AIMC Topic: Humans

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Machine Learning Algorithms to Predict Delayed Cerebral Ischemia After Subarachnoid Hemorrhage: A Systematic Review and Meta-analysis.

Neurocritical care
Delayed cerebral ischemia (DCI) is a common and severe complication after subarachnoid hemorrhage (SAH). Logistic regression (LR) is the primary method to predict DCI, but it has low accuracy. This study assessed whether other machine learning (ML) m...

Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.

Eye (London, England)
PURPOSE: To validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD).

Soft computing applications in the field of human factors and ergonomics: A review of the past decade of research.

Applied ergonomics
The main objectives of this study were to 1) review the literature on the applications of soft computing concepts to the field of human factors and ergonomics (HFE) between 2013 and 2022 and 2) highlight future developments and trends. Multiple soft ...

A study on whether deep learning models based on CT images for bone density classification and prediction can be used for opportunistic osteoporosis screening.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED: This study utilized deep learning to classify osteoporosis and predict bone density using opportunistic CT scans and independently tested the models on data from different hospitals and equipment. Results showed high accuracy and strong c...

Deep learning for the early identification of periodontitis: a retrospective, multicentre study.

Clinical radiology
AIM: To develop a deep-learning model to help general dental practitioners diagnose periodontitis accurately and at an early stage.

Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy.

Journal of applied clinical medical physics
PURPOSE: Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time thro...

Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment.

Journal of digital imaging
Glucose transporter-1 (GLUT-1) expression level is a biomarker of tumour hypoxia condition in immunohistochemistry (IHC)-stained images. Thus, the GLUT-1 scoring is a routine procedure currently employed for predicting tumour hypoxia markers in clini...

iOCT-guided simulated subretinal injections: a comparison between manual and robot-assisted techniques in an ex-vivo porcine model.

Journal of robotic surgery
The purpose of this study is to compare robot-assisted and manual subretinal injections in terms of successful subretinal blistering, reflux incidences and damage of the retinal pigment epithelium (RPE). Subretinal injection was simulated on 84 ex-vi...

Image quality improvement in bowtie-filter-equipped cone-beam CT using a dual-domain neural network.

Medical physics
BACKGROUND: The bowtie-filter in cone-beam CT (CBCT) causes spatially nonuniform x-ray beam often leading to eclipse artifacts in the reconstructed image. The artifacts are further confounded by the patient scatter, which is therefore patient-depende...

The performance of artificial intelligence language models in board-style dental knowledge assessment: A preliminary study on ChatGPT.

Journal of the American Dental Association (1939)
BACKGROUND: Although Chat Generative Pre-trained Transformer (ChatGPT) (OpenAI) may be an appealing educational resource for students, the chatbot responses can be subject to misinformation. This study was designed to evaluate the performance of Chat...