AI Medical Compendium Topic:
ROC Curve

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AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data.

Journal of biomedical informatics
BACKGROUND: Medical decision-making impacts both individual and public health. Clinical scores are commonly used among various decision-making models to determine the degree of disease deterioration at the bedside. AutoScore was proposed as a useful ...

Assessment of deep learning assistance for the pathological diagnosis of gastric cancer.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replace the breadth and contextual knowledge of pathologists; rather, only through their combination may t...

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning.

Scientific reports
Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from ...

Temporal shift and predictive performance of machine learning for heart transplant outcomes.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
BACKGROUND: Outcome prediction following heart transplant is critical to explaining risks and benefits to patients and decision-making when considering potential organ offers. Given the large number of potential variables to be considered, this task ...

Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study.

International journal of paediatric dentistry
BACKGROUND: Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth.

Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set.

BMC medical imaging
BACKGROUND: Enteral nutrition through feeding tubes serves as the primary method of nutritional supplementation for patients unable to feed themselves. Plain radiographs are routinely used to confirm the position of the Nasoenteric feeding tubes the ...

Diagnosis of significant liver fibrosis in patients with chronic hepatitis B using a deep learning-based data integration network.

Hepatology international
BACKGROUND AND AIMS: Chronic hepatitis B virus (CHB) infection remains a major global health burden and the non-invasive and accurate diagnosis of significant liver fibrosis (≥ F2) in CHB patients is clinically very important. This study aimed to ass...

Machine Learning Improves Prediction Over Logistic Regression on Resected Colon Cancer Patients.

The Journal of surgical research
INTRODUCTION: Despite advances, readmission and mortality rates for surgical patients with colon cancer remain high. Prediction models using regression techniques allows for risk stratification to aid periprocedural care. Technological advances have ...

Development of Various Diabetes Prediction Models Using Machine Learning Techniques.

Diabetes & metabolism journal
BACKGROUND: There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters.

Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks.

Sensors (Basel, Switzerland)
Ireland has a wide variety of farmlands that includes arable fields, grassland, hedgerows, streams, lakes, rivers, and native woodlands. Traditional methods of habitat identification rely on field surveys, which are resource intensive, therefore ther...