AIMC Topic: Humans

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Deep Learning Network for Segmentation of the Prostate Gland With Median Lobe Enlargement in T2-weighted MR Images: Comparison With Manual Segmentation Method.

Current problems in diagnostic radiology
PURPOSE: Aim of this study was to evaluate a fully automated deep learning network named Efficient Neural Network (ENet) for segmentation of prostate gland with median lobe enlargement compared to manual segmentation.

Deep-learning-based attenuation correction in dynamic [O]HO studies using PET/MRI in healthy volunteers.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Quantitative [O]HO positron emission tomography (PET) is the accepted reference method for regional cerebral blood flow (rCBF) quantification. To perform reliable quantitative [O]HO-PET studies in PET/MRI scanners, MRI-based attenuation-correction (M...

Clinical applicability of automated cephalometric landmark identification: Part II - Number of images needed to re-learn various quality of images.

Orthodontics & craniofacial research
AIM: To estimate the number of cephalograms needed to re-learn for different quality images, when artificial intelligence (AI) systems are introduced in a clinic.

Detecting lumbar lesions in Tc-MDP SPECT by deep learning: Comparison with physicians.

Medical physics
PURPOSE: Tc-MDP single-photon emission computed tomography (SPECT) is an established tool for diagnosing lumbar stress, a common cause of low back pain (LBP) in pediatric patients. However, detection of small stress lesions is complicated by the low...

Using neural networks to extend cropped medical images for deformable registration among images with differing scan extents.

Medical physics
PURPOSE: Missing or discrepant imaging volume is a common challenge in deformable image registration (DIR). To minimize the adverse impact, we train a neural network to synthesize cropped portions of head and neck CT's and then test its use in DIR.

A feasibility study on deep learning-based individualized 3D dose distribution prediction.

Medical physics
PURPOSE: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models...

Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

BMC endocrine disorders
BACKGROUND: Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients' management. However, to our knowledge, an inst...

A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes.

Fertility and sterility
OBJECTIVE: To determine whether a machine learning causal inference model can optimize trigger injection timing to maximize the yield of fertilized oocytes (2PNs) and total usable blastocysts for a given cohort of stimulated follicles.

Ethics of AI in Pathology: Current Paradigms and Emerging Issues.

The American journal of pathology
Deep learning has rapidly advanced artificial intelligence (AI) and algorithmic decision-making (ADM) paradigms, affecting many traditional fields of medicine, including pathology, which is a heavily data-centric specialty of medicine. The structured...

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

Archives of toxicology
The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it re...