AIMC Topic: Machine Learning

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Machine Intelligence in Cerebrovascular and Endovascular Neurosurgery.

Advances in experimental medicine and biology
The advent of different realms of computational neurosurgery-including not only machine intelligence but also visualization techniques such as mixed reality and robotic applications-is beginning to impact both open vascular as well as endovascular ne...

Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.

Advances in experimental medicine and biology
The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in stereotactic radiosurgery. The rapid evolution of AI technology has led to promising developments in thi...

Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery.

Advances in experimental medicine and biology
This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative car...

Machine and Deep Learning in Hyperspectral Fluorescence-Guided Brain Tumor Surgery.

Advances in experimental medicine and biology
Malignant glioma resection is often the first line of treatment in neuro-oncology. During glioma surgery, the discrimination of tumor's edges can be challenging at the infiltration zone, even by using surgical adjuncts such as fluorescence guidance (...

Machine Learning and Radiomics in Gliomas.

Advances in experimental medicine and biology
The integration of machine learning (ML) and radiomics is emerging as a pivotal advancement in glioma research, offering novel insights into the diagnosis, prognosis, and treatment of these complex tumors. Radiomics involves the extraction of a multi...

Artificial Intelligence in Brain Tumors.

Advances in experimental medicine and biology
The introduction of "intelligent machines" goes back to Alan Turing in the 1940s. Artificial intelligence (AI) is a broad umbrella covering different methodologies, such as machine learning and deep learning. Deep learning, characterized by multilaye...

Artificial Intelligence Methods.

Advances in experimental medicine and biology
Artificial intelligence (AI) is at the forefront of driving pivotal changes across diverse fields. AI holds the potential to make profound impacts on addressing contemporary healthcare challenges. This chapter aims to provide an overview of AI method...

Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.

Technology and health care : official journal of the European Society for Engineering and Medicine
Pneumonia is a dangerous disease that kills millions of children and elderly patients worldwide every year. The detection of pneumonia from a chest x-ray is perpetrated by expert radiologists. The chest x-ray is cheaper and is most often used to diag...

Multimodal representation learning for medical analytics - a systematic literature review.

Health informatics journal
Machine learning-based analytics over uni-modal medical data has shown considerable promise and is now routinely deployed in diagnostic procedures. However, patient data consists of diverse types of data. By exploiting such data, multimodal approach...

Identifying the Role of Oligodendrocyte Genes in the Diagnosis of Alzheimer's Disease through Machine Learning and Bioinformatics Analysis.

Current Alzheimer research
BACKGROUND: Due to the heterogeneity of Alzheimer's disease (AD), the underlying pathogenic mechanisms have not been fully elucidated. Oligodendrocyte (OL) damage and myelin degeneration are prevalent features of AD pathology. When oligodendrocytes a...