AIMC Topic: Machine Learning

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Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions-Abdominal Imagers' Perspective.

Journal of computer assisted tomography
The applications of machine learning in clinical radiology practice and in particular oncologic imaging practice are steadily evolving. However, there are several potential hurdles for widespread implementation of machine learning in oncologic imagin...

Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.

Journal of computer assisted tomography
OBJECTIVE: The aim of this study was to evaluate various radiomics-based machine learning classification models using the apparent diffusion coefficient (ADC) and cerebral blood flow (CBF) maps for differentiating between low-grade gliomas (LGGs) and...

Improving Healthcare Cost, Quality, and Access Through Artificial Intelligence and Machine Learning Applications.

Journal of healthcare management / American College of Healthcare Executives
Since the early 1970s, technology has increasingly become integrated into the healthcare field. Today, artificial intelligence (AI) and machine learning (ML, a set of learning techniques used by AI) have the capacity to revolutionize the delivery of ...

[Construction of an interpretable machine learning-based prediction model for the clinical effect on ischemic stroke in treatment with eye acupuncture combined with rehabilitation therapy].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion
OBJECTIVE: To construct a prediction model for the clinical effect of eye acupuncture combined with rehabilitation therapy on ischemic stroke based on interpretable machine learning.

The Potential Role of AI- and Machine Learning Models in the Early Detection of Oral Cancer and Oral Potentially Malignant Disorders.

Studies in health technology and informatics
INTRODUCTION: Artificial Intelligence (AI) is playing an increasing role in advancing diagnostic processes and decision-making in healthcare. In the early detection of oral cancer and oral potentially malignant disorders (OPMDs), its role is still be...

An Explainable-AI Based Approach Towards Measuring Cognitive Reserve.

Studies in health technology and informatics
Cognitive Reserve (CR) refers to the brain's ability to compensate for brain damage or age-related changes, which can explain why some individuals show greater cognitive resilience to brain pathology despite damage or age-related changes. Understandi...

Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes.

Journal of medical Internet research
Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and ...

A systematic review and meta-analysis of lung cancer risk prediction models.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. Early detection through targeted screening significantly improves patient outcomes. However, identifying high-risk individuals remains a critical challenge.

Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study.

BMC endocrine disorders
BACKGROUND: Short stature is a prevalent pediatric endocrine disorder for which early detection and prediction are pivotal for improving treatment outcomes. However, existing diagnostic criteria often lack the necessary sensitivity and specificity be...