AIMC Topic: Humans

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Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.

Scientific reports
Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with de...

Safely and autonomously cutting meat with a collaborative robot arm.

Scientific reports
Labor shortages in the United States are impacting a number of industries including the meat processing sector. Collaborative technologies that work alongside humans while increasing production abilities may support the industry by enhancing automati...

Potential diagnostic application of a novel deep learning- based approach for COVID-19.

Scientific reports
COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilitie...

Autonomous swab robot for naso- and oropharyngeal COVID-19 screening.

Scientific reports
The COVID-19 outbreak has triggered a global health and economic crisis, necessitating widespread testing to control viral spread amidst rising cases and fatalities. The recommended testing method, a combined naso- and oropharyngeal swab, poses risks...

Deep learning for transesophageal echocardiography view classification.

Scientific reports
Transesophageal echocardiography (TEE) imaging is a vital tool used in the evaluation of complex cardiac pathology and the management of cardiac surgery patients. A key limitation to the application of deep learning strategies to intraoperative and i...

MAPS: pathologist-level cell type annotation from tissue images through machine learning.

Nature communications
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resour...

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine.

Scientific reports
Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learn...

A robust deep learning detector for sleep spindles and K-complexes: towards population norms.

Scientific reports
Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies would benefit from precise and robust automatic sleep event detectors, capable of adapting the variabili...

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tom...

A publicly available newborn ear shape dataset for medical diagnosis of auricular deformities.

Scientific data
Early and accurate diagnosis of ear deformities in newborns is crucial for an effective non-surgical correction treatment, since this commonly seen ear anomalies would affect aesthetics and cause mental problems if untreated. It is not easy even for ...