AIMC Topic: Middle Aged
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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...
Radiographic Image Interpretation, Computer-Assisted
Japan
Radiation Dosage
Male
Female
Lung Neoplasms
Humans
Middle Aged
Aged
Retrospective Studies
Lung
Multiple Pulmonary Nodules
Aged, 80 and over
Tomography, X-Ray Computed
Deep Learning
Solitary Pulmonary Nodule
Sensitivity and Specificity
Early Detection of Cancer
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.
Construction and validation of a machine learning-based prediction model for short-term mortality in critically ill patients with liver cirrhosis.
Clinics and research in hepatology and gastroenterology
Nov 30, 2024
OBJECTIVE: Critically ill patients with liver cirrhosis generally have a poor prognosis due to complications such as multiple organ failure. This study aims to develop a machine learning-based prediction model to forecast short-term mortality in crit...
Machine learning is better than surgeons at assessing unicompartmental knee replacement radiographs.
The Knee
Nov 30, 2024
BACKGROUND: Poor results occasionally occur after unicompartmental knee replacement (UKR). It is often difficult, even for experienced surgeons, to determine why patients have poor outcomes from radiographs. The aim was to compare the ability of expe...
Integrating Metabolomics Domain Knowledge with Explainable Machine Learning in Atherosclerotic Cardiovascular Disease Classification.
International journal of molecular sciences
Nov 30, 2024
Metabolomic data often present challenges due to high dimensionality, collinearity, and variability in metabolite concentrations. Machine learning (ML) application in metabolomic analyses is enabling the extraction of meaningful information from comp...
Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea.
Scientific reports
Nov 30, 2024
This study aimed to develop and validate a machine learning (ML)-based model for predicting liposuction volumes in patients with obesity. This study used longitudinal cohort data from 2018 to 2023 from five nationwide centers affiliated with 365MC Li...
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