Radiology

Robust Multi-contrast MRI Medical Image Translation via Knowledge Distillation and Adversarial Attack.

Medical image translation is of great value but is very difficult due to the requirement with style change of noise pattern and anatomy invariance of image content. Various deep learning methods like the mainstream GAN, Transformer and Diffusion models have been developed to learn the multi-modal mapping to obtain the translated images, but the results from the generator are still far from being p...

Generative artificial intelligence for fundus fluorescein angiography interpretation and human expert evaluation.

Fundus fluorescein angiography (FFA) is the gold standard for diagnosing chorioretinal diseases, but its interpretation requires significant expertise and time. Despite generative AI's enormous potential in medical report generation, automatic FFA interpretation lacks robust models and sufficient evaluation metrics. This study introduces InterpreFFA, a diagnosis-supervised contrastive learning fra...

Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.

To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracranial hypertension (IH) following primary decompressi...

Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy.

Sudden cardiac death from ventricular arrhythmias is a leading cause of mortality worldwide. Arrhythmic death prognostication is challenging in patien...

Clinical value of the 70-kVp ultra-low-dose CT pulmonary angiography with deep learning image reconstruction.

OBJECTIVE: This study aims to assess the feasibility of "double-low," low radiation dosage and low contrast media dosage, CT pulmonary angiography (CT...

A deep learning model for early diagnosis of alzheimer's disease combined with 3D CNN and video Swin transformer.

Alzheimer's disease (AD) constitutes a neurodegenerative disorder predominantly observed in the geriatric population. If AD can be diagnosed early, bo...

Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis.

BACKGROUND: Endobronchial ultrasound (EBUS) is a widely used imaging modality for evaluating thoracic lymph nodes (LNs), particularly in the staging o...

Automated grading of rectocele with an MRI radiomics model.

To develop an automated grading model for rectocele (RC) based on radiomics and evaluate its efficacy. This study retrospectively analyzed a total of ...

Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging.

Early diagnosis of Neurological Disorders (ND) such as Alzheimer's disease (AD) and Brain Tumors (BT) can be highly challenging since these diseases c...

Classifying and diagnosing Alzheimer's disease with deep learning using 6735 brain MRI images.

Traditional diagnostic methods for Alzheimer's disease often suffer from low accuracy and lengthy processing times, delaying crucial interventions and...

Multi channel fusion diffusion models for brain tumor MRI data augmentation.

The early diagnosis of brain tumors is crucial for patient prognosis, and medical imaging techniques such as MRI and CT scans are essential tools for ...

A novel neuroimaging based early detection framework for alzheimer disease using deep learning.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health chall...

Optimizing a hybrid process of electrolysis ultrasound and persulfate for remediation of petroleum contaminated soils using AI models.

Research uses both RANSAC Regressor and Monte Carlo Optimization to improve the performance of electrolysis/ultrasound/persulfate system which detoxif...

Combining multi-parametric MRI radiomics features with tumor abnormal protein to construct a machine learning-based predictive model for prostate cancer.

This study aims to investigate the diagnostic value of integrating multi-parametric magnetic resonance imaging (mpMRI) radiomic features with tumor ab...

Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.

The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, using data from Bama miniature pigs and patients in ...

Differential Phase Contrast Imaging to Predict MSC Immune Function.

Mesenchymal stromal cells (MSCs) exhibit significant immunomodulatory potential, making them promising candidates for cell-based therapies in autoimmu...

MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF).

This work aims to promote early and accurate diagnosis of Temporal Lobe Epilepsy (TLE) by developing state-of-the-art deep learning techniques, with t...

Treatment Response Evaluation in Prostate Cancer Using PSMA PET/CT.

In recent years, there has been a headlong rush into the use of prostate-specific membrane antigen (PSMA)-targeted PET for the staging and restaging o...

Hip surveillance in children with cerebral palsy in the UK : history, challenges, and future directions.

Cerebral palsy (CP) is associated with musculoskeletal complications in children, notably hip migration, which can progress to hip dislocation and joi...

Diagnostic Performance of ChatGPT-4o and DeepSeek-3 Differential Diagnosis of Complex Oral Lesions: A Multimodal Imaging and Case Difficulty Analysis.

BACKGROUND: AI models like ChatGPT-4o and DeepSeek-3 show diagnostic promise, but their reliability in complex, image-based oral lesions remains uncle...