Lymphoma

From fixed points to optimum regions: AI-NSGA-II framework for high-recovery, low-energy brackish water RO.

Escalating global freshwater scarcity demands more energy-efficient and sustainable brackish water reverse osmosis (BWRO) desalination. This study demonstrates how integrating high-fidelity Artificial Neural Network (ANN) surrogates with a robust Non-dominated Sorting Genetic Algorithm II (NSGA-II) can deliver reliable multi-objective optimization for pilot-scale BWRO systems. Unlike conventional ...

Medication-based mortality prediction in COPD using machine learning and conventional statistical methods.

BACKGROUND: Predicting mortality in chronic obstructive pulmonary disease (COPD) patients supports clinical decision-making and resource allocation. While most existing prediction models rely on clinical, physiological, imaging, or biological measures which are not frequently collected in clinical practice, drug claims data may be electronically accessible during routine visits. METHODS: We conduc...

Subgrouping non-specific low back pain based on spinal marker trajectory data: An unsupervised machine learning approach.

BACKGROUND: Non-specific low back pain (LBP) is a heterogeneous condition. Therefore, it is important to investigate whether clinically feasible asses...

NHOC/NHOP as novel biomarkers for predicting lymph node metastasis in NSCLC using PET/CT radiomics and machine learning: a two-center retrospective study.

PURPOSE: NHOC and NHOP, defined as the normalized distances from peak uptake to tumour centroid and perimeter, are novel PET/CT metrics of tumour aggr...

Biased minds, sensitive hearts: Divergent neural signatures of interpersonal sensitivity in atypical and non-atypical depression.

BACKGROUND: Atypical depression (AD) is a distinct subtype of depression, with interpersonal sensitivity as one of its core characteristics. However, ...

Investigating the potential of combining Raman spectroscopy and machine learning for gestational age classification in rat cervical tissue.

Spontaneous preterm birth (SPB) is a leading cause of neonatal morbidity and mortality worldwide. It occurs when the uterine cervix (UC) opens prematu...

Real-world diagnostic performance of knee MRI protocols accelerated using simultaneous multi-slice acquisition and deep learning reconstruction.

OBJECTIVE: To assess whether accelerated knee MRI protocols using simultaneous multi-slice (SMS) and deep learning reconstruction (DLR) are non-inferi...

Utility of multimodal deep learning model to diagnose lymph node metastasis in esophageal cancer using computed tomography and positron emission tomography images.

PURPOSE: This study aimed to assess the performance of a deep learning model using multimodal imaging for detecting lymph node metastasis in esophagea...

DiffMixer: A prediction model based on mixing different frequency features.

Time series forecasting is widely applied in fields such as energy and network security. Various prediction models based on Transformer and MLP archit...

Co-mapping clonal and transcriptional heterogeneity in somatic evolution via GoT-Multi.

Somatic evolution leads to clonal heterogeneity, which fuels cancer progression and therapy resistance. To decipher the consequences of clonal heterog...

Detection of Lymph Node Metastasis in Thyroid Cancer Using Deep Learning and Second Harmonic Generation Imaging.

Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, with a significant proportion of patients being susceptible to lymph n...

Rapid methanol detection in ethanol solutions via non-linear Raman spectroscopy modeling.

Methanol contamination in ethanol-based products poses a significant health risk due to its toxicity at low concentrations. This study developed a rap...

Brain metabolic imaging with 18 F-PET-CT and machine-learning clustering analysis reveal divergent metabolic phenotypes in patients with amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by significant clinicopathologic heterogeneity. Th...

Investigation of cervical cell image segmentation technology based on deep learning and non-coding RNAs.

BACKGROUND: Cervical cancer remains a significant health concern worldwide, necessitating effective diagnostic methods such as cervical cell image seg...

Radiomics-based artificial intelligence (AI) models in colorectal cancer (CRC) diagnosis, metastasis detection, prognosis, and treatment response prediction.

Colorectal cancer (CRC) is the third most common cause of cancer-related morbidity and mortality in the world. Radiomics and radiogenomics are utilize...

Multitask radioclinical decision stratification in non-metastatic colon cancer: integrating MMR status, pT staging, and high-risk pathological factors.

OBJECTIVES: Constructing a multi-task global decision support system based on preoperative enhanced CT features to predict the mismatch repair (MMR) s...

Application of Convolutional Neural Network Image Analysis and Machine Learning to Basic Blood Tests for Intelligent Diagnostic Assistance.

BACKGROUND AND OBJECTIVES: We developed an automated morphological image recognition deep learning system (image recognition DLS) of peripheral blood ...

A unified gradient regularization method for heterogeneous graph neural networks.

Heterogeneous Graph Neural Networks (HGNNs) are advanced deep learning methods widely applied for learning representations of heterogeneous graphs. Ho...

Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [18F]FDG PET/CT study.

PURPOSE: Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unrese...

A Pilot Study on Quantitative Accuracy and Radiomic Feature Stability of Deep Progressive Learning Reconstruction in NSCLC PET.

Deep progressive learning reconstruction (DPR) is a novel deep learning-based algorithm for PET imaging, yet its impact on quantitative metrics and ra...