AIMC Topic: Humans

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Human Skin-Mimicking Ionogel-Based Electronic Skin for Intelligent Robotic Sorting.

Macromolecular rapid communications
Creating bionic intelligent robotic systems that emulate human-like skin perception presents a considerable scientific challenge. This study introduces a multifunctional bionic electronic skin (e-skin) made from polyacrylic acid ionogel (PAIG), desig...

Identification of ChatGPT-Generated Abstracts Within Shoulder and Elbow Surgery Poses a Challenge for Reviewers.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE: To evaluate the extent to which experienced reviewers can accurately discern between artificial intelligence (AI)-generated and original research abstracts published in the field of shoulder and elbow surgery and compare this with the perfor...

How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
Artificial intelligence (AI) refers to computer-based methodologies that use data to teach a computer to solve pre-defined tasks; these methods can be applied to identify patterns in large multi-modal data sources. AI applications in inflammatory bow...

CapNet: An Automatic Attention-Based with Mixer Model for Cardiovascular Magnetic Resonance Image Segmentation.

Journal of imaging informatics in medicine
Deep neural networks have shown excellent performance in medical image segmentation, especially for cardiac images. Transformer-based models, though having advantages over convolutional neural networks due to the ability of long-range dependence lear...

Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline.

Journal of imaging informatics in medicine
De-identification of medical images intended for research is a core requirement for data sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Informati...

Deep Learning-Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study.

Journal of imaging informatics in medicine
Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs were retr...

Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence.

Nature reviews. Urology
Diagnostic cystoscopy in combination with transurethral resection of the bladder tumour are the standard for the diagnosis, surgical treatment and surveillance of bladder cancer. The ability to inspect the bladder in its current form stems from a lon...

AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
PURPOSE: Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools' accura...

Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.

ESC heart failure
AIMS: Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data...

Structural prior-driven feature extraction with gradient-momentum combined optimization for convolutional neural network image classification.

Neural networks : the official journal of the International Neural Network Society
Recent image classification efforts have achieved certain success by incorporating prior information such as labels and logical rules to learn discriminative features. However, these methods overlook the variability of features, resulting in feature ...