Результаты поиска

Quantitative Imaging and Radiomics in Multiple Myeloma: A Potential Opportunity?

Multiple Myeloma (MM) is the second most common type of hematological disease and, although it is rare among patients under 40 years of age, its incidence rises in elderly subjects. MM manifestations are usually identified through hyperCalcemia, Renal failure, Anaemia, and lytic Bone lesions (CRAB). In particular, the extent of the bone disease is negatively related to a decreased quality of life in patients and, in general, bone disease in MM increases both morbidity and mortality. The detection of lytic bone lesions on imaging, especially computerized tomography (CT) and Magnetic Resonance I

Shaping the future of multiple myeloma with artificial intelligence and digital twins: from concept to clinic.

Multiple myeloma (MM) is an incurable hematological malignancy with significant clinical and biological heterogeneity. Despite development and refinement of numerous prognostic models for MM, challenges with accurate and reliable risk stratification remain, highlighted by unexpected, early relapse or progression of disease in patients termed functional high-risk (FHR). To improve decision-making and optimise outcome, there is an unmet need for precise identification of high-risk (HR) patients, to enable tailored therapeutic strategies. With a complex and rapidly evolving treatment landscape, a

Real-World Data and Machine Learning to Predict Cardiac Amyloidosis.

(1) Background: Cardiac amyloidosis or "stiff heart syndrome" is a rare condition that occurs when amyloid deposits occupy the heart muscle. Many patients suffer from it and fail to receive a timely diagnosis mainly because the disease is a rare form of restrictive cardiomyopathy that is difficult to diagnose, often associated with a poor prognosis. This research analyses the characteristics of this pathology and proposes a statistical learning algorithm that helps to detect the disease. (2) Methods: The hospitalization clinical (medical and nursing ones) records used for this study are the ba

Автоматическое отслеживание активного движения в многоразовом склерозе с использованием неконтролируемого машинного обучения

Справочная информация: Выявление активных повреждений в магнитно-резонансной съемке (MRI) имеет решающее значение для диагностики и планирования лечения множественных склерозов (MS). Активные повреждения МРТ выявляются после применения противопоказательных агентов на основе гадолиния (ГБЦА). Однако недавние исследования показали, что неоднократное применение ГБЦН приводит к накоплению ГД в тканях. Кроме того, администрация ГБКА увеличивает расходы на медицинское обслуживание. Таким образом, для повышения безопасности пациентов и снижения расходов на медицинское обслуживание важное значение име

Совпадение в полном тексте источника
… The objective of this study was to demonstrate that unsupervised NLDR methods outperform supervised machine learning methods in identifying active lesions. To accomplish this, we introduce key differences between LDR and NLDR. …

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

A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.

Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nation

Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clini

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has bee

Модель диагностики болезней MS на основе плотности с использованием алгоритма поиска капуцинов и ансамбля глубоких нейронных сетей.

Множественный склероз (MS) — серьезная болезнь мозга, которая постоянно разрушает клетки мозга, влияет на зрение, баланс, мышечный контроль и повседневную активность. В этом исследовании используется взвешенное сочетание глубоких нейронных сетей и методов оптимизации для диагностики заболеваний MS. Этот метод использует кусочки изображений магнитного резонанса (MRI) в качестве входных. Затем, после предварительной обработки, процесс сегментации и идентификации района, представляющего интерес (РОИ), осуществляется с использованием сочетания алгоритма " нечеткие " c-means (FCM) и алгор

Искусственный интеллект и машинное обучение в нейрогенной дисфункции нижних мочевых путей и травме спинного мозга: текущее состояние, новые технологии и этические перспективы.

Искусственный интеллект (АИ) и машинное обучение все чаще применяются во всех областях урологии, при этом особое значение придается нейрогенной дисфункции нижних мочевых путей (НЛУТД) у лиц с повреждениями спинного мозга (КВП). Кроме того, существуют особые перспективы в том, что касается лечения NLUTD — сложного, пожизненного состояния, затрагивающего людей с SCI, а также связанного с множественным склерозом, болезнью Паркинсона и другими неврологическими расстройствами. В этой статье содержится всеобъемлющий анализ нынешней и формирующейся роли МА в нейроурологии с уделением особого внимания

Прогнозирование релапирования и индивидуализированная модель лечения-воздействия при повторном склерозе: систематический обзор.

ВВЕДЕНИЕ: Индивидуальное прогнозирование результатов, связанных с рецидивом, может способствовать отбору и мониторингу лечения при повторном склерозе. Однако существующие исследования существенно отличаются друг от друга по клиническим целям, целевым результатам, подходам к моделированию и стратегиям проверки. МЕТОДЫ: Мы провели систематический обзор в соответствии с ПРИСМА 2020. Пубмед, Web of Science, IEEE Xplore и Scopus были найдены записи на английском языке, опубликованные на английском языке. 1 Январь 2010 в адрес 14 Январь 2026. Были включены исследования по разработке или оценке модел

Совпадение в полном тексте источника
… Supervised Machine Learning Models for Multiple Sclerosis Relapse Probability Prognosis using Clinical Data . 2023 9th International Conference on Web Research (ICWR) . ( 2023 ). p. 150–155 doi: 10.1109/ICWR57742.2023.10139225 16. Walsh K Shah R Armstrong JK Moore ES Oliver BJ . …

Цифровая двойная модель для подкрепления клинического решения на основе фактических данных при многократном лечении миэломы.

За последнее десятилетие в области лечения миеломы (ММ) был достигнут значительный прогресс. Несмотря на эффективность новых веществ, реакция пациентов, как правило, остается весьма непредсказуемой. С ростом когнитивной нагрузки, создаваемой за счет сложного и меняющегося ландшафта лечения, инструменты оказания помощи, основанные на данных, становятся все более популярными. Подходы, основанные на модели, такие как цифровые близнецы (ДТ), позволяют имитировать возможные ответы на набор входных параметров, основанных на ретроспективных наблюдениях. В контексте поддержки принятия решений о лечени

Artificial intelligence assisted preoperative planning and 3D-printing guiding frame for percutaneous screw reconstruction in periacetabular metastatic cancer patients.

BACKGROUND: The percutaneous screw reconstruction technique, known as the "Tripod Technique," has demonstrated favorable clinical outcomes in the management of metastatic periacetabular lesions, as evidenced by our prior investigations and corroborated by independent studies. Nevertheless, there is a steep learning curve in handling this technique, with possible complications such as intraarticular screw placement. METHODS: Preoperative pelvic CT scans were acquired before surgery and utilized for the guiding frame design. A convolutional neural network model was trained with annotated data t

Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence.

The integration of artificial intelligence (AI) into medical diagnostics is transforming the landscape of healthcare, particularly in hematology. AI technologies, leveraging advanced machine learning algorithms and big data analytics, are revolutionizing the diagnosis of hematological disorders such as anemia, leukemia, and lymphoma. This review explores how AI enhances diagnostic accuracy, efficiency, and patient outcomes by processing complex datasets and identifying patterns beyond human capability. AI-driven advancements in hematology include innovations in image analysis, genomic data int

Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.

Artificial Intelligence and machine learning are increasingly used to interrogate complex biological data. This systematic review evaluates their application to multi-omics for the molecular characterization of hematological malignancies, an area with unmet clinical need. We searched PubMed, Embase, Institute of Electrical and Electronics Engineers Xplore, and Web of Science from January 2015 to December 2024. Two reviewers screened records, extracted data, and used a modified appraisal emphasizing explainability, performance, reproducibility, and ethics. From 2847 records, 89 studies met incl

Автоматизированная радиомасса для прогнозирования реакции терапии и минимальных остаточных заболеваний от базовой МРТ в множестве миеломы.

Это многоцентровое исследование снимков было направлено на создание и проверку автоматизированных моделей радиомикалов, прогнозирующих терапию (TR) и минимальную остаточную реакцию (MRD) в недавно диагностированном миеломе (MM) из базового МРТ. Ретроспективно, 118 Пациенты ММ из GMMG-HD7 судебное разбирательство (EudraCT: 2017-004768-37) с данными о TR и/или MRD после индуктивной терапии и базовой МРТ. Данные были разделены по центрам в комплект учебных материалов (центр 1-2; n = 79) и комплект испытаний (центр 3-10; n = 39). TR был классифицирован как очень хороший частичный ответ или лучше п

Unlocking artificial intelligence, machine learning and deep learning to combat therapeutic resistance in metastatic castration-resistant prostate cancer: a comprehensive review.

Metastatic castration-resistant prostate cancer (mCRPC) remains a formidable clinical challenge despite advancements in therapy. This narrative review explores the role of artificial intelligence (AI), machine learning and deep learning in addressing therapeutic resistance in mCRPC. AI-driven approaches leverage integrated datasets encompassing genomics, proteomics and clinical parameters to uncover molecular mechanisms, predict treatment responses and identify biomarkers of resistance. These methodologies promise personalised treatment strategies tailored to individual patient profiles. Howev

Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.

IntroductionArtificial intelligence (AI) is a branch of technology enabling machines to emulate complex human skills; it can also entail problem-solving using bioinspired methods. It is used for automating systematic literature reviews (SLR), that is, defining a clinical question, locating relevant literature, preliminary screening, study evaluation, data extraction and analysis. Title and abstract screening is one of the most time-consuming and error-prone phases involved in developing a systematic review. While AI promises to expedite this process, adopting it faces challenges due to concern

A machine-learning-derived online prediction model based on inflammatory and nutritional composite indicators for acute kidney injury in sepsis patients with multiple myeloma.

BACKGROUND: Acute kidney injury (AKI) frequently complicates sepsis in patients with multiple myeloma (MM), yet no validated predictive tool integrates the inflammatory and nutritional disturbances specific to this dual-disease population. We aimed to develop and externally validate an interpretable machine learning (ML) model-based on composite inflammatory-nutritional indices-to predict in-hospital AKI in septic patients with MM, and to deploy it as a web-based point-of-care tool. METHODS: In this retrospective cohort study, 362 septic MM patients (development cohort: 253 training, 109 inter

Совпадение в полном тексте источника
… Development and validation of machine learning models Six supervised machine learning algorithms were developed to predict in-hospital AKI: Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost …

Prediction of Acute Kidney Injury in Oncology: From Clinical Risk Scores to AI-Enabled Precision Onco-Nephrology.

Background/Objectives: Acute kidney injury (AKI) is a common complication across the cancer care continuum and can compromise kidney function, delay or interrupt anticancer therapy, and worsen renal and oncologic outcomes. This state-of-the-art narrative review examines conventional clinical risk scores and artificial intelligence (AI)- and machine learning (ML)-based approaches to AKI prediction in oncology and identifies priorities for clinical translation. Methods: We conducted iterative searches of PubMed and Google Scholar through July 2026 and screened reference lists of relevant primary