AIMC Topic: Machine Learning

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Enhanced Human Crawling Phase Recognition Based on Kinematic Synergies and Machine Learning.

Journal of biomechanical engineering
Hands-and-knees crawling, an effective rehabilitation method for children with motor impairments, requires precise phase detection for optimizing assistive devices. However, research on phase detection in human crawling remains limited. The research ...

A nanozyme colorimetric sensor combined with cloud-based machine learning algorithm-assisted WeChat mini program for intelligent identification of Chinese green tea.

Food research international (Ottawa, Ont.)
Green tea has become increasingly renowned among consumers by virtue of its exceptional flavor and high nutritional value. There is often a strong correlation between the varieties of green tea, quality and corresponding price. In this work, a simple...

Miniaturized spectroscopy and AI-driven probes in food industry automation.

Food research international (Ottawa, Ont.)
Spectroscopy is a rapidly advancing analytical technique, which is increasingly employed in the food industry as a non-destructive and rapid quality control tool. Based on spectral analysis and developed multivariate predictive models this technique ...

Fusion of near-infrared and Raman spectroscopy with machine learning strategies: Non-destructive rapid assessment of freshness and TVB-N value prediction in Pacific white shrimp (Litopenaeus vannamei).

Food research international (Ottawa, Ont.)
Total volatile base nitrogen (TVB-N) is a key indicator of shrimp freshness. Nevertheless, traditional detection methods are cumbersome, time-intensive, and destructive. Here, a rapid and non-destructive method based on near-infrared (NIR) and Raman ...

Artificial intelligence-assisted analysis of musculoskeletal imaging-A narrative review of the current state of machine learning models.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
The potential of Artificial intelligence (AI) is increasingly recognized in musculoskeletal radiology, offering solutions to challenges posed by increasing imaging volumes and fellowship trained radiologist shortages. The integration of AI is not int...

Discriminating Clear Cell From Non-Clear Cell Renal Cell Carcinoma: A Machine Learning Approach Using Contrast-enhanced Ultrasound Radiomics.

Ultrasound in medicine & biology
OBJECTIVE: The aim of this investigation is to assess the clinical usefulness of a machine learning model using contrast-enhanced ultrasound (CEUS) radiomics in discriminating clear cell renal cell carcinoma (ccRCC) from non-ccRCC.

HGMSurvNet: A two-stage hypergraph learning network for multimodal cancer survival prediction.

Medical image analysis
Cancer survival prediction based on multimodal data (e.g., pathological slides, clinical records, and genomic profiles) has become increasingly prevalent in recent years. A key challenge of this task is obtaining an effective survival-specific global...

Applying exposure-response analysis to enhance Mycophenolate Mofetil dosing precision in pediatric patients with immune-mediated renal diseases by machine learning models.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
BACKGROUND: Mycophenolate mofetil (MMF), a cornerstone immunosuppressant for lupus nephritis, is increasingly used off-label in pediatric immune-mediated renal diseases. The aims of this study were to develop and validate pharmacokinetic models for m...

Learnable prototype-guided multiple instance learning for detecting tertiary lymphoid structures in multi-cancer whole-slide pathological images.

Medical image analysis
Tertiary lymphoid structures (TLS) are ectopic lymphoid aggregates that form under specific pathological conditions, such as chronic inflammation and malignancies. Their presence within the tumor microenvironment (TME) is strongly correlated with pat...

Machine Learning Models of Voxel-Level [F] Fluorodeoxyglucose Positron Emission Tomography Data Excel at Predicting Progressive Supranuclear Palsy Pathology.

Annals of neurology
OBJECTIVE: To determine whether a machine learning model of voxel level [f]fluorodeoxyglucose positron emission tomography (PET) data could predict progressive supranuclear palsy (PSP) pathology, as well as outperform currently available biomarkers.