Calcium-salt precipitation is a widely used compliance-control process for industrial fluoride-containing wastewater. Its core objective is to maintain stable effluent fluoride compliance while avoiding unnecessary chemical consumption. In real indus...
Whether the phosphorus removal chemical in wastewater treatment plants (WWTPs) can be accurately dosed not only affects the compliance of the effluent total phosphorus but also has a huge impact on sludge production and energy consumption during the...
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...
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...
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...
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/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...
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...
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...
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...
Rapid growth of water conservancy/hydropower projects has spurred rising demand for sand-gravel aggregates. Under strict water use and zero-waste policies, treating wet-process aggregate washing wastewater is challenging. Flocculants—key chemic...
This study presents a sustainable, battery-free UV (ultraviolet) dose sensor designed for intelligent food packaging applications. The device integrates laser-induced graphene (LIG) electrodes, a ZnO-CNT (carbon nanotube) UV-active composite, and a b...
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...
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...
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...
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...
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...
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...
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...
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...
Background/Objectives: This study quantitatively evaluates the novel Intelligent Noise Reduction (INR) software NE 3.10.0.15 across three chest radiography protocols, namely, physical anti-scatter grid, non-grid, and virtual anti-scatter grid, to opt...
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...
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...
Precision oncology is grounded in the increasing understanding of genetic and molecular mechanisms that underly malignant disease and offer different treatment pathways for the individual patient. The growing complexity of medical data has led to the...
Background: Despite the availability of direct oral anticoagulants, warfarin remains essential for mechanical valves, renal impairment, and resource-limited settings. Traditional dosing achieves therapeutic range in only 55–65% of patients, inc...
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...
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...
Conventional fertigation systems suffer from limited cross-regional adaptability, mainly due to unstable fertilizer flow from fixed-aperture units, poor terrain adaptability, and an inadequate response to environmental heterogeneity. This study propo...
Background and aim: Chest CT is a major contributor to population radiation exposure. Conventional, pooled diagnostic reference levels (DRLs) do not account for inter-individual variability in body habitus and are typically used retrospectively. We e...
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...
Background: Diabetic foot ulcers recur frequently after healing. The first three months carry the highest risk. Remission is a vulnerable phase that demands precise self-care and timely feedback. Evidence supports thermometry and protective footwear...
Background/Objectives: To date, no systematic review has focused specifically on commercially available deep learning-based image reconstruction (DLIR) software for radiation dose optimization in pediatric radiology. The purpose of this article was t...
Objective: To evaluate the feasibility of simulated ultra-low-dose CT (ULD-CT) enhanced with deep learning denoising (DLD) for detecting pelvic and hip fractures. Method: This Institutional Review Board-approved retrospective study was performed at a...
Background: Cystic fibrosis bone disease (CFBD) is a common comorbidity in adult people with cystic fibrosis (pwCF), resulting in an increased risk of bone fractures. This study evaluated the capacity of artificial intelligence (AI)-assisted low-dose...
A pandemic like COVID-19 caused a massive blow to the global economy, and its impacts will be large and endure across all domains of life. One of the crucial factors in fighting this pandemic is the proper management and administration of the limited...
Low-dose computed tomography (LDCT) is essential for reducing potential radiation-associated risks, but increased noise and artifacts can compromise diagnostic confidence. This review examines how AI is reshaping LDCT by improving reconstruction, den...
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