AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

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Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features.

Aging clinical and experimental research
OBJECTIVES: Sarcopenic obesity (SO), characterized by the coexistence of obesity and sarcopenia, is an increasingly prevalent condition in aging populations, associated with numerous adverse health outcomes. We aimed to identify and validate an expla...

Clinical value of aortic arch morphology in transfemoral TAVR: artificial intelligence evaluation.

International journal of surgery (London, England)
BACKGROUND: The impact of aortic arch (AA) morphology on the management of the procedural details and the clinical outcomes of the transfemoral artery (TF)-transcatheter aortic valve replacement (TAVR) has not been evaluated. The goal of this study w...

Neurofind: using deep learning to make individualised inferences in brain-based disorders.

Translational psychiatry
Within precision psychiatry, there is a growing interest in normative models given their ability to parse heterogeneity. While they are intuitive and informative, the technical expertise and resources required to develop normative models may not be a...

Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture.

Clinical interventions in aging
BACKGROUND: Hip fractures have become a significant health concern, particularly among super-aged patients, who were at a high risk of postoperative pneumonia due to their frailty and the presence of multiple comorbidities. This study aims to establi...

CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.

BMC geriatrics
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that significantly impacts health care worldwide, particularly among the elderly population. The accurate classification of AD stages is essential for slowing disease progression an...

Linguistic cues for automatic assessment of Alzheimer's disease across languages.

Journal of Alzheimer's disease : JAD
BackgroundMost common forms of dementia, including Alzheimer's disease, are associated with alterations in spoken language.ObjectiveThis study explores the potential of a speech-based machine learning (ML) approach in estimating cognitive impairment,...

Preoperative clinical radiomics model based on deep learning in prognostic assessment of patients with gallbladder carcinoma.

BMC cancer
OBJECTIVE: We aimed to develop a preoperative clinical radiomics survival prediction model based on the radiomics features via deep learning to provide a reference basis for preoperative assessment and treatment decisions for patients with gallbladde...

Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.

Scientific reports
Stroke is a significant health concern in China. Differences in stroke risk between rural and urban areas have been highlighted in prior research. However, there is a scarcity of studies on urban-rural differences in predicting stroke. This study aim...

Predicting 90-day risk of urinary tract infections following urostomy in bladder cancer patients using machine learning and explainability.

Scientific reports
This research aims to design and validate a machine learning model to predict the probability of urinary tract infections within 90 days post-urostomy in bladder cancer patients. Clinical and follow-up information from 317 patients who had urostomy p...

Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study.

JMIR aging
BACKGROUND: Understanding the dementia disease trajectory and clinical practice patterns in outpatient settings is vital for effective management. Knowledge about the path from initial memory loss complaints to dementia diagnosis remains limited.