AIMC Topic: Humans

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Detection of Image Level Forgery with Various Constraints Using DFDC Full and Sample Datasets.

Sensors (Basel, Switzerland)
The emergence of advanced machine learning or deep learning techniques such as autoencoders and generative adversarial networks, can generate images known as deepfakes, which astonishingly resemble the realistic images. These deepfake images are hard...

Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China.

BMC public health
BACKGROUND: This study aims to explore appropriate model for predicting the disease burden of pneumoconiosis in Tianjin by comparing the prediction effects of Autoregressive Integrated Moving Average (ARIMA) model, Deep Neural Networks (DNN) model an...

Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors.

BMC cancer
BACKGROUND: Vast amounts of rapidly accumulating biological data related to cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is C...

Contrast phase recognition in liver computer tomography using deep learning.

Scientific reports
Hepatocellular carcinoma (HCC) has become the 4th leading cause of cancer-related deaths, with high social, economical and health implications. Imaging techniques such as multiphase computed tomography (CT) have been successfully used for diagnosis o...

DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.

Communications biology
The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the druggability likelihood for every protein-coding gene in the human ex...

Interpretable brain disease classification and relevance-guided deep learning.

Scientific reports
Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by humans. Heat mapping by deep Taylor decomposition revealed that (potentially misleading) image feat...

Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images.

Canadian respiratory journal
BACKGROUND AND AIMS: Chest X-ray (CXR) is indispensable to the assessment of severity, diagnosis, and management of pneumonia. Deep learning is an artificial intelligence (AI) technology that has been applied to the interpretation of medical images. ...

A Single-Institution Comparison of Robot-Assisted and Conventional Laparoscopic Colorectal Surgeries.

The American surgeon
There has been ongoing discussion regarding the superiority of robotic laparoscopic surgery (RLS) over conventional laparoscopic surgery (CLS) in many surgical subspecialties. We therefore sought to elucidate if RLS is associated with more favorable ...

Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis.

Journal of neurointerventional surgery
BACKGROUND: This study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH).