AIMC Topic: Humans

Clear Filters Showing 7291 to 7300 of 95995 articles

Artificial intelligence-based Raynaud's quantification index (ARTIX): an objective mobile-based tool for patient-centered assessment of Raynaud's phenomenon.

Arthritis research & therapy
BACKGROUND: We aimed to develop an artificial intelligence algorithm able to assess Raynaud's phenomenon (RP) from mobile phone photography, ensuring as a patient-centered, image-based method for RP quantification.

Construction and validation of a prognostic nomogram model integrating machine learning-pathomics and clinical features in IDH-wildtype glioblastoma.

Journal of translational medicine
BACKGROUND: Novel diagnostic criteria for glioblastoma (GBM) in the 2021 WHO classification emphasize the importance of integrating pathological and molecular features. Pathomics, which involves the extraction of digital pathology data, is gaining si...

DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform.

Genome biology
The rapid advancements in spatially resolved transcriptomics (SRT) enable the characterization of gene expressions while preserving spatial information. However, high dropout rates and noise hinder accurate spatial domain identification for understan...

Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis.

BMC oral health
OBJECTIVES: Accurate preoperative discrimination of salivary gland pleomorphic adenoma (SPA) stromal subtypes is essential for therapeutic plannings. We aimed to establish and test machine learning (ML) models for classification of stromal subtypes i...

The potentials and challenges of integrating generative artificial intelligence (AI) in dental and orthodontic education: a systematic review.

BMC oral health
BACKGROUND: Generative AI technologies offer significant opportunities to enhance orthodontic education by improving knowledge retention, clinical decision-making, and skills training. This systematic review aimed to evaluate the impact of generative...

Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning.

BMC medical informatics and decision making
BACKGROUND: Fatigue is a prevalent and debilitating symptom of non-communicable diseases (NCDs); however, its biological basis are not well-defined. This exploratory study aimed to identify key biological drivers of fatigue by integrating metabolomic...

Uncovering nonlinear patterns in time-sensitive prehospital breathing emergencies: an exploratory machine learning study.

BMC medical informatics and decision making
BACKGROUND: Timely prehospital care is crucial for patients presenting with high-risk time-sensitive (HRTS) conditions. However, the interplay between response time and demographic factors in patients with breathing problems remains insufficiently un...

Comparative analysis of AI chatbot (ChatGPT-4.0 and Microsoft Copilot) and expert responses to common orthodontic questions: patient and orthodontist evaluations.

BMC oral health
OBJECTIVE: The aim of this study was to evaluate the adequacy of responses provided by experts and artificial intelligence-based chatbots (ChatGPT-4.0 and Microsoft Copilot) to frequently asked orthodontic questions, utilizing scores assigned by pati...

Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume.

BMC psychiatry
BACKGROUND: The thalamus plays a crucial role in sensory processing, emotional regulation, and cognitive functions, and its dysregulation may be implicated in psychosis. The aim of the present study was to examine the differences in thalamic subregio...

Efficient structure learning of gene regulatory networks with Bayesian active learning.

BMC bioinformatics
BACKGROUND: Gene regulatory network modeling is a complex structure learning problem that involves both observational data analysis and experimental interventions. Bayesian causal discovery provides a principled framework for modeling observational d...