AI Medical Compendium Topic

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Utilization of Artificial Intelligence in Echocardiography.

Circulation journal : official journal of the Japanese Circulation Society
Echocardiography has a central role in the diagnosis and management of cardiovascular disease. Precise and reliable echocardiographic assessment is required for clinical decision-making. Even if the development of new technologies (3-dimentional echo...

Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network.

Medical physics
PURPOSE: Scatter is a major factor degrading the image quality of cone beam computed tomography (CBCT). Conventional scatter correction strategies require handcrafted analytical models with ad hoc assumptions, which often leads to less accurate scatt...

Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets.

Molecules (Basel, Switzerland)
Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two l...

Improving Workflow Efficiency for Mammography Using Machine Learning.

Journal of the American College of Radiology : JACR
OBJECTIVE: The aim of this study was to determine whether machine learning could reduce the number of mammograms the radiologist must read by using a machine-learning classifier to correctly identify normal mammograms and to select the uncertain and ...

IILS: Intelligent imaging layout system for automatic imaging report standardization and intra-interdisciplinary clinical workflow optimization.

EBioMedicine
BACKGROUND: To achieve imaging report standardization and improve the quality and efficiency of the intra-interdisciplinary clinical workflow, we proposed an intelligent imaging layout system (IILS) for a clinical decision support system-based ubiqui...

Breast cancer outcome prediction with tumour tissue images and machine learning.

Breast cancer research and treatment
PURPOSE: Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.

NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer.

BMC medical genomics
BACKGROUND: The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue,...

Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning.

Pediatric radiology
BACKGROUND: An automated method for identifying the anatomical region of an image independent of metadata labels could improve radiologist workflow (e.g., automated hanging protocols) and help facilitate the automated curation of large medical imagin...

Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos.

International journal of computer assisted radiology and surgery
PURPOSE: Real-time surgical tool tracking is a core component of the future intelligent operating room (OR), because it is highly instrumental to analyze and understand the surgical activities. Current methods for surgical tool tracking in videos nee...