AIMC Topic: Aged, 80 and over

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Machine learning-based histopathological features of histological slides and clinical characteristics as a novel prognostic indicator in diffuse large B-cell lymphoma.

Pathology, research and practice
OBJECTIVE: This study developed and validated a deep learning model based on clinical and histopathological features for predicting the outcomes of diffuse large B-cell lymphoma (DLBCL).

Predicting mortality risk following major lower extremity amputation using machine learning.

Journal of vascular surgery
OBJECTIVE: Major lower extremity amputation for advanced vascular disease involves significant perioperative risks. Although outcome prediction tools could aid in clinical decision-making, they remain limited. To address this, we developed machine le...

Effect of contrast enhancement on diagnosis of interstitial lung abnormality in automatic quantitative CT measurement.

European radiology
OBJECTIVE: To investigate the effect of contrast enhancement on the diagnosis of interstitial lung abnormalities (ILA) in automatic quantitative CT measurement in patients with paired pre- and post-contrast scans.

Explainable AI assisted vertebral refracture diagnosis after percutaneous vertebroplasty through effective feature engineering and stacked ensemble learning.

International journal of medical informatics
BACKGROUND AND OBJECTIVE: To develop and validate a machine learning model based on stacking ensemble learning and feature selection strategies to predict vertebral refracture risk after percutaneous vertebroplasty.

Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models.

JAMA network open
IMPORTANCE: Clinical artificial intelligence (AI) systems are susceptible to performance degradation due to data shifts, which can lead to erroneous predictions and potential patient harm. Proactively detecting and mitigating these shifts is crucial ...

Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability.

BMC public health
BACKGROUND: Mild Cognitive Impairment (MCI) is a critical transitional stage between normal aging and Alzheimer's disease, and its early identification is essential for delaying disease progression.

Utility of artificial intelligence-based conversation voice analysis for detecting cognitive decline.

PloS one
Recent developments in artificial intelligence (AI) have introduced new technologies that can aid in detecting cognitive decline. This study developed a voice-based AI model that screens for cognitive decline using only a short conversational voice s...

A preliminary study of the reliability and validity of the Uyghur version of the NUCOG cognitive screening application.

BMC neurology
INTRODUCTION: Technological advances and artificial intelligence now make it feasible to administer cognitive assessments on touch-screen devices. The aim of this study is to develop a Uyghur version of the NUCOG cognitive screening application and e...

Operationalizing postmortem pathology-MRI association studies in Alzheimer's disease and related disorders with MRI-guided histology sampling.

Acta neuropathologica communications
Postmortem neuropathological examination, while the gold standard for diagnosing neurodegenerative diseases, often relies on limited regional sampling that may miss critical areas affected by Alzheimer's disease and related disorders. Ultra-high reso...