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Predicting early recurrence in locally advanced gastric cancer after gastrectomy using CT-based deep learning model: a multicenter study.
International journal of surgery (London, England)
Feb 1, 2025
BACKGROUND: Early recurrence in patients with locally advanced gastric cancer (LAGC) portends aggressive biological characteristics and a dismal prognosis. Predicting early recurrence may help determine treatment strategies for LAGC. The goal is to d...
Machine learning-based risk prediction of mild cognitive impairment in patients with chronic heart failure: A model development and validation study.
Geriatric nursing (New York, N.Y.)
Feb 1, 2025
Accurate identification of individuals at high risk for mild cognitive impairment (MCI) among chronic heart failure (CHF) patients is crucial for reducing rehospitalization and mortality rates. This study aimed to develop and validate a machine learn...
Using natural language processing to identify patterns associated with depression, anxiety, and stress symptoms during the COVID-19 pandemic.
Journal of affective disorders
Jan 31, 2025
BACKGROUND: Combining data-driven natural language processing techniques with traditional methods using predefined word lists may offer greater insights into the connections between language patterns and depression and anxiety symptoms, particularly ...
A novel artificial intelligence framework to quantify the impact of clinical compared with nonclinical influences on postoperative length of stay.
Surgery
Jan 31, 2025
BACKGROUND: The relative proportion of clinical compared with nonclinical influences on length of stay after colectomy has never been measured. We developed a novel machine-learning framework that quantifies the proportion of length of stay after col...
Machine learning model for predicting DIBH non-eligibility in left-sided breast cancer radiotherapy: Development, validation and clinical impact analysis.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jan 31, 2025
OBJECTIVE: Multi-day assessments accurately identify patients with left-sided breast cancer who are ineligible for irradiation in Deep Inspiration Breath Hold (DIBH) and minimise on-couch treatment time in those who are eligible. The challenge of imp...
Multiscale deep learning radiomics for predicting recurrence-free survival in pancreatic cancer: A multicenter study.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jan 31, 2025
PURPOSE: This multicenter study aimed to develop and validate a multiscale deep learning radiomics nomogram for predicting recurrence-free survival (RFS) in patients with pancreatic ductal adenocarcinoma (PDAC).
Integrating neuroscience and artificial intelligence: EEG analysis using ensemble learning for diagnosis Alzheimer's disease and frontotemporal dementia.
Journal of neuroscience methods
Jan 31, 2025
BACKGROUND: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are both progressive neurological disorders that affect the elderly. Distinguishing between individuals suffering from these two diseases in the early stages can be quite challeng...
Prediction of pink esthetic score using deep learning: A proof of concept.
Journal of dentistry
Jan 31, 2025
OBJECTIVES: This study aimed to develop a deep learning (DL) model for the predictive esthetic evaluation of single-implant treatments in the esthetic zone.
Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks (part II).
Journal of the neurological sciences
Jan 31, 2025
Neural networks (NNs) possess the capability to learn complex data relationships, recognize inherent patterns by emulating human brain functions, and generate predictions based on novel data. We conducted deep learning utilizing an NN to differentiat...
Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features.
European journal of radiology
Jan 31, 2025
OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlanations (SHAP) for model interpretation.
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