AIMC Topic: Female

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YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.

BMC medical imaging
OBJECTIVES: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and...

Explainable deep learning and biomechanical modeling for TMJ disorder morphological risk factors.

JCI insight
Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis, development of targeted prevention, and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analyses of disease ...

Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.

PloS one
Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healt...

Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The process of reconstructing underlying cortical and subcortical electrical activities from Electroencephalography (EEG) or Magnetoencephalography (MEG) recordings is called Electrophysiological Source Imaging (ESI). Given the complementarity betwee...

Integrated Transcriptomic Landscape and Deep Learning Based Survival Prediction in Uterine Sarcomas.

Cancer research and treatment
PURPOSE: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs).

Predicting Secondary Vertebral Compression Fracture After Vertebral Augmentation via CT-Based Machine Learning Radiomics-Clinical Model.

Academic radiology
RATIONALE AND OBJECTIVES: Secondary vertebral compression fractures (SVCF) are very common in patients after vertebral augmentation (VA). The aim of this study was to establish a radiomic-based model to predict SVCF and specify appropriate treatment ...

Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study.

International journal of medical informatics
BACKGROUND: Diabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early detection and recognition of patients vulnerable to progression is crucial.

Early diagnosis of HIV cases by means of text mining and machine learning models on clinical notes.

Computers in biology and medicine
Undiagnosed and untreated human immunodeficiency virus (HIV) infection increases morbidity in the HIV-positive person and allows onward transmission of the virus. Minimizing missed opportunities for HIV diagnosis when a patient visits a healthcare fa...

Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors.

Computers in biology and medicine
Brain atrophy measurements derived from magnetic resonance imaging (MRI) are a promising marker for the diagnosis and prognosis of neurodegenerative pathologies such as Alzheimer's disease or multiple sclerosis. However, its use in individualized ass...