AIMC Topic: Area Under Curve

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Deep learning and radiomics of longitudinal CT scans for early prediction of tuberculosis treatment outcomes.

European journal of radiology
BACKGROUND: To predict tuberculosis (TB) treatment outcomes at an early stage, prevent poor outcomes ofdrug-resistant tuberculosis(DR-TB) and interrupt transmission.

A transformer-based multi-task deep learning model for simultaneous infiltrated brain area identification and segmentation of gliomas.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: The anatomical infiltrated brain area and the boundaries of gliomas have a significant impact on clinical decision making and available treatment options. Identifying glioma-infiltrated brain areas and delineating the tumor manually is a ...

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review.

Journal of medical Internet research
BACKGROUND: Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that th...

Classification of mathematical test questions using machine learning on datasets of learning management system questions.

PloS one
Every student has a varied level of mathematical proficiency. Therefore, it is important to provide them with questions accordingly. Owing to advances in technology and artificial intelligence, the Learning Management System (LMS) has become a popula...

A Combined Model Integrating Radiomics and Deep Learning Based on Contrast-Enhanced CT for Preoperative Staging of Laryngeal Carcinoma.

Academic radiology
RATIONALE AND OBJECTIVES: Accurate staging of laryngeal carcinoma can inform appropriate treatment decision-making. We developed a radiomics model, a deep learning (DL) model, and a combined model (incorporating radiomics features and DL features) ba...

Development and Validation of a Deep Learning and Radiomics Combined Model for Differentiating Complicated From Uncomplicated Acute Appendicitis.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to develop and validate a deep learning and radiomics combined model for differentiating complicated from uncomplicated acute appendicitis (AA).

Few-shot out-of-distribution detection for automated screening in retinal OCT images using deep learning.

Scientific reports
Deep neural networks have been increasingly proposed for automated screening and diagnosis of retinal diseases from optical coherence tomography (OCT), but often provide high-confidence predictions on out-of-distribution (OOD) cases, compromising the...

Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity.

Scientific reports
Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 p...

A large-scale evaluation of NLP-derived chemical-gene/protein relationships from the scientific literature: Implications for knowledge graph construction.

PloS one
One area of active research is the use of natural language processing (NLP) to mine biomedical texts for sets of triples (subject-predicate-object) for knowledge graph (KG) construction. While statistical methods to mine co-occurrences of entities wi...

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques.

Sensors (Basel, Switzerland)
Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmen...