AIMC Topic: Aged, 80 and over

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Vertebral compression fractures at abdominal CT: underdiagnosis, undertreatment, and evaluation of an AI algorithm.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Vertebral compression fractures (VCFs) are common and indicate a high future risk of additional osteoporotic fractures. However, many VCFs are unreported by radiologists, and even if reported, many patients do not receive treatment. The purpose of th...

Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patients With Cancer.

JCO clinical cancer informatics
PURPOSE: Palliative care is recommended for patients with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patients with cancer and may help distinguish who benefits the most...

Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting.

Radiology
Background Radiology practices have a high volume of unremarkable chest radiographs and artificial intelligence (AI) could possibly improve workflow by providing an automatic report. Purpose To estimate the proportion of unremarkable chest radiograph...

A preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.

The British journal of radiology
OBJECTIVES: To investigate the usefulness of super-resolution deep learning reconstruction (SR-DLR) with cardiac option in the assessment of image quality in patients with stent-assisted coil embolization, coil embolization, and flow-diverting stent ...

Drug Burden Index Is a Modifiable Predictor of 30-Day Hospitalization in Community-Dwelling Older Adults With Complex Care Needs: Machine Learning Analysis of InterRAI Data.

The journals of gerontology. Series A, Biological sciences and medical sciences
BACKGROUND: Older adults (≥65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predic...

DEEP LEARNING FOR AUTOMATIC PREDICTION OF EARLY ACTIVATION OF TREATMENT-NAIVE NONEXUDATIVE MACULAR NEOVASCULARIZATIONS IN AGE-RELATED MACULAR DEGENERATION.

Retina (Philadelphia, Pa.)
BACKGROUND: Around 30% of nonexudative macular neovascularizations exudate within 2 years from diagnosis in patients with age-related macular degeneration. The aim of this study is to develop a deep learning classifier based on optical coherence tomo...

Smartphone-Based Balance Assessment Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study explores the potential of smartphones to objectively assess balance, which is crucial for the elderly and individuals recovering from various medical conditions. We propose an innovative methodology to estimate the Modified Clinical Test o...

Integrating Wearable Sensor Technology and Machine Learning for Objective m-CTSIB Balance Score Estimation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study marks the first endeavor to utilize wearable technology combined with machine learning to objectively assess the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB). We focus on developing an affordable, easily accessible me...

Sequence of Morphological Changes Preceding Atrophy in Intermediate AMD Using Deep Learning.

Investigative ophthalmology & visual science
PURPOSE: Investigating the sequence of morphological changes preceding outer plexiform layer (OPL) subsidence, a marker preceding geographic atrophy, in intermediate AMD (iAMD) using high-precision artificial intelligence (AI) quantifications on opti...