Lymphoma

Differentiating septic arthritis from non-infectious inflammatory causes of acute monoarticular arthritis in children: A machine learning approach based on routine laboratory tests.

OBJECTIVE: Acute monoarthritis in children poses a diagnostic challenge, particularly in distinguishing septic arthritis from non-infectious inflammatory causes. Delayed or incorrect diagnosis may lead to serious complications or inappropriate treatment. This study aims to develop and validate machine learning (ML) models for distinguishing septic arthritis from non-infectious inflammatory arthrit...

Development and multicenter validation of an AI-driven detection system for DLBCL cells in bone marrow smears.

The presence of diffuse large B-cell lymphoma (DLBCL) cells in bone marrow (BM) smears is recognised as a key morphological basis for diagnosing BM involvement by DLBCL. BM involvement directly affects disease staging, treatment strategies, and prognosis assessment. Nevertheless, traditional manual identification of DLBCL cells in BM smears under a microscope is time-consuming, subject to observer...

Incremental prognostic value of AI-based coronary CT angiography combined with PET/CT dynamic myocardial perfusion imaging in patients with suspected or confirmed coronary artery disease.

BACKGROUND: Coronary computed tomography angiography (CCTA) and positron emission tomography/computed tomography (PET/CT) myocardial perfusion imaging...

BASG-Net: a background-aware spatial gating network for cervical cell segmentation in pap smear images.

The precise segmentation of cervical cell images is regarded as a fundamental prerequisite for the implementation of computer-aided cervical cancer sc...

Prognostic survival models for diffuse large B-cell lymphoma using statistical and machine learning approaches.

Although diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, existing prognostic tools, including the revised International Prognostic I...

Automated quantification of drug-induced cell death via vacuolation scoring in microscopic images.

Cell death plays a critical role in maintaining cellular homeostasis. The accurate evaluation of cell death is essential to determine the efficacy of ...

Masked attribution-based probing of strategies as a computational framework to align human, non-human primate, and model explanations.

What visual information do primate brains use to recognize objects, and can explanations from artificial neural networks (ANNs) help reveal these biol...

Evaluation of deep learning-based reconstruction models on non-TOF BGO PET/CT: impact of acquisition times and BSREM penalization factors on lesion detectability and SNR.

BACKGROUND: New long field-of-view (FOV) PET scanners using bismuth germanate (BGO) detectors without time-of-flight (TOF) capability are now availabl...

FUO-PETMamba: a pet maximum-intensity-projection-based artificial intelligence framework for aetiological classification of fever of unknown origin with multicentre validation.

BACKGROUND: Fever of unknown origin (FUO) remains diagnostically challenging because of heterogeneous causes, non-specific clinical manifestations, an...

A novel model of perceived sustainable work performance through the active bystander behavior, team cohesion, and innovative work behavior: A multi-analytical approach.

The study examines the relationships among active bystander behavior (ABSB), innovative work behavior (IWB), team cohesion (TC), and sustainable work ...

Deep learning-based malignancy probability estimation of pulmonary nodules in PET/CT imaging.

OBJECTIVE: The current BTS guidelines recommend evaluation of suspicious pulmonary nodules using [18F]FDG-PET/CT imaging, followed by Herder model ris...

A Multimodal Multitask Deep Learning Model Based on Ultrasound RF Signals for Joint Assessment of Breast Masses and Axillary Lymph Nodes Status.

PURPOSE: Developing a deep learning model to simultaneously evaluate lymph node status and distinguish between benign and malignant breast masses has ...

Metal-organic frameworks (MOFs) for nonlinear optical properties: design principles, DFT insights, and future directions for photonic applications.

Metal-organic frameworks (MOFs) have emerged as a uniquely versatile platform for nonlinear optical (NLO) applications, combining the large hyperpolar...

Development and validation of a novel multimodal deep neural network model based on CBC digit parameters and scattergrams for rapid hematolymphoid malignancy classification: a multicenter cohort study.

BACKGROUND: Rapid screening of hematolymphoid malignancies (HMs) by complete blood cell count (CBC) is crucial for choosing the next appropriate worku...

Multi-regional radiomics based on planning CT and information complementarity between GTVp and GTVn: an explainable model for predicting 5-year recurrence risk in HPV-positive oropharyngeal cancer.

PURPOSE: This study aims to develop and validate a multi-regional radiomics machine learning model integrating the spatial heterogeneity features of b...

Spiky Magnetic Titania Particles for Integrated Exosome Capture and Metabolic Profiling Toward Cancer Diagnosis.

Exosomal metabolite profiling represents a promising non-invasive approach for cancer diagnosis. However, its widespread application has been constrai...

Automated body composition quantification from non-contrast CT for urolithiasis classification and exploratory incident risk assessment.

To develop a deep learning-based body composition quantification framework from non-contrast CT for urolithiasis classification (calcium, non-calcium,...

Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis.

BACKGROUND: The evaluation of genetic mutations is crucial for personalized therapy in colorectal cancer (CRC), but the invasive tissue biopsy is subj...

Large language models enable prognostic stratification of cancer patients using real-world clinical notes.

In medical documentation, vast amounts of unstructured text are generated that are still underutilized in current prognostic models. We investigate th...

Automatic Sleep Staging Using Cardiorespiratory Signals: A Systematic Review of Methodologies and Performance.

Cardiorespiratory-based methods offer promising alternatives to traditional PSG for longitudinal sleep monitoring, holding significant systemic medica...