AIMC Topic: Humans

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Predicting Intracranial Aneurysm Rupture: A Multifactor Analysis Combining Radscore, Morphology, and PHASES Parameters.

Academic radiology
RATIONALE AND OBJECTIVES: We aimed at developing and validating a nomogram and machine learning (ML) models based on radiomics score (Radscore), morphology, and PHASES to predict intracranial aneurysm (IA) rupture.

The synchronous upregulation of a specific protein cluster in the blood predicts both colorectal cancer risk and patient immune status.

Gene
BACKGROUND: Early detection and treatment of colorectal cancer (CRC) is crucial for improving patient survival rates. This study aims to identify signature molecules associated with CRC, which can serve as valuable indicators for clinical hematologic...

Endometriosis Online Communities: How Machine Learning Can Help Physicians Understand What Patients Are Discussing Online.

Journal of minimally invasive gynecology
STUDY OBJECTIVE: Use machine learning to characterize the content of endometriosis online community posts and comments.

Leveraging feature selection for enhanced fall risk prediction in elderly using gait analysis.

Medical & biological engineering & computing
There is no effective fall risk screening tool for the elderly that can be integrated into clinical practice. Developing a system that can be easily used in primary care services is a current need. Current studies focus on the use of multiple sensors...

Breast histopathological imaging using ultra-fast fluorescence confocal microscopy to identify cancer lesions at early stage.

Microscopy research and technique
Ultrafast fluorescent confocal microscopy is a hypothetical approach for breast cancer detection because of its potential to achieve instantaneous, high-resolution images of cellular-level tissue features. Traditional approaches such as mammography a...

Assessing the performance of AI-assisted technicians in liver segmentation, Couinaud division, and lesion detection: a pilot study.

Abdominal radiology (New York)
BACKGROUND: In patients with primary and secondary liver cancer, the number and sizes of lesions, their locations within the Couinaud segments, and the volume and health status of the future liver remnant are key for informing treatment planning. Cur...

Randomized algorithms for large-scale dictionary learning.

Neural networks : the official journal of the International Neural Network Society
Dictionary learning is an important sparse representation algorithm which has been widely used in machine learning and artificial intelligence. However, for massive data in the big data era, classical dictionary learning algorithms are computationall...

Predicting whether patients in an acute medical unit are physiologically fit-for-discharge using machine learning: A proof-of-concept.

International journal of medical informatics
INTRODUCTION: Delays in discharging patients from Acute Medical Units hamper patient flows throughout the hospital. The decision to discharge a patient is mainly based on the patients' physiological condition, but may vary between physicians. An obje...

Use of artificial intelligence-guided echocardiography to detect cardiac dysfunction and heart valve disease in rural and remote areas: Rationale and design of the AGILE-echo trial.

American heart journal
BACKGROUND: Transthoracic echocardiography (TTE) is essential in the diagnosis of cardiovascular diseases (CVD), including but not limited to heart failure (HF) and heart valve disease (HVD). However, its dependence on expert acquisition means that i...

Automated Method for Growing Rod Length Measurement on Ultrasound Images in Children With Early Onset Scoliosis.

Ultrasound in medicine & biology
OBJECTIVE: To develop and validate machine learning algorithms to automatically extract the rod length of the magnetically controlled growing rod from ultrasound images (US) in a pilot study.