Recent concerns regarding artificial intelligent (AI) technologies have spurred studies into improving wastewater treatment efficiency and identifying low-carbon processes. Treating one cubic meter of wastewater necessarily consumes a certain amount...
Background: Antimicrobial resistance (AMR) is a major global health threat, while conventional diagnostic, stewardship, dosing, and drug-development strategies remain limited by diagnostic delays, population-level decision-making, and a shrinking ant...
Background/Objective: Traditional therapeutic drug monitoring is limited by manual interpretation and specific constraints like sampling at steady-state and requiring a minimum of two drug concentrations. The integration of model-informed precision d...
Hybrid artificial intelligence (AI) and Monte Carlo (MC) methods for radiotherapy dose calculation refer to computational approaches that integrate machine learning models with physics-based MC simulation to achieve both fast and accurate estimation...
22 January 2021
Ten years have elapsed since the accident at the Fukushima Daiichi Nuclear Power Plant in 2011, and the relative contribution of natural radiation is increasing in Fukushima Prefecture due to the reduced dose of artificial radiation. In order to accu...
This study investigates the identification of artificial irradiation in thermoluminescence (TL) pre-dose dating of ancient Chinese porcelain to address the challenges posed by sophisticated counterfeiting techniques. While TL pre-dose dating is effec...
Accurate calculation of gamma dose rates in medical and industrial facilities is a critical component of comprehensive dosimetry assessment. Usually, two complementary approaches are employed to this end: experimental measurements and Monte Carlo (MC...
Cone-beam computed tomography (CBCT) plays an important role in dental diagnosis; however, radiation exposure remains a concern. This study evaluated the feasibility of artificial intelligence (AI)-based image processing for improving image quality i...
This study aimed to investigate the changes in abomasum transcriptome and the associated microbial community structure in young calves with artificially dosed, adult rumen contents. Eight young bull calves were randomly dosed with freshly extracted r...
Optimizing immunosuppressant dosing presents significant challenges in kidney transplantation due to narrow therapeutic ranges and considerable inter-patient pharmacokinetic differences. Emerging strategies for precision dosing, encompassing Bayesian...
Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk smoker populations have shown a reduction in the number of lung cancer deaths in the screening group compared to a control group. Even if vari...
Background/Objectives: High dose rate brachytherapy (HDR-BT) is resource-intensive. Workflow efficiency and inter-user variability remain prevalent issues in HDR-BT. To improve workflow efficiency and reduce inter-user variability, we introduced arti...
Background/Objectives: The effectiveness of chemotherapy in cancer treatment is often compromised by inter-patient variability, leading to suboptimal outcomes. Traditional dosing protocols rely on population-based models that do not account for indiv...
Background and purpose: Based on artificial intelligence (AI), 3D angiography (3DA) is a novel postprocessing algorithm for “DSA-like” 3D imaging of cerebral vasculature. Because 3DA requires neither mask runs nor digital subtraction as t...
Objectives: Deep learning-based artificial intelligence (AI) tools have been gradually used to detect and segment pulmonary nodules in clinical practice. This study aimed to assess the diagnostic performance of quantitative measures derived from a co...
In this study, the thermoluminescence (TL) glow curve test results performed with eleven different dose values were used as training data, and its attempted to estimate the test results of the curves performed at four different doses using artificial...
Objectives: This study aims to predict patient-specific Average Glandular Dose (AGD) in mammography using machine learning-based models to support personalised radiation dose optimisation and reduce unnecessary exposure during breast cancer screening...
Background/Objectives: This study evaluated the daily delivered dose in prostate cancer patients using the automated artificial intelligence (AI)-based software Adaptbox (v2.3.2, Therapanacea). The aim was to assess target coverage and organ-at-risk...
Work processes and sociological structures can differ significantly between organizations. These organizational aspects determine user behavior, which in turn exerts considerable influence on the key performance indicators of artificial lighting syst...
Quartz is a widely used mineral in dosimetric and geochronological applications due to its stable luminescence properties under ionizing radiation. This study presents an artificial neural network (ANN)-based approach to predict the optically stimula...
The thermoluminescence (TL) method is one of the most widely used techniques in various studies, including dosimetric applications, dating of archaeological and geological materials, luminescence spectroscopy of certain insulating or semiconducting p...
The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a bala...
Interindividual variability in drug response remains a major challenge in clinical pharmacology despite substantial advances in therapeutic drug monitoring (TDM), pharmacogenomics, pharmacokinetics/pharmacodynamics (PK/PD), and model-informed precisi...
A key challenge for drug dosing schedules is the ability to learn an optimal control policy even when there is a paucity of accurate information about the systems. Artificial intelligence has great potential for shaping a smart control policy for the...
Background and Objectives: Diabetes mellitus represents one of the most prevalent chronic metabolic disorders worldwide, necessitating precise insulin dose management to prevent both acute and long-term complications. The optimization of insulin dosi...
Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerston...
Gas bubbles are one of the main disturbances encountered when dispensing drugs of microliter volumes using portable miniaturized systems based on piezoelectric diaphragm micropumps. The presence of a gas bubble in the pump chamber leads to the inaccu...
Adaptive radiotherapy (ART) transforms radiotherapy from a static treatment plan into a monitored, feedback-driven process that can respond to anatomic and biologic change. This critical narrative review synthesizes foundational physics literature, c...
Artificial intelligence (AI) is reforming pharmaceutical sciences by renovating traditional drug formulation and dosage calculation approaches. This review provides a comprehensive overview of how AI technologies, such as machine learning (ML), deep...
Estimation of the percutaneous absorption is essential for the safety assessment of cosmetic and dermopharmaceutical products. Currently, an artificial membrane, Strat-M®, has been focused on as the tool which could obtain the permeation paramete...
Lung cancer is the leading cause of cancer-related death worldwide, with 1.8 million deaths annually. Early detection is vital for improving patient outcomes; however, survival rates remain low due to late-stage diagnoses. Accumulating data supports...
Biological effects of the Fukushima nuclear accident have been reported in various organisms, including the pale grass blue butterfly Zizeeria maha and its host plant Oxalis corniculata. This plant upregulates various secondary metabolites in respons...
The growing demand for raw and processed scientific data has encouraged many researchers and research institutions to adopt an open-source data policy. At present, data accessibility is of paramount importance due to the growing demand for artificial...
Artificial intelligence (AI) has rapidly emerged as a transformative force in musculoskeletal imaging and interventional radiology. This article explores how AI-based methods—including machine learning (ML) and deep learning (DL)—streamli...
Lung cancer is the leading cause of cancer-related mortality worldwide, yet the majority of cases are diagnosed at advanced stages when a cure is rarely achievable. Low-dose computed tomography (LDCT) screening in high-risk populations reduces lung c...
Chest imaging plays a pivotal role in screening and monitoring patients, and various predictive artificial intelligence (AI) models have been developed in support of this. However, little is known about the effect of decreasing the radiation dose and...
Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimiza...
The ban on the use of animals in testing cosmetic products has led to the development of animal-free in vitro methods. Strat-M® is an artificial membrane engineered to mimic human skin and is recommended as a replacement for skin. However, its us...
Short-wavelength artificial light at night is increasingly being associated with health and ecological risks. The negative impact of this relatively new source of pollution has been studied intensively in wild birds but to a much lesser extent in cap...
Urinary stone disease is common, recurrent, and increasingly managed through imaging-driven pathways, yet standard-dose CT of the kidneys, ureters, and bladder (CT KUB) raises concerns about cumulative radiation exposure and the limited use of quanti...
Background and Objectives: The use of artificial intelligence (AI) and, in particular, machine learning (ML) techniques is growing rapidly in the healthcare field. Their application in pharmacokinetics is of potential interest due to the need to rela...
The study evaluated the relation between the oxidative stress index (OSI) in porcine seminal plasma (n = 76) with sperm resilience and in vivo fertility (farrowing rate and litter size of 3137 inseminated sows) of liquid-stored artificial inseminatio...
Background/Objectives: GnRH agonists may offer potential benefits when used for luteal phase support in assisted reproductive treatments. This systematic review and meta-analysis of randomized controlled trials evaluates the effect of a single-dose a...
Background: Gamma Knife radiosurgery (GKS) delivers highly conformal intracranial irradiation, yet clinical decision-making still relies predominantly on physical dose metrics that do not account for fractionation, dose rate, treatment time, or DNA r...
11 July 2020
Activity concentrations of 234U, 235U, 238U, 226Ra, 228Ra, 222Rn, 210Po, 210Pb, 40K, 3H, 14C, 134Cs and 137Cs were determined in 20 different Japanese bottled drinking water commercially available in Japan. The origins of the mineral water samples we...
Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Comp...
The impact of artificially sweetened beverages (ASBs), alternatives to sugar-sweetened beverages, on cancer incidence remains controversial. We conducted a meta-analysis of prospective studies to assess the association of daily ASB intake with cancer...
Background: Computed tomography scans are widely used in everyday medical practice due to speed, image reliability, and detectability of a wide range of pathologies. Each scan exposes the patient to a radiation dose, and performing a fast estimation...
Objective: This study proposes a novel deep learning approach for enhancing low-dose bone scintigraphy images using an Enhanced Convolutional Autoencoder (ECAE), aiming to reduce patient radiation exposure while preserving diagnostic quality, as asse...
Geriatric pharmacotherapy is usually challenged by physiological senescence. For instance, progressive declines in organ function and alterations in body composition can complicate drug disposition. However, conventional pharmacometrics models common...
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