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An audit of the impact of the introduction of a commercial artificial intelligence-driven auto-contouring tool into a radiotherapy department.

The British journal of radiology
OBJECTIVES: To audit prospectively the accuracy, time saving, and utility of a commercial artificial intelligence auto-contouring tool (AIAC). To assess the reallocation of time released by AIAC.

CATALYZE: a deep learning approach for cataract assessment and grading on SS-OCT images.

Journal of cataract and refractive surgery
PURPOSE: To assess a new objective deep learning model cataract grading method based on swept-source optical coherence tomography (SS-OCT) scans provided by the Anterion.

Applying Conformal Prediction to a Deep Learning Model for Intracranial Hemorrhage Detection to Improve Trustworthiness.

Radiology. Artificial intelligence
Purpose To apply conformal prediction to a deep learning (DL) model for intracranial hemorrhage (ICH) detection and evaluate model performance in detection as well as model accuracy in identifying challenging cases. Materials and Methods This was a r...

Using Machine Learning Techniques to Predict Viral Suppression Among People With HIV.

Journal of acquired immune deficiency syndromes (1999)
BACKGROUND: This study aims to develop and examine the performance of machine learning (ML) algorithms in predicting viral suppression among statewide people living with HIV (PWH) in South Carolina.

Deep learning enables automatic detection of joint damage progression in rheumatoid arthritis-model development and external validation.

Rheumatology (Oxford, England)
OBJECTIVES: Although deep learning has demonstrated substantial potential in automatic quantification of joint damage in RA, evidence for detecting longitudinal changes at an individual patient level is lacking. Here, we introduce and externally vali...

Machine learning model-based prediction of postpancreatectomy acute pancreatitis following pancreaticoduodenectomy: A retrospective cohort study.

World journal of gastroenterology
BACKGROUND: The International Study Group of Pancreatic Surgery has established the definition and grading system for postpancreatectomy acute pancreatitis (PPAP). There are no established machine learning models for predicting PPAP following pancrea...

Optimizing functional brain network analysis by incorporating nonlinear factors and frequency band selection with machine learning models.

Medicine
The accurate assessment of the brain's functional network is seen as crucial for the understanding of complex relationships between different brain regions. Hidden information within different frequency bands, which is often overlooked by traditional...

[Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR 
Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].

Zhongguo fei ai za zhi = Chinese journal of lung cancer
BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic facto...

Deep learning model for identifying acute heart failure patients using electrocardiography in the emergency room.

European heart journal. Acute cardiovascular care
AIMS: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep learning...