AIMC Topic: Aged, 80 and over

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c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease.

BMC medical genomics
BACKGROUND: Alzheimer's disease (AD) is an incurable, debilitating neurodegenerative disorder. Current biomarkers for AD diagnosis require expensive neuroimaging or invasive cerebrospinal fluid sampling, thus precluding early detection. Blood-based b...

Direct deep learning-based survival prediction from pre-interventional CT prior to transcatheter aortic valve replacement.

European journal of radiology
PURPOSE: To investigate survival prediction in patients undergoing transcatheter aortic valve replacement (TAVR) using deep learning (DL) methods applied directly to pre-interventional CT images and to compare performance with survival models based o...

Detection of Vertebral Mass and Diagnosis of Spinal Cord Compression in Computed Tomography With Deep Learning Reconstruction: Comparison With Hybrid Iterative Reconstruction.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
PURPOSE: To compare the impact of deep learning reconstruction (DLR) and hybrid-iterative reconstruction (hybrid-IR) on vertebral mass depiction, detection, and diagnosis of spinal cord compression on computed tomography (CT).

Developing tongue coating status assessment using image recognition with deep learning.

Journal of prosthodontic research
PURPOSE: To build an image recognition network to evaluate tongue coating status.

Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer.

Journal of medical imaging and radiation oncology
INTRODUCTION: Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to pr...

Perioperative safety and efficacy of robot-assisted total hip arthroplasty in ERAS-managed patients: a pilot study.

Journal of orthopaedic surgery and research
AIMS: Robot-assisted total hip arthroplasty (rTHA) boasts superior accuracy in implant placement, but there is a lack of effective assessment in perioperative management in the context of enhanced recovery after surgery (ERAS). This study aimed to co...

Predicting brain age gap with radiomics and automl: A Promising approach for age-Related brain degeneration biomarkers.

Journal of neuroradiology = Journal de neuroradiologie
The Brain Age Gap (BAG), which refers to the difference between chronological age and predicted neuroimaging age, is proposed as a potential biomarker for age-related brain degeneration. However, existing brain age prediction models usually rely on a...

The Fidelity of Artificial Intelligence to Multidisciplinary Tumor Board Recommendations for Patients with Gastric Cancer: A Retrospective Study.

Journal of gastrointestinal cancer
PURPOSE: Due to significant growth in the volume of information produced by cancer research, staying abreast of recent developments has become a challenging task. Artificial intelligence (AI) can learn, reason, and understand the enormous corpus of l...

Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm.

European radiology
OBJECTIVES: To perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASi...

Using a dual-stream attention neural network to characterize mild cognitive impairment based on retinal images.

Computers in biology and medicine
Mild cognitive impairment (MCI) is a critical transitional stage between normal cognition and dementia, for which early detection is crucial for timely intervention. Retinal imaging has been shown as a promising potential biomarker for MCI. This stud...