AIMC Topic: Adult

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Impact of artificial intelligence assistance on diagnosing periapical radiolucencies: A randomized controlled trial.

Journal of dentistry
OBJECTIVES: This randomized controlled trial aimed to evaluate the impact of artificial intelligence (AI) assistance on dentists' diagnostic accuracy, confidence, and treatment decisions when detecting periapical radiolucencies (PRs) on panoramic rad...

Super-resolution sodium MRI of human gliomas at 3T using physics-based generative artificial intelligence.

Journal of neuro-oncology
PURPOSE: Sodium neuroimaging provides unique insights into the cellular and metabolic properties of brain tumors. However, at 3T, sodium neuroimaging MRI's low signal-to-noise ratio (SNR) and resolution discourages routine clinical use. We evaluated ...

Developing a CT radiomics-based model for assessing split renal function using machine learning.

Japanese journal of radiology
PURPOSE: This study aims to investigate whether non-contrast computed tomography radiomics can effectively reflect split renal function and to develop a radiomics model for its assessment.

A comparative study of ANN-based forward dynamics and inverse dynamics in human gait analysis.

Journal of biomechanics
This study investigates the similarities and differences in the analysis of human walking motion between the traditional inverse dynamics method and the forward dynamics method that employs an Artificial Neural Network (ANN)-based controller. Nine he...

Assessing the impact of artifact correction and artifact rejection on the performance of SVM- and LDA-based decoding of EEG signals.

NeuroImage
Numerous studies have demonstrated that eyeblinks and other large artifacts can decrease the signal-to-noise ratio of EEG data, resulting in decreased statistical power for conventional univariate analyses. However, it is not clear whether eliminatin...

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...

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...

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...