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Image Interpretation, Computer-Assisted

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Deep Learning and Habitat Radiomics for the Prediction of Glioma Pathology Using Multiparametric MRI: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive ability remains suboptimal due to the tumor intrinsic heterogeneity. We aimed to achieve better pat...

Accelerating FLAIR imaging via deep learning reconstruction: potential for evaluating white matter hyperintensities.

Japanese journal of radiology
PURPOSE: To evaluate deep learning-reconstructed (DLR)-fluid-attenuated inversion recovery (FLAIR) images generated from undersampled data, compare them with fully sampled and rapidly acquired FLAIR images, and assess their potential for white matter...

Reliability of brain volume measures of accelerated 3D T1-weighted images with deep learning-based reconstruction.

Neuroradiology
PURPOSE: The time-intensive nature of acquiring 3D T1-weighted MRI and analyzing brain volumetry limits quantitative evaluation of brain atrophy. We explore the feasibility and reliability of deep learning-based accelerated MRI scans for brain volume...

Comparative evaluation of interpretation methods in surface-based age prediction for neonates.

NeuroImage
Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the op...

AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images.

European radiology
OBJECTIVES: The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axia...

Exploring Deep Learning Applications using Ultrasound Single View Cines in Acute Gallbladder Pathologies: Preliminary Results.

Academic radiology
RATIONALE AND OBJECTIVES: In this preliminary study, we aimed to develop a deep learning model using ultrasound single view cines that distinguishes between imaging of normal gallbladder, non-urgent cholelithiasis, and acute calculous cholecystitis r...

Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis.

Breast cancer research : BCR
Breast cancer is the most common malignant tumor among women worldwide and remains one of the leading causes of death among women. Its incidence and mortality rates are continuously rising. In recent years, with the rapid advancement of deep learning...

Semi-supervised recognition for artificial intelligence assisted pathology image diagnosis.

Scientific reports
The analysis and interpretation of cytopathological images are crucial in modern medical diagnostics. However, manually locating and identifying relevant cells from the vast amount of image data can be a daunting task. This challenge is particularly ...

Retrosynthetic analysis via deep learning to improve pilomatricoma diagnoses.

Computers in biology and medicine
BACKGROUND: Pilomatricoma, a benign childhood skin tumor, presents diagnostic challenges due to its manifestation variations and requires surgical excision upon histological confirmation of its characteristic cellular features. Recent artificial inte...

Automated diagnosis of atherosclerosis using multi-layer ensemble models and bio-inspired optimization in intravascular ultrasound imaging.

Medical & biological engineering & computing
Atherosclerosis causes heart disease by forming plaques in arterial walls. IVUS imaging provides a high-resolution cross-sectional view of coronary arteries and plaque morphology. Healthcare professionals diagnose and quantify atherosclerosis physica...