AIMC Topic: Humans

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Readability, quality and accuracy of generative artificial intelligence chatbots for commonly asked questions about labor epidurals: a comparison of ChatGPT and Bard.

International journal of obstetric anesthesia
INTRODUCTION: Over 90% of pregnant women and 76% expectant fathers search for pregnancy health information. We examined readability, accuracy and quality of answers to common obstetric anesthesia questions from the popular generative artificial intel...

Do machine learning methods solve the main pitfall of linear regression in dental age estimation?

Forensic science international
INTRODUCTION: Age estimation is crucial in forensic and anthropological fields. Teeth, are valued for their resilience to environmental factors and their preservation over time, making them essential for age estimation when other skeletal remains det...

Automated identification of impact spatters and fly spots with a residual neural network.

Forensic science international
In criminal investigations, distinguishing between impact spatters and fly spots presents a challenge due to their morphological similarities. Traditional methods of bloodstain pattern analysis (BPA) rely significantly on the expertise of professiona...

Adaptive cascade decoders for segmenting challenging regions in medical images.

Computers in biology and medicine
CNN-based techniques have achieved impressive outcomes in medical image segmentation but struggle to capture long-term dependencies between pixels. The Transformer, with its strong feature extraction and representation learning abilities, performs ex...

TSegLab: Multi-stage 3D dental scan segmentation and labeling.

Computers in biology and medicine
This study introduces a novel deep learning approach for 3D teeth scan segmentation and labeling, designed to enhance accuracy in computer-aided design (CAD) systems. Our method is organized into three key stages: coarse localization, fine teeth segm...

DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization.

Computers in biology and medicine
Neurologists often face challenges in identifying epileptic activities within multichannel EEG recordings, requiring extensive hours of analysis. Computer-aided diagnosis systems have been proposed to reduce manual inspection of EEG signals by neurol...

3D MFA: An automated 3D Multi-Feature Attention based approach for spine segmentation using a multi-stage network pruning.

Computers in biology and medicine
Spine segmentation poses significant challenges due to the complex anatomical structure of the spine and the variability in imaging modalities, which often results in unclear boundaries and overlaps with surrounding tissues. In this research, a novel...

Pinning down the accuracy of physics-informed neural networks under laminar and turbulent-like aortic blood flow conditions.

Computers in biology and medicine
BACKGROUND: Physics-informed neural networks (PINNs) are increasingly being used to model cardiovascular blood flow. The accuracy of PINNs is dependent on flow complexity and could deteriorate in the presence of highly-dynamical blood flow conditions...

Advanced Mass-Spectra-Based Machine Learning for Predicting the Toxicity of Traditional Chinese Medicines.

Analytical chemistry
Traditional Chinese medicine (TCM) has been a cornerstone of health care for centuries, valued for its preventive and therapeutic properties. However, recent decades have revealed significant toxicological concerns associated with TCMs due to their c...