AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

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Mortality prediction in ICU Using a Stacked Ensemble Model.

Computational and mathematical methods in medicine
Artificial intelligence (AI) technology has huge scope in developing models to predict the survival rate of critically ill patients in the intensive care unit (ICU). The availability of electronic clinical data has led to the widespread use of variou...

Preoperative prediction of lymph node status in patients with colorectal cancer. Developing a predictive model using machine learning.

International journal of colorectal disease
PURPOSE: Develop a prediction model to determine the probability of no lymph node metastasis (pN0) in patients with colorectal cancer.

Deep multiple instance learning for predicting chemotherapy response in non-small cell lung cancer using pretreatment CT images.

Scientific reports
The individual prognosis of chemotherapy is quite different in non-small cell lung cancer (NSCLC). There is an urgent need to precisely predict and assess the treatment response. To develop a deep multiple-instance learning (DMIL) based model for pre...

Detection of mandibular fractures on panoramic radiographs using deep learning.

Scientific reports
Mandibular fractures are among the most frequent facial traumas in oral and maxillofacial surgery, accounting for 57% of cases. An accurate diagnosis and appropriate treatment plan are vital in achieving optimal re-establishment of occlusion, functio...

Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND: The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (A...

A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.

BMC bioinformatics
BACKGROUND: Selecting and prioritizing candidate disease genes is necessary before conducting laboratory studies as identifying disease genes from a large number of candidate genes using laboratory methods, is a very costly and time-consuming task. T...

Diagnostic performance of convolutional neural networks for dental sexual dimorphism.

Scientific reports
Convolutional neural networks (CNN) led to important solutions in the field of Computer Vision. More recently, forensic sciences benefited from the resources of artificial intelligence, especially in procedures that normally require operator-dependen...

A Fully Deep Learning Paradigm for Pneumoconiosis Staging on Chest Radiographs.

IEEE journal of biomedical and health informatics
Pneumoconiosis staging has been a very challenging task, both for certified radiologists and computer-aided detection algorithms. Although deep learning has shown proven advantages in the detection of pneumoconiosis, it remains challenging in pneumoc...

Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.

Tomography (Ann Arbor, Mich.)
Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective:...

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.

International journal of environmental research and public health
Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an incr...