AIMC Topic: Aged
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Surgical Insight-guided Deep Learning for Colorectal Lesion Management.
Surgical laparoscopy, endoscopy & percutaneous techniques
Dec 1, 2024
BACKGROUND: Colonoscopy stands as a pivotal diagnostic tool in identifying gastrointestinal diseases, including potentially malignant tumors. The procedure, however, faces challenges in the precise identification of lesions during visual inspections....
Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western population.
EBioMedicine
Dec 1, 2024
BACKGROUND: Manually extracted imaging-based body composition measures from a single-slice area (A) have shown associations with clinical outcomes in patients with cardiometabolic disease and cancer. With advances in artificial intelligence, fully au...
A Machine Learning-Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults.
Computers, informatics, nursing : CIN
Dec 1, 2024
Fall is a common adverse event among older adults. This study aimed to identify essential fall factors and develop a machine learning-based prediction model to predict the fall risk category among community-dwelling older adults, leading to earlier i...
Machine learning to predict lymph node metastasis in T1 esophageal squamous cell carcinoma: a multicenter study.
International journal of surgery (London, England)
Dec 1, 2024
BACKGROUND: Existing models do poorly when it comes to quantifying the risk of lymph node metastases (LNM). This study aimed to develop a machine-learning model for LNM in patients with T1 esophageal squamous cell carcinoma (ESCC).
Development and validation of a deep learning model for predicting gastric cancer recurrence based on CT imaging: a multicenter study.
International journal of surgery (London, England)
Dec 1, 2024
INTRODUCTION: The postoperative recurrence of gastric cancer (GC) has a significant impact on the overall prognosis of patients. Therefore, accurately predicting the postoperative recurrence of GC is crucial.
Predicting Short-Term Mortality in Patients With Acute Pulmonary Embolism With Deep Learning.
Circulation journal : official journal of the Japanese Circulation Society
Nov 30, 2024
BACKGROUND: Accurate prediction of short-term mortality in patients with acute pulmonary embolism (PE) is critical for optimizing treatment strategies and improving patient outcomes. The Pulmonary Embolism Severity Index (PESI) is the current referen...
External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan.
Japanese journal of radiology
Nov 30, 2024
PURPOSE: Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the co...
Radiation Dosage
Early Detection of Cancer
Radiographic Image Interpretation, Computer-Assisted
Japan
Sensitivity and Specificity
Tomography, X-Ray Computed
Male
Female
Middle Aged
Aged
Lung
Aged, 80 and over
Deep Learning
Lung Neoplasms
Humans
Multiple Pulmonary Nodules
Solitary Pulmonary Nodule
Retrospective Studies
Psychosocial effects of a humanoid robot on informal caregivers of people with dementia: A randomised controlled trial with nested interviews.
International journal of nursing studies
Nov 30, 2024
BACKGROUND: Dementia rates are rising globally, impacting healthcare systems and society. The care of people with dementia is largely provided by informal caregivers (e.g., family, friends), which can present significant challenges and increase careg...
A novel AI-based score for assessing the prognostic value of intra-epithelial lymphocytes in oral epithelial dysplasia.
British journal of cancer
Nov 30, 2024
BACKGROUND: Oral epithelial dysplasia (OED) poses a significant clinical challenge due to its potential for malignant transformation and the lack of reliable prognostic markers. Current OED grading systems do not reliably predict transformation and s...
Predicting sinonasal inverted papilloma attachment using machine learning: Current lessons and future directions.
American journal of otolaryngology
Nov 30, 2024
BACKGROUND: Hyperostosis is a common radiographic feature of inverted papilloma (IP) tumor origin on computed tomography (CT). Herein, we developed a machine learning (ML) model capable of analyzing CT images and identifying IP attachment sites.
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