Accurate prediction of difficult direct laryngoscopy (DDL) is essential to ensure optimal airway management and patient safety. The present study proposed an AI model that would accurately predict DDL using a small number of bedside pictures of the p...
Hearing problems are commonly diagnosed with the use of tonal audiometry, which measures a patient's hearing threshold in both air and bone conduction at various frequencies. Results of audiometry tests, usually represented graphically in the form of...
This study aimed to develop a machine learning (ML)-based tool for early and accurate prediction of in-hospital mortality risk in patients with spontaneous intracerebral hemorrhage (sICH) in the intensive care unit (ICU). We did a retrospective study...
BACKGROUND: Deep learning (DL) models for auto-segmentation in radiotherapy have been extensively studied in retrospective and pilot settings. However, these studies might not reflect the clinical setting. This study compares the use of a clinically ...
BACKGROUND/AIMS: To assess the performance of deep-learning (DL) models for prediction of conversion to normal-tension glaucoma (NTG) in normotensive glaucoma suspect (GS) patients.
BACKGROUND: Ultrasound imaging is suitable for detecting and diagnosing ophthalmic abnormalities. However, a shortage of experienced sonographers and ophthalmologists remains a problem. This study aims to develop a multibranch transformer network (MB...
BACKGROUND/AIMS: Support vector machine-based automated grading (known as iGradingM) has been shown to be safe, cost-effective and robust in the diabetic retinopathy (DR) screening (DES) programme in Scotland. It triages screening episodes as gradabl...
AIM: To explore the status of job crafting, leisure crafting, and burnout among nurses and to examine the impact of job crafting and leisure crafting variations on burnout using machine learning-based models.
European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP)
Jun 19, 2024
OBJECTIVE: The objective of this study is to develop and validate a multiparametric MRI model employing machine learning to predict the effectiveness of treatment and the stage of breast cancer.
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