AIMC Topic: Retrospective Studies
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Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.
PloS one
Nov 11, 2024
BACKGROUND AND PURPOSE: Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality an...
Building a machine learning-based risk prediction model for second-trimester miscarriage.
BMC pregnancy and childbirth
Nov 9, 2024
BACKGROUND: Second-trimester miscarriage is a common adverse pregnancy outcome that imposes substantial economic and psychological pressures on both the physical and mental well-being of patients and their families. Currently, there is a scarcity of ...
Large-scale multi-center CT and MRI segmentation of pancreas with deep learning.
Medical image analysis
Nov 8, 2024
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, la...
Measurement of ureteral length: Comparison of deep learning-based method and other estimation methods on CT and KUB.
Computers in biology and medicine
Nov 8, 2024
BACKGROUND: Accurate preoperative assessment of ureteral length is crucial for effective ureteral stenting.
An Artificial Intelligence Approach for Test-Free Identification of Sarcopenia.
Journal of cachexia, sarcopenia and muscle
Nov 8, 2024
BACKGROUND: The diagnosis of sarcopenia relies extensively on human and equipment resources and requires individuals to personally visit medical institutions. The objective of this study was to develop a test-free, self-assessable approach to identif...
Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study.
BMC musculoskeletal disorders
Nov 8, 2024
BACKGROUND: Predicting the progression of hip osteoarthritis (OA) remains challenging, and no reliable predictive method has been established. This study aimed to develop an artificial intelligence (AI) model to predict hip OA progression via plain r...
An explainable deep learning model to predict partial anomalous pulmonary venous connection for patients with atrial septal defect.
BMC pediatrics
Nov 8, 2024
BACKGROUND: Patients with partial anomalous pulmonary venous connection (PAPVC) usually present asymptomatic and accompanied by intricate anatomical types, which results in missed diagnosis from atrial septal defect (ASD). The present study aimed to ...
Artificial intelligence algorithms enhance urine cytology reporting confidence in postoperative follow-up for upper urinary tract urothelial carcinoma.
International urology and nephrology
Nov 7, 2024
PURPOSE: In Taiwan, the incidence of urothelial carcinoma of the upper urinary tract (UTUC) is high and intravesical recurrence is approximately 22%-47%. Thus, postoperative cystoscopy and urine cytology follow-up, which require experienced cytologis...
Deep learning automatically distinguishes myocarditis patients from normal subjects based on MRI.
The international journal of cardiovascular imaging
Nov 7, 2024
Myocarditis, characterized by inflammation of the myocardial tissue, presents substantial risks to cardiovascular functionality, potentially precipitating critical outcomes including heart failure and arrhythmias. This investigation primarily aims to...
Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence.
Scientific reports
Nov 7, 2024
This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-instrument training dataset composed of Optical coherence tomography-angiography (OCTA) images from tw...
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