AIMC Topic: Reproducibility of Results

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Cycle-level traffic conflict prediction at signalized intersections with LiDAR data and Bayesian deep learning.

Accident; analysis and prevention
Real-time safety prediction models are vital in proactive road safety management strategies. This study develops models to predict traffic conflicts at signalized intersections at the signal cycle level, using advanced Bayesian deep learning techniqu...

Portable Device to Assist With Force Control in Ultrasound Acquisition.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
This study presents a portable device that ensures precise contact force between a subject and a probe to improve the stability and reproducibility of ultrasound (US) acquisition. The mechanical portion of the device includes a servo motor, gears, an...

Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset.

Scientific reports
Preoperative radiological identification of mandibular canals is essential for maxillofacial surgery. This study demonstrates the reproducibility of a deep learning system (DLS) by evaluating its localisation performance on 165 heterogeneous cone bea...

Artificial intelligence for dementia research methods optimization.

Alzheimer's & dementia : the journal of the Alzheimer's Association
Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability ...

Robotic transcranial magnetic stimulation in the treatment of depression: a pilot study.

Scientific reports
There has been an increasing demand for robotic coil positioning during repetitive transcranial magnetic stimulation (rTMS) treatment. Accurate coil positioning is crucial because rTMS generally targets specific brain regions for both research and cl...

A Comprehensive, Valid, and Reliable Tool to Assess the Degree of Responsibility of Digital Health Solutions That Operate With or Without Artificial Intelligence: 3-Phase Mixed Methods Study.

Journal of medical Internet research
BACKGROUND: Clinicians' scope of responsibilities is being steadily transformed by digital health solutions that operate with or without artificial intelligence (DAI solutions). Most tools developed to foster ethical practices lack rigor and do not c...

Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM).

Revista do Colegio Brasileiro de Cirurgioes
The landscape of surgical training is rapidly evolving with the advent of artificial intelligence (AI) and its integration into education and simulation. This manuscript aims to explore the potential applications and benefits of AI-assisted surgical ...

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP p...

Deep learning-based postoperative visual acuity prediction in idiopathic epiretinal membrane.

BMC ophthalmology
BACKGROUND: To develop a deep learning (DL) model based on preoperative optical coherence tomography (OCT) training to automatically predict the 6-month postoperative visual outcomes in patients with idiopathic epiretinal membrane (iERM).

3D Breast Cancer Segmentation in DCE-MRI Using Deep Learning With Weak Annotation.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning models require large-scale training to perform confidently, but obtaining annotated datasets in medical imaging is challenging. Weak annotation has emerged as a way to save time and effort.