Latest AI and machine learning research in preventive care for healthcare professionals.
Multi-map Sparse Monocular visual Simultaneous Localization and Mapping applied to monocular endoscopic sequences has proven efficient to robustly recover tracking after the frequent losses in endoscopy due to motion blur, temporal occlusion, tools interaction or water jets. The sparse multi-maps are adequate for robust camera localization, however they are very poor for environment representati...
While research has established the potential of AI models for mammography to improve breast cancer screening outcomes, there have not been any detailed subgroup evaluations performed to assess the strengths and weaknesses of commercial models for digital breast tomosynthesis (DBT) imaging. This study presents a granular evaluation of the Lunit INSIGHT DBT model on a large retrospective cohort of...
Despite advancements in Computer-Aided Diagnosis (CAD) systems, breast cancer remains one of the leading causes of cancer-related deaths among women...
While AI-assisted colonoscopy promises improved colorectal cancer screening, its success relies on effective integration into clinical practice, not...
Early detection of cervical cancer is crucial for improving patient outcomes and reducing mortality by identifying precancerous lesions as soon as p...
Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities. However, they also present challenges,...
Despite significant progress in robust deep learning techniques for mammogram breast cancer classification, their reliability in real-world clinical...
Scoring functions (SFs) of molecular docking is a vital component of structure-based virtual screening (SBVS). Traditional SFs yield their inherent sh...
The COVID-19 pandemic has profoundly affected the normal course of life -- from lock-downs and virtual meetings to the unprecedentedly swift creatio...
Efficient CO2 capture is vital for mitigating climate change, with amine-based solvents being widely used due to their strong reactivity with CO2. H...
Children with neurodevelopmental disorders require timely intervention to improve long-term outcomes, yet early screening remains inaccessible in ma...
BACKGROUND: The Mayo endoscopic subscore (MES) is an important quantitative measure of disease activity in ulcerative colitis. Colonoscopy reports in ...
Purpose To evaluate the performance of eight lung cancer prediction models on patient cohorts with screening-detected, incidentally detected, and bron...
This study aims to utilize male blood metabolite signatures for (i) distinguishing between healthy individuals and athletes, thereby optimizing the at...
This systematic review evaluates the application of machine learning (ML) models for diagnosing pulmonary tuberculosis and their potential to inform n...
Background The ScreenTrustCAD trial was a prospective study that evaluated the cancer detection rates for combinations of artificial intelligence (AI)...
The lack of labeled datasets in 3D vision for surgical scenes inhibits the development of robust 3D reconstruction algorithms in the medical domain....
Generalizable medical image segmentation is essential for ensuring consistent performance across diverse unseen clinical settings. However, existing...
Linear $L_1$-regularized models have remained one of the simplest and most effective tools in data science. Over the past decade, screening rules ha...
Background: Recruitment for cohorts involving complex liver diseases, such as hepatocellular carcinoma and liver cirrhosis, often requires interpret...