AIMC Topic: Algorithms
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Artificial intelligence-based detection of dens invaginatus in panoramic radiographs.
BMC oral health
Jun 5, 2025
OBJECTIVE: The aim of this study was to automatically detect teeth with dens invaginatus (DI) in panoramic radiographs using deep learning algorithms and to compare the success of the algorithms.
Development and validation of a predictive nomogram for bilateral posterior condylar displacement using cone-beam computed tomography and machine-learning algorithms: a retrospective observational study.
BMC oral health
Jun 5, 2025
BACKGROUND: Temporomandibular disorders (TMDs) are frequently associated with posterior condylar displacement; however, early prediction of this displacement remains a significant challenge. Therefore, in this study, we aimed to develop and evaluate ...
A radiogenomics study on F-FDG PET/CT in endometrial cancer by a novel deep learning segmentation algorithm.
BMC cancer
Jun 5, 2025
OBJECTIVE: To create an automated PET/CT segmentation method and radiomics model to forecast Mismatch repair (MMR) and TP53 gene expression in endometrial cancer patients, and to examine the effect of gene expression variability on image texture feat...
StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images.
BMC medical imaging
Jun 5, 2025
BACKGROUND AND OBJECTIVE: Stroke ranks among the leading causes of disability and death worldwide. Timely detection can reduce its impact. Machine learning delivers powerful tools for image‑based diagnosis. This study introduces StrokeNeXt, a lightwe...
Screening of glioma susceptibility SNPs and construction of risk models based on machine learning algorithms.
BMC neurology
Jun 5, 2025
BACKGROUND: Glioma is a common primary malignant brain tumor. This study aimed to develop a predictive model for glioma risk by these screened key SNPs in the Chinese Han population.
A method for spatial interpretation of weakly supervised deep learning models in computational pathology.
Scientific reports
Jun 5, 2025
Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where labels only exist on the patient or WSI level (e.g. patient outcomes or...
A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation.
Scientific reports
Jun 5, 2025
In clinical medicine, a reliable and resource-friendly computer-aided diagnosis (CAD) method for brain tumor segmentation is essential to enhance diagnostic accuracy and therapeutic outcomes, particularly in regions with uneven healthcare resource di...
Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer.
Scientific reports
Jun 5, 2025
Breast cancer is the most common type of cancer in women, and while current treatments can cure the majority of early-stage primary BC cases, recurrence remains a significant challenge. Traditional methods of assessing patient prognosis, such as AJCC...
Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning.
Scientific reports
Jun 5, 2025
The pancreas is a gland in the abdomen that helps to produce hormones and digest food. The irregular development of tissues in the pancreas is termed as pancreatic cancer. Identification of pancreatic tumors early is significant for enhancing surviva...
GNNs surpass transformers in tumor medical image segmentation.
Scientific reports
Jun 5, 2025
To assess the suitability of Transformer-based architectures for medical image segmentation and investigate the potential advantages of Graph Neural Networks (GNNs) in this domain. We analyze the limitations of the Transformer, which models medical i...
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