AIMC Topic: Area Under Curve

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A Short-Duration Gonadotropin-Releasing Hormone Stimulation Test for the Diagnosis of Central Precocious Puberty.

Medicina (Kaunas, Lithuania)
: The gonadotropin-releasing hormone (GnRH) stimulation test is the gold standard method for diagnosing central precocious puberty (CPP), although it requires multiple blood samplings over 120 min. This study aimed to evaluate if a shorter test may h...

Automated detection and segmentation of pleural effusion on ultrasound images using an Attention U-net.

Journal of applied clinical medical physics
BACKGROUND: Ultrasonic for detecting and evaluating pleural effusion is an essential part of the Extended Focused Assessment with Sonography in Trauma (E-FAST) in emergencies. Our study aimed to develop an Artificial Intelligence (AI) diagnostic mode...

Population-Based Artificial Intelligence Assessment of Relationship Between the Risk Factors for Diabetic Retinopathy in Indian Population.

Ophthalmic epidemiology
PURPOSE: Risk factors (RFs), like 'body mass index (BMI),' 'age,' and 'gender' correlate with Diabetic Retinopathy (DR) diagnosis and have been widely studied. This study examines how these three secondary RFs independently affect the predictive capa...

Development of a multivariable prediction model for prolonged air leak after lung resection.

World journal of surgery
OBJECTIVES: Prolonged air leak (PAL) is a common complication of lung resection. Research on predictors of PAL using a digital drainage system (DDS) remains insufficient. In this study, we investigated the predictive factors of PAL to establish a nov...

Attention-based neural networks for clinical prediction modelling on electronic health records.

BMC medical research methodology
BACKGROUND: Deep learning models have had a lot of success in various fields. However, on structured data they have struggled. Here we apply four state-of-the-art supervised deep learning models using the attention mechanism and compare against logis...

Machine learning-based approach for predicting low birth weight.

BMC pregnancy and childbirth
BACKGROUND: Low birth weight (LBW) has been linked to infant mortality. Predicting LBW is a valuable preventative tool and predictor of newborn health risks. The current study employed a machine learning model to predict LBW.

An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection.

International journal of neural systems
To avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggregating the personal data of these drivers may lead to insufficient data usage and pose a risk of priva...

An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: The purpose of this study was to develop a model for predicting chemoradiation response in non-small cell lung cancer (NSCLC) patients by integrating radiomics and deep-learning features and combined intra- and peritumoral regions with pre-t...

Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To develop a MRI-based deep learning signature for predicting axillary response after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients.