OBJECTIVES: To investigate the feasibility and efficacy of a deep-learning (DL)-based three-dimensional (3D) super-resolution (SR) MRI radiomics model for preoperative T-staging prediction in rectal cancer (RC).
In the last decade, research on artificial intelligence has seen rapid growth with deep learning models, especially in the field of medical image segmentation. Various studies demonstrated that these models have powerful prediction capabilities and a...
Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the ...
This study aims to investigate the accuracy of a fine-tuned deep convolutional neural network (CNN) for evaluating responses to the pentagon copying test (PCT). To develop a CNN that could classify PCT images, we fine-tuned and compared the pre-train...
INTRODUCTION: To continue closing the gap between the predictive modeling and its real-world application, we report a new data-to-prediction pipeline that advanced the state-of-the-art predictive performance of body mass index (BMI) classifications b...
The pathological identification of lymph node (LN) metastasis is demanding and tedious. Although convolutional neural networks (CNNs) possess considerable potential in improving the process, the ultrahigh-resolution of whole slide images hinders the ...
In recent years, extensive resources are dedicated to the development of machine learning (ML) based clinical prediction models for intensive care unit (ICU) patients. These models are transforming patient care into a collaborative human-AI task, yet...
BACKGROUND: Machine learning algorithms are currently used in a wide array of clinical domains to produce models that can predict clinical risk events. Most models are developed and evaluated with retrospective data, very few are evaluated in a clini...
Correlative microscopy combines data from different microscopical techniques to gain unique insights about specimens. A key requirement to unlocking the full potential is an advanced classification method that can combine the various analytical signa...
BACKGROUND: Pulmonary embolism (PE) is a life-threatening condition associated with ~10% of deaths of hospitalized patients. Machine learning algorithms (MLAs) which predict the onset of pulmonary embolism (PE) could enable earlier treatment and impr...