AIMC Topic: ROC Curve

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Ensemble deep learning architectures for detecting pulmonary tuberculosis in chest X-rays.

Scientific reports
Tuberculosis (TB) remains a major global health challenge, causing approximately 1.4 million deaths annually. In many high-burden regions, limited access to expert radiological interpretation leads to delayed or missed diagnoses. To address this, we ...

Early Prediction of Cardiac Arrest Based on Time-Series Vital Signs Using Deep Learning: Retrospective Study.

JMIR formative research
BACKGROUND: Cardiac arrest (CA), characterized by an extremely high mortality rate, remains one of the most pressing global public health challenges. It not only causes a substantial strain on health care systems but also severely impacts individual ...

Sex-specific machine learning models for carotid plaque prediction in individuals with fatty liver disease: a cross-sectional study.

BMJ open
INTRODUCTION: Early detection of carotid plaque prevents stroke and myocardial infarction. Individuals with fatty liver might be at an increased risk of developing carotid plaque, yet limited access to carotid artery ultrasound underscores the need f...

Hypoxemia prediction in pediatric patients under general anesthesia using machine learning: A retrospective observational study and external validation.

PloS one
BACKGROUND: Pediatric patients under general anesthesia are particularly vulnerable to hypoxemia, which can lead to rapid oxygen desaturation. This vulnerability necessitates heightened vigilance from anesthesiologists, making pediatric anesthesia ma...

Deep Learning for Dynamic Prognostic Prediction in Minimally Invasive Surgery for Intracerebral Hemorrhage: Model Development and Validation Study.

JMIR medical informatics
BACKGROUND: The pathological and physiological state of patients with intracerebral hemorrhage (ICH) after minimally invasive surgery (MIS) is a dynamic evolution, and the traditional models cannot dynamically predict prognosis. Clinical data at mult...

Morphological and textural descriptors analysis of digital mammograms with radiological findings to support breast cancer detection using artificial neural networks.

Biomedical physics & engineering express
. To classify digital mammograms based on radiological findings using morphology and texture descriptors with artificial neural networks (ANN) for breast cancer detection.The mammography dataset from High Specialty Regional Hospital of Oaxaca (HRAEO)...

Artificial intelligence-based machine learning models for preoperative diagnosis and staging of ovarian tumors.

Journal of the Egyptian National Cancer Institute
BACKGROUND: Ovarian cancer remains the most lethal gynecological malignancy, necessitating precise diagnostic strategies to improve patient outcomes. This study aims to develop and evaluate machine learning models that utilize patient history, imagin...

Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

Annals of medicine
BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection.

Ultrasound and SWE-based transfer learning for predicting fibrotic NASH.

Scientific reports
The aim of this study was to develop a combined deep-learning model utilizing liver ultrasound, liver elastography images, and clinical features to predict and diagnose fibrotic non-alcoholic steatohepatitis (NASH). A rat model of liver steatosis and...

Deciphering the clinical implication of an obesity-related gene signature as the novel biomarker for acute myocardial infarction diagnosis.

Scientific reports
Acute myocardial infarction (AMI) stands as a major contributor to mortality and disability worldwide, with obesity playing a significant role in its development. This study aims to investigate potential biomarkers associated with obesity-related gen...