AIMC Topic: Humans

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Improved switching condition for reachable set estimation of discrete-time switched delayed neural networks.

Neural networks : the official journal of the International Neural Network Society
This research delves into the reachable set estimation (RSE) problem for general switched delayed neural networks (SDNNs) in the discrete-time context. Note that existing relevant research on SDNNs predominantly relies on either time-dependent or sta...

Boosting integral-based human pose estimation through implicit heatmap learning.

Neural networks : the official journal of the International Neural Network Society
Human pose estimation typically encompasses three categories: heatmap-, regression-, and integral-based methods. While integral-based methods possess advantages such as end-to-end learning, full-convolution learning, and being free from quantization ...

Smell cancer by machine learning-assisted peptide/MXene bioelectronic array.

Biosensors & bioelectronics
Non-invasive detection of tumors is of utmost importance to save lives. Nonetheless, identifying tumors through gas analysis is a challenging task. In this work, biosensors with remarkable gas-sensing characteristics were developed using a self-assem...

INDELpred: Improving the prediction and interpretation of indel pathogenicity within the clinical genome.

HGG advances
Small insertions and deletions (indels) are critical yet challenging genetic variations with significant clinical implications. However, the identification of pathogenic indels from neutral variants in clinical contexts remains an understudied proble...

Response to "Harnessing artificial intelligence in bariatric surgery: correspondence".

Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery

A spatio-temporal graph convolutional network for ultrasound echocardiographic landmark detection.

Medical image analysis
Landmark detection is a crucial task in medical image analysis, with applications across various fields. However, current methods struggle to accurately locate landmarks in medical images with blurred tissue boundaries due to low image quality. In pa...

Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study.

International journal of medical informatics
BACKGROUND: Diabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early detection and recognition of patients vulnerable to progression is crucial.

Computational approaches for lead compound discovery in dipeptidyl peptidase-4 inhibition using machine learning and molecular dynamics techniques.

Computational biology and chemistry
The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Melli...

Towards multimodal graph neural networks for surgical instrument anticipation.

International journal of computer assisted radiology and surgery
PURPOSE: Decision support systems and context-aware assistance in the operating room have emerged as the key clinical applications supporting surgeons in their daily work and are generally based on single modalities. The model- and knowledge-based in...

Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
PURPOSE: This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for stratifying osteoporosis and osteopenia risk by evaluating vertebral trabecular bone structure in CT sc...