AIMC Topic: ROC Curve

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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...

An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions.

European radiology
OBJECTIVES: To build an artificial intelligence (AI) system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images.

Differentiation between subchondral insufficiency fractures and advanced osteoarthritis of the knee using transfer learning and an ensemble of convolutional neural networks.

Injury
PURPOSE: Subchondral insufficiency fractures (SIF) and advanced osteoarthritis (OA) of the knee are usually seen in conjunction with bone marrow lesions (BMLs) and their differentiation may pose a significant diagnostic challenge. We aimed to develop...

A deep learning-driven low-power, accurate, and portable platform for rapid detection of COVID-19 using reverse-transcription loop-mediated isothermal amplification.

Scientific reports
This paper presents a deep learning-driven portable, accurate, low-cost, and easy-to-use device to perform Reverse-Transcription Loop-Mediated Isothermal Amplification (RT-LAMP) to facilitate rapid detection of COVID-19. The 3D-printed device-powered...

Artificial Intelligence for Automated Implant Identification in Total Hip Arthroplasty: A Multicenter External Validation Study Exceeding Two Million Plain Radiographs.

The Journal of arthroplasty
BACKGROUND: The surgical management of complications after total hip arthroplasty (THA) necessitates accurate identification of the femoral implant manufacturer and model. Automated image processing using deep learning has been previously developed a...

A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Patients with angle-closure glaucoma (ACG) are asymptomatic until they experience a painful attack. Shallow anterior chamber depth (ACD) is considered a significant risk factor for ACG. We propose a deep learning approach t...