AIMC Topic: Middle Aged

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Enhancing brain age estimation under uncertainty: A spectral-normalized neural gaussian process approach utilizing 2.5D slicing.

NeuroImage
Brain age gap, the difference between estimated brain age and chronological age via magnetic resonance imaging, has emerged as a pivotal biomarker in the detection of brain abnormalities. While deep learning is accurate in estimating brain age, the a...

A novel skeletal muscle quantitative method and deep learning-based sarcopenia diagnosis for cervical cancer patients treated with radiotherapy.

Medical physics
BACKGROUND: Sarcopenia is associated with decreased survival in cervical cancer patients treated with radiotherapy. Cone-beam computed tomography (CBCT) was widely used in image-guided radiotherapy. Sarcopenia is assessed by the skeletal muscle index...

Generation of surgical reports for lymph node dissection during laparoscopic gastric cancer surgery based on artificial intelligence.

International journal of computer assisted radiology and surgery
PURPOSE: This study aimed to develop an artificial intelligence (AI) model for the surgical report output of laparoscopic lymph node dissection in the suprapancreatic region during gastric cancer surgery.

Clinical Trial Notifications Triggered by Artificial Intelligence-Detected Cancer Progression: A Randomized Trial.

JAMA network open
IMPORTANCE: Historically, fewer than 10% of adults with cancer have enrolled in clinical trials. Computational tools have been developed to match patients to trials, but these tools are relevant only when patients need new treatment.

Perspectives on AI and Novel Technologies Among Older Adults, Clinicians, Payers, Investors, and Developers.

JAMA network open
IMPORTANCE: Artificial intelligence (AI) and novel technologies, such as remote sensors, robotics, and decision support algorithms, offer the potential for improving the health and well-being of older adults, but the priorities of key partners across...

Identifying chemotherapy beneficiaries in nasal and paranasal sinus cancers: epidemiological trends and machine learning insights.

European journal of medical research
BACKGROUND: Studies on the epidemiological characteristics, treatment strategies and prognosis of nasal and paranasal sinus cancer are still relatively limited.

Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning.

BMC medical informatics and decision making
BACKGROUND: Cognitive impairment is common after a stroke, but it can often go undetected. In this study, we investigated whether using gait and dual tasks could help detect cognitive impairment after stroke.

Comparative analysis of deep learning architectures for thyroid eye disease detection using facial photographs.

BMC ophthalmology
PURPOSE: To compare two artificial intelligence (AI) models, residual neural networks ResNet-50 and ResNet-101, for screening thyroid eye disease (TED) using frontal face photographs, and to test these models under clinical conditions.

Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches.

Scientific reports
ST-segment elevation myocardial infarction (STEMI) is considered a critical cardiac condition with a poor prognosis. Shortly after STEMI occurs, the increased number of circulating leukocytes including macrophages can lead to the accumulation of more...

AI-based detection and classification of anomalous aortic origin of coronary arteries using coronary CT angiography images.

Nature communications
Anomalous aortic origin of the coronary artery (AAOCA) is a rare cardiac condition that can lead to ischemia or sudden cardiac death, yet it is often overlooked or falsely classified in routine coronary CT angiography (CCTA). Here, we developed, vali...