AIMC Topic: Humans

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Automated pneumothorax triaging in chest X-rays in the New Zealand population using deep-learning algorithms.

Journal of medical imaging and radiation oncology
INTRODUCTION: The primary aim was to develop convolutional neural network (CNN)-based artificial intelligence (AI) models for pneumothorax classification and segmentation for automated chest X-ray (CXR) triaging. A secondary aim was to perform interp...

Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts.

Journal of gastroenterology
BACKGROUND: Ultrasonography (US) is widely used for the diagnosis of liver tumors. However, the accuracy of the diagnosis largely depends on the visual perception of humans. Hence, we aimed to construct artificial intelligence (AI) models for the dia...

Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening.

Future medicinal chemistry
Since December 2019, SARS-CoV-2 has continued to spread rapidly around the world. The effective drugs may provide a long-term strategy to combat this virus. The main protease (Mpro) and papain-like protease (PLpro) are two important targets for the ...

AI Enabled Bridge Bidding Supporting Interactive Visualization.

Sensors (Basel, Switzerland)
With the fast progress in perfect information game problems such as AI chess and AI Go, researchers have turned to imperfect information game problems, including Texas Hold'em and Bridge. Bridge is one of the most challenging card games that have sig...

Comparative analysis of explainable machine learning prediction models for hospital mortality.

BMC medical research methodology
BACKGROUND: Machine learning (ML) holds the promise of becoming an essential tool for utilising the increasing amount of clinical data available for analysis and clinical decision support. However, the lack of trust in the models has limited the acce...

Grayscale medical image segmentation method based on 2D&3D object detection with deep learning.

BMC medical imaging
BACKGROUND: Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extractio...

Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care.

Contrast media & molecular imaging
Presently, colorectal cancer is the second most dangerous cancer; around 13% of people have been affected; and it requires an effective image analysis and earlier cancer prediction (IAECP) system for reducing the mortality rate. Here, the IAECP syste...

Detection of Emotion of Speech for RAVDESS Audio Using Hybrid Convolution Neural Network.

Journal of healthcare engineering
Every human being has emotion for every item related to them. For every customer, their emotion can help the customer representative to understand their requirement. So, speech emotion recognition plays an important role in the interaction between hu...

A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network.

Journal of healthcare engineering
BACKGROUND: The liver is one of the most significant and most essential organs in the human body. It is divided into two granular lobes, one on the right and one on the left, connected by a bile duct. The liver is essential in the removal of waste pr...

A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning.

Journal of healthcare engineering
Vaginitis is a gynecological disease affecting the health of millions of women all over the world. The traditional diagnosis of vaginitis is based on manual microscopy, which is time-consuming and tedious. The deep learning method offers a fast and r...