AIMC Topic: Humans

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AI support for colonoscopy quality control using CNN and transformer architectures.

BMC gastroenterology
BACKGROUND: Construct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms.

Admission blood tests predicting survival of SARS-CoV-2 infected patients: a practical implementation of graph convolution network in imbalance dataset.

BMC infectious diseases
BACKGROUND: Predicting an individual's risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accur...

Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence.

Scientific reports
The emergence of generative AI technologies has led to an increasing number of people collaborating with AI to produce creative works. Across two experimental studies, in which we carefully designed and programmed state-of-the-art human-AI interfaces...

Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection.

Scientific reports
Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. These problems include dedu...

Performance of machine learning algorithms for lung cancer prediction: a comparative approach.

Scientific reports
Due to the excessive growth of PM 2.5 in aerosol, the cases of lung cancer are increasing rapidly and are most severe among other types as the highest mortality rate. In most of the cases, lung cancer is detected with least symptoms at its later stag...

Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction.

Physics in medicine and biology
Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied p...

Universal optimal design in the vertebrate limb pattern and lessons for bioinspired design.

Bioinspiration & biomimetics
This paper broadly summarizes the variation of design features found in vertebrate limbs and analyses the resultant versatility and multifunctionality in order to make recommendations for bioinspired robotics. The vertebrate limb pattern (e.g. should...

A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Following endovascular thrombectomy in patients with large-vessel occlusion stroke, successful recanalization from 1 attempt, known as the first-pass effect, has correlated favorably with long-term outcomes. Pretreatment imagi...

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is tim...

Guiding questions to avoid data leakage in biological machine learning applications.

Nature methods
Machine learning methods for extracting patterns from high-dimensional data are very important in the biological sciences. However, in certain cases, real-world applications cannot confirm the reported prediction performance. One of the main reasons ...