Tuberculosis

Pulmonary tuberculosis prediction using CAD4TB artificial intelligence (computer-aided detection for tuberculosis) based on thoracic x-ray photos among Indonesian subjects in hospital

Tuberculosis remains a major global health concern, particularly in high-burden countries where early detection is essential but often limited by insufficient radiological expertise. This study evaluated the diagnostic performance of a computer-aided detection system, CAD4TB, in interpreting chest X-ray images of suspected tuberculosis cases in a hospital setting in Indonesia. Using a retrospectiv...

Signal detection in the psychotic phenotype: Increased sensory precision and reduced decision threshold associated with psychotic-like experiences

Psychotic-like experiences may reflect disrupted signal detection, whereby individuals detect signals in noisy input that are unlikely to be present. Drawing on predictive coding accounts, we investigated whether increased sensory precision and reduced data-gathering relate to psychotic-like experiences in a signal detection task. We fitted drift-diffusion models to Random Dot Motion (RDM) task da...

Posterior Parietal Cortex Modulates Perceptual Decisions Depending On Psychotic Phenotype

Reduced data-gathering and altered sensory precision are associated with psychotic phenotypes in tasks engaging the posterior parietal cortex (PPC). W...

Predicting Tuberculosis Incidence in Adult HIV Patients on ART in Debre Markos, Ethiopia: A Machine Learning Approach

Tuberculosis (TB) is the commonest comorbidity among individuals with HIV/AIDS, especially in low- and middle-income nations such as Ethiopia. Early d...

Performance of Universal and Stratified Computer-Aided Detection Thresholds for Chest X-Ray-Based Tuberculosis Screening: A Cross-Sectional Diagnostic Accuracy Study

Computer-aided detection (CAD) software analyzes chest X-rays for features suggestive of tuberculosis (TB) and provides a numeric abnormality score. H...

Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment response

There is considerable interest in training machine learning (ML) models on genomic data that achieve clinical grade diagnostic accuracy. Many successf...

The Development and Evaluation of AI-based Tuberculosis Screening with a Digital Stethoscope used to Capture Lung Sounds. A Case-Control Study

Tuberculosis (TB) remains a leading global cause of preventable death, with 10.8 million cases and 1.3 million deaths reported in 2023. Current method...

Combining blood transcriptomic signatures improves the prediction of progression to tuberculosis among household contacts in Brazil

Tuberculosis remains a major health threat, infecting nearly a third of the world’s population. Of those infected, 5-10% progress from latent infectio...

Neural network-based identification of easily-obtainable demographic and clinical characteristics to identify people with tuberculosis

We consider the application of machine learning to the classification of tuberculosis (TB) based on clinical and demographic data. Such data is routin...

Quantum Dot Encoding for In-Solution Single-Molecule Biomarker Counting in Metastatic Prostate Cancer

Digital assays are in wide development for biomarker quantification at the single-molecule level, but the common use of surface-pulldown steps limits ...

Microscale Multiplexed Antigen-Specific Antibody Fc Profiling for Point-of-Care Diagnosis of Tuberculosis

Accurate, affordable tuberculosis (TB) diagnostics that do not require sputum samples are urgently needed for TB control and elimination. Prior serolo...

Multimodal deep learning model for prediction of prognosis in central nervous system inflammation.

Inflammatory diseases of the CNS impose a substantial disease burden, necessitating prompt and appropriate prognosis prediction. We developed a multim...

Recent progress in tuberculosis diagnosis: insights into blood-based biomarkers and emerging technologies.

Tuberculosis (TB) remains a global health challenge, with timely and accurate diagnosis being critical for effective disease management and control. R...

Graph attention networks based multi-agent path finding via temporal-spatial information aggregation.

An effective Multi-Agent Path Finding (MAPF) algorithm must efficiently plan paths for multiple agents while adhering to constraints, ensuring safe na...

Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model.

BACKGROUND: Tuberculous pleural effusion (TPE) is a prevalent tuberculosis complication, with diagnosis presenting considerable challenges. Timely and...

Convolutional neural network using magnetic resonance brain imaging to predict outcome from tuberculosis meningitis.

INTRODUCTION: Tuberculous meningitis (TBM) leads to high mortality, especially amongst individuals with HIV. Predicting the incidence of disease-relat...

Towards Real-Time 2D Mapping: Harnessing Drones, AI, and Computer Vision for Advanced Insights

This paper presents an advanced mapping system that combines drone imagery with machine learning and computer vision to overcome challenges in speed...

IMAGINE: An 8-to-1b 22nm FD-SOI Compute-In-Memory CNN Accelerator With an End-to-End Analog Charge-Based 0.15-8POPS/W Macro Featuring Distribution-Aware Data Reshaping

Charge-domain compute-in-memory (CIM) SRAMs have recently become an enticing compromise between computing efficiency and accuracy to process sub-8b ...

Unified Local and Global Attention Interaction Modeling for Vision Transformers

We present a novel method that extends the self-attention mechanism of a vision transformer (ViT) for more accurate object detection across diverse ...

Radiology Report Generation via Multi-objective Preference Optimization

Automatic Radiology Report Generation (RRG) is an important topic for alleviating the substantial workload of radiologists. Existing RRG approaches ...