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Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays.

Computers in biology and medicine
Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be a significant global health issue, especially with the increasing use of engineered stone (ES) surfaces containing high silica content. Traditi...

Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.

International journal of molecular sciences
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to...

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.

Journal of visualized experiments : JoVE
This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in...

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model.

Journal of visualized experiments : JoVE
Lymph node status is a critical prognostic predictor for patients; however, the prognosis of colorectal signet-ring cell carcinoma (SRCC) has garnered limited attention. This study investigates the prognostic predictive capacity of the log odds of po...

Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections.

Radiation oncology (London, England)
BACKGROUND: Delineating the internal gross tumor volume (IGTV) is crucial for the treatment of non-small cell lung cancer (NSCLC). Deep learning (DL) enables the automation of this process; however, current studies focus mainly on multiple phases of ...

MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d'Ivoire.

Malaria journal
BACKGROUND: In sub-Saharan Africa, Plasmodium falciparum is the most prevalent species of malaria parasites. In endemic areas, malaria is mainly diagnosed using microscopy or rapid diagnostic tests (RDTs), which have limited sensitivity, and microsco...

A machine learning-based severity stratification tool for high altitude pulmonary edema.

BMC medical informatics and decision making
This study aimed to identify key predictors for the severity of High Altitude Pulmonary Edema (HAPE) to assist clinicians in promptly recognizing severely affected patients in the emergency department, thereby reducing associated mortality rates. Mul...

A machine learning approach to investigate the role of fear of pain, personal experience, and vicarious learning in dental anxiety.

BMC oral health
BACKGROUND: Dental anxiety is a pervasive problem worldwide, leading to avoidance of dental care, resulting in oral health problems and impacting daily life through social withdrawal and work absenteeism. Addressing this fear is an important public h...

Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.

BMC gastroenterology
BACKGROUND AND AIM: Colorectal cancer is among the most prevalent and deadliest cancers. Early prediction of metastasis in patients with colorectal cancer is crucial in preventing it from the advanced stages and enhancing the prognosis among these pa...