Результаты поиска

Биомаркеры мультимодального МРТ и машинное обучение по расстройству спектра нейромиелита: обзор.

Нейромиелитное расстройство спектра (NMOSD) — воспроизводящееся воспалительное демиелионирующее заболевание центральной нервной системы, которое может привести к значительной кумулятивной неврологической неполноценности. Магнитный резонансный снимок (MRI) играет центральную роль в обнаружении повреждений, дифференциальной диагностике и продольной оценке. Однако обычные МРТ могут не в полной мере улавливать утончённые повреждения тканей и в клинически двусмысленных случаях могут иметь место значительные переплетения со связанными с ними подстрекательскими демиелионирующими расстройствами. Надеж

Скринэтон 2.0:: Проведение совместного обследования с участием человека и АИ на основе данных о состоянии здоровья пациентов.

Систематические обзоры имеют важное значение для проведения исследований, основанных на фактических данных, однако традиционный процесс отбора требует много времени и его трудно масштабировать. Скрининг, проводимый исключительно для человека, может привести к непоследовательности, в то время как полностью автоматизированные подходы с использованием моделей Большого языка зачастую не имеют контекстуального суждения, необходимого для принятия сложных решений. Для решения этой проблемы мы внедряем методологию скрининга на основе толпы, которая сочетает человеческий опыт и адаптивное машинное обуч

Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score.

Background and objectives: Little is known about outcome improvements and disparities in cardiac arrest and active cancer. We performed the first known AI and propensity score (PS)-augmented clinical, cost-effectiveness, and computational ethical analysis of cardio-oncology cardiac arrests including left heart catheterization (LHC)-related mortality reduction and related disparities. Materials and methods: A nationally representative cohort analysis was performed for mortality and cost by active cancer using the largest United States all-payer inpatient dataset, the National Inpatient Sample,

От прогнозирования к вмешательству: Искусственный интеллект для адаптивного реагирования и моделирования токсичности в клеточной терапии для гематологовых злодеяний.

Гематологическая магнелезность, включая острую лейкемию миэлоида, миэлодипластические синдромы, лимфому и множественную миелому, характеризуется глубокой биологической гетерогенностью и высокодинамичными траекториями лечения, которые традиционные статические прогнозирующие системы неполно улавливают. Клеточная терапия, такая, как химерик антигенная рецепторная терапия Т-клеток и трансплантация стволовых клеток с гематополией, предлагает потенциально лечебные варианты для рецидива или рефракторного заболевания, однако результаты остаются весьма различными, а управленческие решения, касающиеся и

Coronary health index based on immunoglobulin light chains to assess coronary heart disease risk with machine learning: a diagnostic trial.

BackgroundRecent studies suggest a connection between immunoglobulin light chains (IgLCs) and coronary heart disease (CHD). However, current diagnostic methods using peripheral blood IgLCs levels or subtype ratios show limited accuracy for CHD, lacking comprehensive assessment and posing challenges in early detection and precise disease severity evaluation. We aim to develop and validate a Coronary Health Index (CHI) incorporating total IgLCs levels and their distribution. Additionally, we aim to evaluate its effectiveness by integrating patient data and using machine learning models through d

Регулируемые иммунные белки в плазме Биобанка Великобритании предсказывают множественные миеломы 12 За годы до клинического диагноза.

Аннотация не предоставлена источником. Откройте карточку для перехода к публикации.

Orthopaedic Oncology in Contemporary Practice: A Comprehensive Review of Primary Bone Tumours, Metastatic Pathways, and Evolving Surgical Techniques.

Orthopaedic oncology encompasses the diagnosis and management of primary bone sarcomas and metastatic skeletal disease, requiring integration of oncologic principles with complex musculoskeletal reconstruction. Advances in molecular classification, cross-sectional imaging, systemic therapy, and surgical technology have substantially transformed contemporary clinical practice. This comprehensive review synthesises current evidence across epidemiology, tumour biology, diagnostic strategies, staging systems, multidisciplinary management, reconstructive techniques, complication profiles, and survi

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

BACKGROUND: Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elder

Epidemiology of Bacteremia in Patients with Hematological Malignancies and Hematopoietic Stem Cell Transplantation and the Impact of Antibiotic Resistance on Mortality: Data from a Multicenter Study in Argentina.

The epidemiology of bacteremia and the antibiotic resistance profile (ARP) of Gram-negative bacilli (GNB) in hematological malignancies (HM) and hematopoietic stem cell transplant (HSCT) patients may differ according to geographic region. In addition, multidrug-resistant organisms (MDROs) may impact mortality. This is a prospective, observational, and multicenter study. The first episodes of bacteremia in adult patients with HM or HSCT were included. The risk factors for 30-day mortality were identified. One thousand two hundred and seventy-seven episodes were included (HM: 920; HSCT: 357). GN

Integrative multi-omics analysis reveals CXCL10-driven inflammation and TREM2 + macrophage-plasma cell survival niche as hallmarks of late-stage rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is characterized by persistent synovial inflammation, yet the molecular mechanisms distinguishing early from late-stage disease remain incompletely elucidated. Identifying stage-specific biomarkers and pathogenic cellular interactions is crucial for precision medicine. OBJECTIVE: To comprehensively characterize the transcriptomic landscape and cellular composition of early versus late RA synovium, identify diagnostic biomarkers, and elucidate key pathogenic cell–cell interactions driving disease chronicity. METHODS: Synovial tissues from 51 RA patients (13

Deep Learning-Based 30-Day Mortality Prediction in Critically Ill Bone and Bone Marrow Metastasis Patients: A Multicenter Retrospective Cohort Study.

Bone and bone marrow Metastasis (BBM) are life-threatening complications of advanced malignancies, frequently requiring intensive care and associated with high short-term mortality. However, prognostic tools specifically tailored to critically ill BBM patients are limited. This multicenter cohort study aimed to develop and validate deep learning models for predicting 30-day mortality using ICU data from MIMIC-IV, eICU-CRD, and the First Affiliated Hospital of Xinjiang Medical University. After univariate screening, XGBoost-Boruta and Lasso regression identified 11 key clinical features within

Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset.

The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV (Medical Information Mart for Intensive Care, version IV), the largest publicly available healthcare dataset, and conduct comprehensive analyses of interpretability as w

The reporting quality and transparency of orthopaedic studies using Bayesian analysis requires improvement: A systematic review.

BackgroundBayesian methods are being used more frequently in orthopaedics. To advance the use and transparent reporting of Bayesian studies, reporting guidelines have been recommended. There is currently little known about the use or applications of Bayesian analysis in orthopedics including adherence to recommended reporting guidelines. The objective is to investigate the reporting of Bayesian analysis in orthopedic surgery studies; specifically, to evaluate if these papers adhere to reporting guidelines.MethodsWe searched PUBMED to December 2nd, 2020. Two reviewers independently identified s

An explainable predictive machine learning model of osteopenia for perimenopausal women based on clinical data: a retrospective single-center study.

BACKGROUND: Osteoporosis is increasingly prevalent, yet early detection remains difficult. This study aimed to develop a machine learning (ML)-based model for identifying individuals at risk of osteopenia using clinical data. METHODS: Female participants who were aged 45 years and above with complete femoral neck BMD data were included. A total of 1,108 participants were divided into training (70%) and test (30%) datasets. Various ML-based algorithms (random forest (RF), least absolute shrinkage and selection operator (LASSO), gradient boosting decision tree (GBDT) were used to identify key p

Материалы Международного общества по картированию раковых заболеваний (ИСИС) 18th Ежегодный учебный курс: Ментон, Франция. 7-9 октябрь 2018

Аннотация не предоставлена источником. Откройте карточку для перехода к публикации.

Artificial intelligence in drug discovery and development.

Artificial intelligence-integrated drug discovery and development has accelerated the growth of the pharmaceutical sector, leading to a revolutionary change in the pharma industry. Here, we discuss areas of integration, tools, and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them.

Совпадение в полном тексте источника
… Algorithms, such as Nearest-Neighbour classifiers, RF, extreme learning machines, SVMs, and deep neural networks (DNNs), are used for VS based on synthesis feasibility and can also predict in vivo activity and toxicity 31 , 33 . …

The Surgical Management of Proximal Femoral Metastases: A Narrative Review.

The proximal femur is a common location for the development of bony metastatic disease. Metastatic bone disease in this location can cause debilitating pain, pathologic fractures, reduced quality of life, anemia or hypercalcemia. A thorough history, physical examination and preoperative investigations are required to ensure accurate diagnosis and prognosis. The goals of surgical management is to provide pain relief and return to function with a construct that provides stability to allow for immediate weightbearing. Current surgical treatment options include intramedullary nailing, hemiarthropl

Outcome Prediction of Hematologic Malignancy in Critically Sick People.

People with hematologic malignancies (HM) frequently postulate intensive care unit (ICU) hospitalization due to organ damage caused by the disease process or treatment-related consequences. This study is aimed at looking at mortality and sign factors in adult patients with hematologic malignancy (HM) who have been hospitalized in the ICU. Death was one quality indicator; researchers used a machine learning approach to find determinants of death. As per the study, there have been 206 patients hospitalized in the ICU (mean age: 51.3 ± 13.6 years; 60% male). The average length of stay was three d

Возраст мозга при множественном склерозе: исследование с глубоким обучением и традиционным машинным обучением.

«Возраст мозга» — это количественная оценка биологического возраста мозга и общие усилия по измерению нейродегенерации независимо от типа болезни. При множественном склерозе ускоренное старение мозга связано с начислением инвалидности. Искусственный интеллект стал многообещающим инструментом для оценки и количественной оценки воздействия нейродегенеративных заболеваний. Несмотря на существование многочисленных моделей ИИ, наблюдается заметная нехватка сравнительных данных снимков для традиционного машинного обучения по сравнению с глубоким обучением в таких условиях, как множественный склероз.

Сегментация с искусственным интеллектом и автоматизированный расчет индекса каллосу с корпусом при множественном склерозе.

ЦЕЛИ: Определить различия в индексе каллосум с корпусом (ККИ) между пациентами, страдающими хроническим множественным склерозом (MS), и здоровыми людьми, а также оценить сегментацию каллосумов с корпусом у пациентов с КБ с использованием технологий искусственного интеллекта. CCI может быть надежно измерен на магнитно-резонансной съемке (MRI) и предложен в качестве возможного маркера атрофии мозга в MS. В ходе этого исследования были сканированы 150 пациентов, страдающих МЗ (продолжительность болезни - 12,6 5,9 года), и 150 здоровых пациентов. Индекс corpus callosum был измерен вручную на основ