Cutaneous lupus is a common manifestation of childhood-onset Systemic Lupus Erythematosus (cSLE), affecting up to 85% of patients. Skin involvement can cause irritation, scarring, hair loss, changes in skin color and appearance, which may negatively impact quality of life and mental health. This study aims to assess the impact of cutaneous lupus on quality of life and mental health in diverse pediatric populations, with the goal of identifying disparities and improving individualized care. We will use validated surveys to assess disease burden on quality of life.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.