AIMC Topic: Middle Aged

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Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalizing Care.

IEEE journal of biomedical and health informatics
The role of sensing technologies, such as wearables, in delivering precision care is becoming widely acceptable. Given the very large quantities of sensor data that rapidly accumulate, there is a need to employ automated algorithms to label biosignal...

Benchmarking deep learning models on large healthcare datasets.

Journal of biomedical informatics
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist w...

Robot-assisted enucleation of large dumbbell-shaped esophageal schwannoma: a case report.

BMC surgery
BACKGROUND: Esophageal schwannomas are extremely rare, with few cases reported in the literature. Traditionally, resection of esophageal schwannoma is typically performed using thoracotomy or video-assisted thoracic surgery. However, large, irregular...

A Deep Learning-Based Algorithm Identifies Glaucomatous Discs Using Monoscopic Fundus Photographs.

Ophthalmology. Glaucoma
PURPOSE: To develop and test the performance of a deep learning-based algorithm for glaucomatous disc identification using monoscopic fundus photographs.

Multicenter validation of [F]-FDG PET and support-vector machine discriminant analysis in automatically classifying patients with amyotrophic lateral sclerosis versus controls.

Amyotrophic lateral sclerosis & frontotemporal degeneration
OBJECTIVE: F-Fluorodeoxyglucose (F-FDG) positron emission tomography (PET) single-center studies using support vector machine (SVM) approach to differentiate amyotrophic lateral sclerosis (ALS) from controls have shown high overall accuracy on an ind...

Assessment of the Diagnostic Accuracy of Biparametric Magnetic Resonance Imaging for Prostate Cancer in Biopsy-Naive Men: The Biparametric MRI for Detection of Prostate Cancer (BIDOC) Study.

JAMA network open
IMPORTANCE: Multiparametric magnetic resonance imaging (MRI) enhances detection and risk stratification for significant prostate cancer but is time-consuming (approximately 40 minutes) and expensive. Rapid and simpler (approximately 15-minute) bipara...

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients.

Experimental brain research
Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in ...

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

BMC cancer
BACKGROUND: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. Howev...

Using machine-learning approaches to predict non-participation in a nationwide general health check-up scheme.

Computer methods and programs in biomedicine
BACKGROUND: In the time since the launch of a nationwide general health check-up and instruction program in Japan in 2008, interest in the formulation of an effective and efficient strategy to improve the participation rate has been growing. The aim ...