Lymphoma

Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System Without Electroencephalography.

STUDY OBJECTIVES: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography (ECG), triaxial accelerometry, and chest temperature, and finger photoplethysmography and finger temperature. METHODS: We obtained wearable sensor recordings from 357 ad...

Non-inferiority of automated deep learning-based [18F]FDG PET/CT tumour volume compared to manual GTV for prognostic modelling in head and neck cancer.

Label-free serum SERS combined with RFE-GBDT algorithm for non-invasive screening of liver cancer.

Early diagnosis of liver cancer is crucial for developing clinical treatment strategies and improving patient survival rates. However, current diagnos...

Ann-assisted UV imaging for non-destructive dissolution prediction of HPMC matrix tablets.

This study investigates the use of UV imaging combined with artificial neural networks (ANNs) to estimate the dissolution behaviour of extended-releas...

Auto-LSN: fully automated liver surface nodularity quantification in CT based on deep learning for the evaluation of advanced chronic liver disease.

OBJECTIVES: Liver surface nodularity (LSN) is a recognized non-invasive biomarker of cirrhosis. This study introduces auto-LSN, an artificial intellig...

Quantification of PET activation in adipose tissue from non-contrast CT scans.

Brown adipose tissue (BAT) plays a key role in energy metabolism and cardiometabolic health. Its detection typically relies on 18F-FDG PET, which is c...

MYB: A potential therapeutic target in triple-negative breast cancer based on the PI3K/AKT signaling pathway.

BACKGROUND: Compared to non-triple-negative breast cancer (Non-TNBC), triple-negative breast cancer (TNBC) exhibits significantly poorer prognosis. Pr...

Identification of Micrometastasis in Cervical Lymph Nodes - A Machine Learning-Based Approach.

INTRODUCTION AND AIM: Oral squamous cell carcinoma (OSCC) rates have been on the rise globally due to a lack of health care facilities, unaffordable t...

[Innovative imaging techniques for urothelial carcinoma].

BACKGROUND: Imaging guides the staging of urothelial carcinoma and thus plays a central role in treatment decisions. Current guidelines reflect the in...

Clinical-radiological machine learning model for non-invasive diagnosis and stratification of peripheral artery disease: a multicenter study.

BACKGROUND AND OBJECTIVE: Peripheral artery disease (PAD) is an atherosclerotic disorder prevalent in the elderly that leads to peripheral function de...

Incremental prognostic value of immune cell densities beyond clinical parameters in non-small cell lung cancer.

BACKGROUND: Multiplex immunofluorescence imaging enables detailed characterization of the tumor immune microenvironment, but whether immune cell densi...

Advances in cartilage imaging techniques.

Articular cartilage is crucial for joint function; however, it has limited regenerative capacity when damaged, a hallmark of many rheumatic diseases. ...

Parametric imaging of dynamic long-axial-field-of-view PET scans: Technical challenges, statistical insights and clinical applications.

Dynamic long-axial-field-of-view (LAFOV) PET imaging offers unprecedented opportunities for quantitative assessment of tracer kinetics across the enti...

Genetic Information Enhances a Novel Non-Invasive Steatosis Index and Outperforms Existing Steatosis Indices.

BACKGROUND AND AIMS: Early detection of individuals with metabolic dysfunction-associated steatotic liver disease (MASLD) is important as intervention...

Scanner-integrated reconstruction versus post-processing deep learning for low-count 18F-FDG PET/CT: a comparative clinical evaluation.

OBJECTIVES: To compare two deep learning (DL) approaches for low-count PET/CT: deep progressive reconstruction (DPR), a scanner-integrated reconstruct...

Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trial.

BACKGROUND: Evidence indicates that artificial intelligence (AI) can improve mammography screening by increasing cancer detection and reducing screen ...

Predicting nonresponse to sexual identity question in youth risk behavior surveillance: A machine learning analysis of complex survey data.

PURPOSE: To compare seven machine learning (ML) models developed to predict non-response to the sexual identity question in the 2023 Youth Risk Behavi...

Padé Neurons for Efficient Neural Models.

Neural networks commonly employ the McCulloch-Pitts neuron model, which is a linear model followed by a point-wise non-linear activation. Various rese...

Multimodal deep learning for laryngeal squamous cell carcinoma staging using CT and laryngoscopy.

OBJECTIVES: To develop and validate a multimodal deep learning model integrating clinical data, contrast-enhanced CT, and laryngoscopic images for dif...

Predicting transcranial ultrasound insertion loss using skull CT: A deep learning approach.

Transcranial ultrasound (tUS) is a non-invasive neuromodulation technique with applications in brain disorders. However, ultrasound attenuation induce...