AIMC Topic: Area Under Curve

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A Weakly Supervised Deep Learning Approach for Leakage Detection in Fluorescein Angiography Images.

Translational vision science & technology
PURPOSE: The purpose of this study was to design an automated algorithm that can detect fluorescence leakage accurately and quickly without the use of a large amount of labeled data.

Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVES: To develop and validate an artificial intelligence system, the Prenatal ultrasound diagnosis Artificial Intelligence Conduct System (PAICS), to detect different patterns of fetal intracranial abnormality in standard sonographic reference ...

Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest.

Critical care medicine
OBJECTIVES: Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predict...

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.

Briefings in bioinformatics
More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which...

Comparative analysis of machine learning approaches for predicting frequent emergency department visits.

Health informatics journal
BACKGROUND: Emergency Department (ED) overcrowding is an emerging risk to patient safety. This study aims to assess and compare the predictive ability of machine learning (ML) models for predicting frequent ED users.

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part III-Model Evaluation and Other Points of Significance.

Acta neurochirurgica. Supplement
Various available metrics to describe model performance in terms of discrimination (area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 Score) and calibration (slope, intercept, Bri...

Novel artificial intelligence approach for automatic differentiation of fetal occiput anterior and non-occiput anterior positions during labor.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVES: To describe a newly developed machine-learning (ML) algorithm for the automatic recognition of fetal head position using transperineal ultrasound (TPU) during the second stage of labor and to describe its performance in differentiating be...

Prediction of seroma after total mastectomy using an artificial neural network algorithm.

Breast disease
Seroma is a common complication after mastectomy. To the best of our knowledge, no prediction models have been developed for this. Henceforth, medical records of total mastectomy patients were retrospectively reviewed. Data consisting of 120 subjects...

Single-Examination Risk Prediction of Severe Retinopathy of Prematurity.

Pediatrics
BACKGROUND AND OBJECTIVES: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but requires multiple examinations of infants, most of whom will not develop severe disease. Previous wo...