AIMC Topic: Adult

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Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of autom...

Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder.

Journal of affective disorders
BACKGROUND: Bipolar disorder (BD) is a chronic psychiatric mood disorder that is solely diagnosed based on clinical symptoms. These symptoms often overlap with other psychiatric disorders. Efforts to use machine learning (ML) to create predictive mod...

Machine Learning in Clinical Diagnosis of Head and Neck Cancer.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
OBJECTIVE: Machine learning has been effective in other areas of medicine, this study aims to investigate this with regards to HNC and identify which algorithm works best to classify malignant patients.

Deep learning constrained compressed sensing reconstruction improves high-resolution three-dimensional (3D) T2-weighted turbo spin echo magnetic resonance imaging (MRI) of the lumbar spine.

Clinical radiology
AIM: We sought to assess the image quality of three-dimensional (3D) T2-weighted (T2w) turbo spin echo (TSE) sequences with deep learning (DL)-constrained compressed sensing (CS) reconstruction relative to a reference two-dimensional (2D) T2w TSE seq...

Machine learning predicts acute respiratory failure in pancreatitis patients: A retrospective study.

International journal of medical informatics
PURPOSE: The purpose of the research is to design an algorithm to predict the occurrence of acute respiratory failure (ARF) in patients with acute pancreatitis (AP).

Development and validation of machine learning models for predicting cancer-related fatigue in lymphoma survivors.

International journal of medical informatics
BACKGROUND: New cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (CRF), severely impacts the quality of life of lymphoma survivors. However, clinical diagnosis and treatment of CRF are inadequate and require enhanceme...

Development and validation of cardiometabolic risk predictive models based on LDL oxidation and candidate geromarkers from the MARK-AGE data.

Mechanisms of ageing and development
The predictive value of the susceptibility to oxidation of LDL particles (LDLox) in cardiometabolic risk assessment is incompletely understood. The main objective of the current study was to assess its relationship with other relevant biomarkers and ...

AI in obstetrics: Evaluating residents' capabilities and interaction strategies with ChatGPT.

European journal of obstetrics, gynecology, and reproductive biology
In line with the digital transformation trend in medical training, students may resort to artificial intelligence (AI) for learning. This study assessed the interaction between obstetrics residents and ChatGPT during clinically oriented summative eva...

Validation of anorexia nervosa and Bulimia nervosa diagnosis coding in Danish hospitals assisted by a natural language processing model.

Journal of psychiatric research
INTRODUCTION: The Danish Health Care Registers rely on the International Statistical Classification of Diseases and Related Health Problems (ICD)-classification and stand as a widely utilized resource for health epidemiological research. Eating disor...

Ensemble machine learning framework for predicting maternal health risk during pregnancy.

Scientific reports
Maternal health risks can cause a range of complications for women during pregnancy. High blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health conditions can all lead to pregnancy complications. Proper identificatio...