AIMC Topic: Humans

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Prediction of transfusion risk after total knee arthroplasty: use of a machine learning algorithm.

Orthopaedics & traumatology, surgery & research : OTSR
INTRODUCTION: Total knee arthroplasty (TKA) carries a significant hemorrhagic risk, with a non-negligible rate of postoperative transfusions. The blood-sparing strategy has evolved to reduce blood loss after TKA by identifying the patient's risk fact...

Comparison of Pathologist and Artificial Intelligence-based Grading for Prediction of Metastatic Outcomes After Radical Prostatectomy.

European urology oncology
Gleason grade group (GG) is the most powerful prognostic variable in localized prostate cancer; however, interobserver variability remains a challenge. Artificial intelligence algorithms applied to histopathologic images standardize grading, but most...

Volume Measurements for Surveillance after Endovascular Aneurysm Repair using Artificial Intelligence.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
OBJECTIVE: Surveillance after endovascular aneurysm repair (EVAR) is suboptimal due to limited compliance and relatively large variability in measurement methods of abdominal aortic aneurysm (AAA) sac size after treatment. Measuring volume offers a m...

Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.

Journal of environmental science and health. Part C, Toxicology and carcinogenesis
The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effect...

The application value of Rs-fMRI-based machine learning models for differentiating mild cognitive impairment from Alzheimer's disease: a systematic review and meta-analysis.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND: Various machine learning (ML) models based on resting-state functional MRI (Rs-fMRI) have been developed to facilitate differential diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the diagnostic accurac...

Social anxiety prediction based on ERP features: A deep learning approach.

Journal of affective disorders
BACKGROUND: Social Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained importance for anxiety detection due to its ability to capture stable and objective neurophysiological ...

Towards a configurable and non-hierarchical search space for NAS.

Neural networks : the official journal of the International Neural Network Society
Neural Architecture Search (NAS) outperforms handcrafted Neural Network (NN) design. However, current NAS methods generally use hard-coded search spaces, and predefined hierarchical architectures. As a consequence, adapting them to a new problem can ...

Sample selection of adversarial attacks against traffic signs.

Neural networks : the official journal of the International Neural Network Society
In the real world, the correct recognition of traffic signs plays a crucial role in vehicle autonomous driving and traffic monitoring. The research on its adversarial attack can test the security of vehicle autonomous driving system and provide enlig...

State transition learning with limited data for safe control of switched nonlinear systems.

Neural networks : the official journal of the International Neural Network Society
Switching dynamics are prevalent in real-world systems, arising from either intrinsic changes or responses to external influences, which can be appropriately modeled by switched systems. Control synthesis for switched systems, especially integrating ...

Learning clustering-friendly representations via partial information discrimination and cross-level interaction.

Neural networks : the official journal of the International Neural Network Society
Despite significant advances in the deep clustering research, there remain three critical limitations to most of the existing approaches. First, they often derive the clustering result by associating some distribution-based loss to specific network l...