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Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model.

BACKGROUND: Donepezil, galantamine, rivastigmine and memantine are potentially effective interventions for cognitive impairment in dementia, but the use of these drugs has not been personalised to individual patients yet. We examined whether artificial intelligence-based recommendations can identify the best treatment using routinely collected patient-level information. METHODS: Six thousand eight hundred four patients aged 59-102 years with a diagnosis of dementia from two National Health Service (NHS) Foundation Trusts in the UK were used for model training/internal validation and external

Three-Dimensional Ultrasound Images in the Assessment of Bladder Tumor Health Monitoring under Deep Learning Algorithms.

This study was aimed at exploring the application value of three-dimensional (3D) ultrasound based on deep learning and continued nursing health monitoring (CNHM) mode in promoting the recovery of bladder cancer patients after surgery. 60 patients who underwent muscular noninvasive superficial bladder cancer and bladder perfusion treatment were selected as the research objects. The patients were randomly divided into two groups: an experimental group (30 cases) and a control group (30 cases). Patients in the experimental group adopted a CNHM model during the bladder perfusion treatment. The pa

Application of deep-learning to the seronegative side of the NMO spectrum.

OBJECTIVES: To apply a deep-learning algorithm to brain MRIs of seronegative patients with neuromyelitis optica spectrum disorders (NMOSD) and NMOSD-like manifestations and assess whether their structural features are similar to aquaporin-4-seropositive NMOSD or multiple sclerosis (MS) patients. PATIENTS AND METHODS: We analyzed 228 T2- and T1-weighted brain MRIs acquired from aquaporin-4-seropositive NMOSD (n = 85), MS (n = 95), aquaporin-4-seronegative NMOSD [n = 11, three with anti-myelin oligodendrocyte glycoprotein antibodies (MOG)], and aquaporin-4-seronegative patients with NMOSD-like

Computer-aided therapeutic diagnosis for anorexia.

BACKGROUND: Anorexia nervosa is a clinical disorder syndrome of the wide spectrum without a fully recognized etiology. The necessary issue in the clinical diagnostic process is to detect the causes of this disease (e.g., my body image, food, family, peers), which the therapist gradually comes to by verifying assumptions using proper methods and tools for diagnostic process. When a person is diagnosed with anorexia, a clinician (a doctor, a therapist or a psychologist) proposes a therapeutic diagnosis and considers the kind of treatment that should be applied. This process is also continued dur

Application of dose-volume histogram prediction in biologically related models for nasopharyngeal carcinomas treatment planning.

PURPOSE: In this study, we employed a gated recurrent unit (GRU)-based recurrent neural network (RNN) using dosimetric information induced by individual beam to predict the dose-volume histogram (DVH) and investigated the feasibility and usefulness of this method in biologically related models for nasopharyngeal carcinomas (NPC) treatment planning. METHODS AND MATERIALS: One hundred patients with NPC undergoing volumetric modulated arc therapy (VMAT) between 2018 and 2019 were randomly selected for this study. All the VMAT plans were created using the Monaco treatment planning system (Elekta,

Dose-volume histogram prediction in volumetric modulated arc therapy for nasopharyngeal carcinomas based on uniform-intensity radiation with equal angle intervals.

In this study, we developed a gated recurrent unit (GRU)-based recurrent neural network (RNN) for dose-volume histogram (DVH) prediction in volumetric modulated arc therapy (VMAT) planning for nasopharyngeal carcinomas (NPCs) based on uniform-intensity radiation with equal angle intervals and investigated the feasibility and usefulness of this method for treatment optimization. One hundred twenty-four NPC patients were selected from a database containing clinical VMAT plans from 2015 to 2018; of these, the data from 100 patients were used to train the GRU-RNN, and the data of the other 24 pati

Early Diagnosis of Knee Osteoarthritis With a Natural Language Processing-Driven Approach Based on Clinician Notes: Development and Validation Study.

BACKGROUND: Knee osteoarthritis (OA) is a common form of knee arthritis that can cause significant disability and affect a patient's quality of life. Although this disease is chronic and irreversible, the patient's condition can be improved and the progression of the disease can be prevented if the disease is diagnosed early and the patient receives appropriate treatment immediately. Therefore, the prediction of knee OA is considered one of the essential steps to effectively diagnose and prevent further severe OA conditions. Knee OA is commonly diagnosed by medical experts or physicians, and t

TILTomorrow today: dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injury.

Practices for controlling intracranial pressure (ICP) in traumatic brain injury (TBI) patients admitted to the intensive care unit (ICU) var … fectiveness Research in TBI study. We developed the TILTomorrow modelling strategy, which leverages recurrent neural networks to map a token-embedded time series representation of all variables (including missing values) to an ordinal, dynamic prediction of the following day's five-category therapy intensity level (TIL(Basic)) score. With 20 repeats of fivefold cross-valid …

A multicenter study on the prediction model for chronic low back pain after lumbar decompression surgery in patients with diabetes mellitus: integration of metabolic and paraspinal muscle features.

OBJECTIVE: Based on multicenter clinical data, this study aimed to develop and validate a predictive model for chronic low back pain (CLBP) after lumbar decompression surgery in patients with diabetes mellitus and lumbar disc herniation. The model integrated metabolic indicators and imaging-derived features of the paraspinal muscles. METHODS: This study was designed as a multicenter retrospective cohort study. A total of 2776 patients with diabetes mellitus and lumbar disc herniation were included. All patients underwent unilateral biportal endoscopic (UBE) decompression between January 2021

Pharmacovigilance analysis and real-world adverse event profile (safety signals and product-related issues) of potassium citrate: a US FDA adverse event reporting system (FAERS) based analysis.

Potassium citrate is widely used as oral alkali therapy for the prevention of recurrent nephrolithiasis. Despite its long-standing clinical use, systematically collected real-world safety data remain limited. We performed a pharmacovigilance analysis to characterize adverse event reports associated with potassium citrate using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS). FAERS reports submitted between 2014 and 2025 listing potassium citrate as the primary suspect drug were analyzed. Duplicate records were removed, and adverse events were coded using the Medica

Magnetically Repulsive Cushion Triboelectric Nanogenerator for Rotating Machinery Structural Health Monitoring.

Rotor imbalance and abnormal vibration are classical operating conditions in rotating machinery and can often be identified by conventional vibration analysis. However, the development of low-power, self-powered, and distributed sensing nodes remains important for long-term condition monitoring, particularly in scenarios where external power supply, wiring, and maintenance are constrained. Existing vibration sensors, including piezoelectric and capacitive types, are constrained by power consumption and degraded performance under low-frequency and weak excitation. To address this issue, a magne

Poincaré feature-based classification of electroencephalography signals for multiple sclerosis diagnosis.

BACKGROUND: As one of the most widespread neurodegenerative disorders, Multiple Sclerosis (MS) is a progressive neuroinflammatory disorder affecting millions of individuals worldwide. Although magnetic resonance imaging (MRI) techniques are commonly employed for diagnosis, there is an increasing emphasis on electroencephalogram (EEG) signal processing to diagnose neurodegenerative disorders. METHODS: This study explores the use of Poincaré plot-derived features from EEG signals to differentiate MS patients from healthy individuals. EEG recordings from 50 subjects (25 MS, 25 Healthy controls)

Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis.

OBJECTIVE: The purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases. METHODS: We retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple agg

An Artificial Intelligence System for Optimizing Radioactive Iodine Therapy Dosimetry.

Thyroid cancer, specifically differentiated thyroid carcinoma (DTC), is one of the most prevalent endocrine malignancies worldwide. Radioactive iodine therapy (RAIT) using I-131 has been a standard-of-care approach for DTC due to its ability to ablate remnant thyroid disease following surgery, thus reducing the risk of recurrence. It is also used for the treatment of iodine-avid metastases. RAIT dosimetry can be employed to determine the optimal treatment dose of I-131 to effectively treat cancer cells while safeguarding against undesirable radiation effects such as bone marrow toxicity or rad

A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus.

Gestational diabetes mellitus (GDM) is a subtype of diabetes that develops during pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce clinical complications for women with gestational diabetes. The objectives of this study are to (1) develop benchmark glucose prediction models with long short-term memory (LSTM) recurrent neural network models using time-series data collected from the GDm-Health platform, (2) compare the prediction accuracy with published results, and (3) suggest an optimized clinical review schedule with the potential to reduce the overall

Fourier Synchrosqueezing Transform-ICA-EMD Framework Based EOG-Biometric Sustainable and Continuous Authentication via Voluntary Eye Blinking Activities.

In recent years, limited works on EOG (electrooculography)-based biometric authentication systems have been carried out with eye movements or eye blinking activities in the current literature. EOGs have permanent and unique traits that can separate one individual from another. In this work, we have investigated FSST (Fourier Synchrosqueezing Transform)-ICA (Independent Component Analysis)-EMD (Empirical Mode Decomposition) robust framework-based EOG-biometric authentication (one-versus-others verification) performances using ensembled RNN (Recurrent Neural Network) deep models voluntary eye bl

Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital-acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language a

Using Deep Learning to Predict Minimum Foot-Ground Clearance Event from Toe-Off Kinematics.

Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as the key biomechanical determinant of tripping probability. MFC is defined as the minimum swing foot clearance, which is seen approximately mid-swing, and it is routinely measured in gait biomechanics laboratories

Стратегии интеграции искусственного интеллекта и когнитивной оценки для прогнозирования развития инвалидности в релапсирующихся множестве склерозов: исследование по разработке моделей.

Серийная когнитивная оценка при множественном склерозе может помочь выявить пациентов, подверженных более высокому риску развития инвалидности, однако высокая масштабность данных о времени реакции затрудняет анализ. Мы разработали метод преобразования данных о времени продольной реакции в изображения и применили модели глубокого выживания для прогнозирования развития инвалидности в релапсирующих MS (RRMS). В этом когортном исследовании клинические данные были получены из регистра MSBase и когнитивных данных из компьютеризированной когнитивной батареи MSRector в период с февраля 2016 года по се

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… DDH and RDSM are multilayer perceptron models that incorporate recurrent neural networks for dynamic analysis of survival data. …

Adverse events associated with cyclophosphamide: A pharmacovigilance study using the FDA adverse event reporting system.

OBJECTIVE: Cyclophosphamide(CHOP) is a widely used chemotherapeutic agent belonging to the class of alkylating agents. It primarily inhibits the proliferation of tumor cells by interfering with DNA replication and transcription. CHOP has been approved for the treatment of various types of malignant tumors. The aim of this research is to assess adverse events linked to CHOP using real-world data. METHODS: This research collected and analyzed adverse event reports associated with CHOP from the FAERS(US Food and Drug Administration Adverse Event Reporting System) database spanning from the first