AIMC Topic: ROC Curve

Clear Filters Showing 231 to 240 of 3585 articles

Predicting the molecular subtypes of 2021 WHO grade 4 glioma by a multiparametric MRI-based machine learning model.

BMC cancer
BACKGROUND: Accurately distinguishing the different molecular subtypes of 2021 World Health Organization (WHO) grade 4 Central Nervous System (CNS) gliomas is highly relevant for prognostic stratification and personalized treatment.

Integrated bioinformatics and machine learning reveal key genes and immune mechanisms associated with uremia.

Scientific reports
Uremia is a serious complication of end-stage chronic kidney disease, closely associated with immune imbalance and chronic inflammation. However, its molecular mechanisms remain largely unclear. In this study, we analyzed transcriptomic data from the...

Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy.

Scientific reports
Breast cancer continues to be a leading cause of death among women in the world. The prediction of survival outcomes based on treatment modalities, i.e., chemotherapy, hormone therapy, surgery, and radiation therapy is an essential step towards perso...

Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage.

Scientific reports
Lower extremity deep vein thrombosis is one of the important complications of spontaneous intracerebral hemorrhage. We aimed to develop a risk assessment model to predict the risk of lower extremity DVT during hospitalization in patients with spontan...

Recurrence prediction of invasive ductal carcinoma from preoperative contrast-enhanced computed tomography using deep convolutional neural network.

Biomedical physics & engineering express
Predicting the risk of breast cancer recurrence is crucial for guiding therapeutic strategies, including enhanced surveillance and the consideration of additional treatment after surgery. In this study, we developed a deep convolutional neural networ...

BIScreener: enhancing breast cancer ultrasound diagnosis through integrated deep learning with interpretability.

Analytical methods : advancing methods and applications
Breast cancer is the leading cause of death among women worldwide, and early detection through the standardized BI-RADS framework helps physicians assess the risk of malignancy and guide appropriate diagnostic and treatment decisions. In this study, ...

Development of a deep learning-based MRI diagnostic model for human Brucella spondylitis.

Biomedical engineering online
INTRODUCTION: Brucella spondylitis (BS) and tuberculous spondylitis (TS) are prevalent spinal infections with distinct treatment protocols. Rapid and accurate differentiation between these two conditions is crucial for effective clinical management; ...

Machine learning models for predicting multimorbidity trajectories in middle-aged and elderly adults.

Scientific reports
Multimorbidity has emerged as a significant public health issue in the context of global population aging. Predicting and managing the progression of multimorbidity in the elderly population is crucial. This study aims to develop predictive models fo...

Integrative multimodal ultrasound and radiomics for early prediction of neoadjuvant therapy response in breast cancer: a clinical study.

BMC cancer
PURPOSE: This study aimed to develop an early predictive model for neoadjuvant therapy (NAT) response in breast cancer by integrating multimodal ultrasound (conventional B-mode, shear-wave elastography, and contrast-enhanced ultrasound) and radiomics...

Deep learning-based video analysis for automatically detecting penetration and aspiration in videofluoroscopic swallowing study.

Scientific reports
The videofluoroscopic swallowing study (VFSS) is the gold standard for diagnosing dysphagia, but its interpretation is time-consuming and requires expertise. This study developed a deep learning model for automatically detecting penetration and aspir...