assess the effect of AI-ass…

BMC Medical Education · Health

Effect of AI-assisted caries annotation on dental students' performance in caries detection on panoramic radiographs

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AI-Assisted cardiomegaly screening via implicit morphological inference and human-in-the-loop validation

Cardiomegaly screening via manual Cardiothoracic Ratio (CTR) measurement remains a clinical bottleneck, while contemporary deep learning solutions often suffer from algorithmic bloating. To address the need for resource-efficient and interpretable triage, this study proposes a framework driven by implicit morphological inference, which bypasses the requirement for explicit heart segmentation. We developed UBNet-Seg, a lightweight U-Net variant (2.3 million parameters) trained on a heterogeneous dataset of 11,748…

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Artificial intelligence for diagnosis of keratoconus using Scheimpflug based corneal tomography

Aim To develop and evaluate the diagnostic accuracy of deep learning (DL) models in differentiating keratoconus (KC) from normal eyes with regular astigmatism. Methods A comparative cross-sectional study was conducted at the Cornea and Diagnostic Department of Al-Shifa Trust Eye Hospital, Pakistan. Galilei dual Scheimpflug-based corneal topography was performed to obtain four corneal maps: anterior axial curvature, posterior axial curvature, corneal thickness, and posterior elevation. Four convolutional neural n…

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Advancements in machine learning and deep learning for early detection and management of mental health disorder

For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) have started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to improve clinical results significantly. However, they also present unique challenges relating to data integration and ethical issues. The development of ML and DL methods for the early diagnosis and treatment…

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A deep learning framework for efficient pathology image analysis

Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images. However, current methods are computationally inefficient, processing thousands of redundant tiles per slide and requiring complex aggregation models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that emulates pathologists by selectively analyzing informative regions. EAGLE combines task-agnostic tile selection with detailed feature…

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Phenotyping antidepressant treatment response with deep learning in electronic health records

ABSTRACT Efficient, accurate phenotyping for antidepressant treatment response in electronic health records (EHRs) could facilitate precision psychiatry applications but remains a challenge. Increasingly, artificial intelligence methods using “deep learning” applied to clinical data have shown promise in complex classification problems. Here, we systematically evaluate the performance of eight deep-learning-based natural language processing models in classifying response to antidepressants in a large real-world…

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Large language models are powerful electronic health record encoders

Electronic health records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity challenge traditional machine learning. Domain-specific electronic health record foundation models trained on unlabeled EHR data have shown improved predictive accuracy and generalization. However, their development is constrained by limited data access and site-specific vocabularies. We convert EHR data into plain text by replacing medical codes with natural-language descriptions, enabli…

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A survey on computational pathology foundation models: datasets, adaptation strategies, and evaluation tasks

Abstract Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, su…

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A survey on computational pathology foundation models: datasets, adaptation strategies, and evaluation tasks

Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy

Nasopharyngeal carcinoma (NPC) is rare in Europe, and emerging data suggest poorer outcomes in Caucasian patients compared with Asian populations, highlighting the need for region-specific prognostic tools. Inflammation-based biomarkers and artificial intelligence show promise for risk stratification and prediction of survival and second primary cancers (SPCs). We conducted a retrospective multicentre study including 405 NPC patients from six European institutions. Demographic, clinicopathological, and haematolo…

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Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy

Predictive value of the uric acid to high-density cholesterol ratio (UHR) combined with intact parathyroid hormone for protein-energy wasting after incident hemodialysis: a multicenter study

Protein-energy wasting (PEW) is common in incident hemodialysis patients and linked to poor outcomes. The uric acid/HDL-cholesterol ratio (UHR) and intact parathyroid hormone (iPTH) relate to metabolic, inflammatory, and nutritional disturbances, but their value for predicting PEW in incident hemodialysis is unclear. This retrospective multicenter study included 863 incident hemodialysis patients. PEW was defined according to the International Society of Renal Nutrition and Metabolism criteria. UHR and iPTH were…

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Beyond BMI: Deep Learning Segmentation-Driven CT Reveals Body Composition Changes after Metabolic and Bariatric Surgery

Background BMI is the primary metric used to evaluate outcomes of metabolic and bariatric surgery (MBS), but it does not distinguish tissue compartments or quantify visceral adiposity (VAT), a key determinant of cardiometabolic risk. We evaluated the relationship between BMI and VAT and characterized compartment-specific remodeling after MBS using artificial intelligence-enabled CT segmentation. Study design A retrospective analysis of prospectively collected abdominal CT scans was performed at a single tertiary…

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Medical student reliance on artificial intelligence in nephrology education

Background Large language models such as ChatGPT are increasingly used in medical education and may influence student learning and decision-making. Despite strong performance on factual recall, limitations in clinical reasoning raise concerns about how learners engage with artificial intelligence (AI)-generated recommendations, particularly in challenging domains such as renal physiology, where foundational understanding underpins clinical application. Methods Fifty-seven first-year medical students completed 24…

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Medical student reliance on artificial intelligence in nephrology education

Randomized, Double-Blind, Placebo-Controlled First-in-Human Trial of a First-in-Class AI-Designed Monoclonal Antibody (GB-0669) Against the Conserved SARS-CoV-2 Spike S2 Stem Helix

Background Antibodies against the SARS-CoV-2 spike receptor-binding domain provided effective COVID-19 treatment until resistant variants emerged. GB-0669 is a half-life-extended monoclonal antibody optimized using artificial intelligence. It targets the conserved spike S2 stem helix, a region subject to limited selective pressure from antibody responses induced by natural infection or vaccination. Methods Pre-clinical safety studies were conducted in cynomolgus monkeys. In the first-in-human trial, healthy adul…

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Randomized, Double-Blind, Placebo-Controlled First-in-Human Trial of a First-in-Class AI-Designed Monoclonal Antibody (GB-0669) Against the Conserved SARS-CoV-2 Spike S2 Stem Helix

Comprehensive analysis of neutrophil extracellular traps-associated inflammatory genes for patients with idiopathic pulmonary fibrosis

Neutrophil extracellular traps (NETs) facilitate inflammation and epithelial-mesenchymal transition (EMT), promoting the progression of pulmonary fibrosis. Various machine learning methods were used to screen for prognostic genes. Based on prognostic genes, a risk model was constructed to assess their ability for prognosis prediction of idiopathic pulmonary fibrosis (IPF). Mendelian Randomization (MR) analysis evaluated causal associations between IPF and prognostic genes, while GSE122960 examined cell-type-spec…

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Comprehensive analysis of neutrophil extracellular traps-associated inflammatory genes for patients with idiopathic pulmonary fibrosis

Urinary Metabolic Age from High-Resolution NMR Reveals Longitudinal Aging Patterns

Biological age captures inter-individual heterogeneity in aging process arising from genetic and environmental influences. Metabolites, as the end-products of metabolism, integrate these factors and are therefore well suited for biological age estimation. Urinary metabolomics, in particular, provides a non-invasive and information-rich matrix for assessing systemic metabolic states. We applied different machine learning techniques to develop a biological age score from high-resolution 1H nuclear magnetic resonan…

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