AI Medical Compendium Topic

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Mass Screening

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Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely t...

Improving colorectal cancer screening - consumer-centred technological interventions to enhance engagement and participation amongst diverse cohorts.

Clinics and research in hepatology and gastroenterology
The current "Gold Standard" colorectal cancer (CRC) screening approach of faecal occult blood test (FOBT) with follow-up colonoscopy has been shown to significantly improve morbidity and mortality, by enabling the early detection of disease. However,...

Chest X-ray-based opportunistic screening of sarcopenia using deep learning.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Early detection and management of sarcopenia is of clinical importance. We aimed to develop a chest X-ray-based deep learning model to predict presence of sarcopenia.

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study.

Frontiers in endocrinology
BACKGROUND: Opportunely screening for diabetes is crucial to reduce its related morbidity, mortality, and socioeconomic burden. Machine learning (ML) has excellent capability to maximize predictive accuracy. We aim to develop ML-augmented models for ...

On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review.

Frontiers in public health
A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbid...

Improvement of intervention information detection for automated clinical literature screening during systematic review.

Journal of biomedical informatics
Systematic literature review (SLR) is a crucial method for clinicians and policymakers to make their decisions in a flood of new clinical studies. Because manual literature screening in SLR is a highly laborious task, its automation by natural langua...

Deep Learning-Based Computed Tomography Features in Evaluating Early Screening and Risk Factors for Chronic Obstructive Pulmonary Disease.

Contrast media & molecular imaging
This research aimed to investigate the diagnostic effect of computed tomography (CT) images based on a deep learning double residual convolution neural network (DRCNN) model on chronic obstructive pulmonary disease (COPD) and the related risk factors...

Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis.

Scientific reports
Machine learning (ML) algorithms are becoming increasingly pervasive in the domains of medical diagnostics and prognostication, afforded by complex deep learning architectures that overcome the limitations of manual feature extraction. In this system...

Mapping the evidence on identity processes and identity-related interventions in the smoking and physical activity domains: a scoping review protocol.

BMJ open
INTRODUCTION: Smoking and insufficient physical activity (PA), independently but especially in conjunction, often lead to disease and (premature) death. For this reason, there is need for effective smoking cessation and PA-increasing interventions. I...

Automated image curation in diabetic retinopathy screening using deep learning.

Scientific reports
Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require curation, a laborious task to perform manually. We developed and validated single and multi-output ...