AIMC Topic: ROC Curve

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Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.

Breast cancer research : BCR
BACKGROUND: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography conside...

Machine learning algorithms to predict colistin-induced nephrotoxicity from electronic health records in patients with multidrug-resistant Gram-negative infection.

International journal of antimicrobial agents
OBJECTIVES: Colistin-induced nephrotoxicity prolongs hospitalisation and increases mortality. The study aimed to construct machine learning models to predict colistin-induced nephrotoxicity in patients with multidrug-resistant Gram-negative infection...

Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network.

Medical & biological engineering & computing
A tissue sample is a valuable resource for understanding a patient's symptoms and health status in relation to tumor growth. Recent research seeks to establish a connection between tissue-specific tumor samples and genetic markers (genes). This break...

Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.

Respiratory research
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep lear...

Development and Internal Validation of a Multivariable Prediction Model for Mortality After Hip Fracture with Machine Learning Techniques.

Calcified tissue international
In order to estimate the likelihood of 1, 3, 6 and 12 month mortality in patients with hip fractures, we applied a variety of machine learning methods using readily available, preoperative data. We used prospectively collected data from a single univ...

Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning.

Nature medicine
Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we develope...

BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs.

Molecules (Basel, Switzerland)
Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug-ADR associations often entails expensive and time-consuming investigations. Thus, a computational model t...

Sex estimation from maxillofacial radiographs using a deep learning approach.

Dental materials journal
The purpose of this study was to construct deep learning models for more efficient and reliable sex estimation. Two deep learning models, VGG16 and DenseNet-121, were used in this retrospective study. In total, 600 lateral cephalograms were analyzed....

Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks.

Sensors (Basel, Switzerland)
Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of p...

Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis.

Ultraschall in der Medizin (Stuttgart, Germany : 1980)
PURPOSE: To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis.