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AI-driven prognostics in pediatric bone marrow transplantation: a CAD approach with Bayesian and PSO optimization.

Bone marrow transplantation (BMT) is a critical treatment for various hematological diseases in children, offering a potential cure and significantly improving patient outcomes. However, the complexity of matching donors and recipients and predicting post-transplant complications presents significant challenges. In this context, machine learning (ML) and artificial intelligence (AI) serve essential functions in enhancing the analytical processes associated with BMT. This study introduces a novel Computer-Aided Diagnosis (CAD) framework that analyzes critical factors such as genetic compatibili

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… In this context, some supervised machine learning techniques were examined using an 80–20 train-test split ratio. The Chi-square feature selection method was used to choose only the best 11 of the 59 database features to ensure prediction with the least time and resources. …

Computational proteomics to enhance personalized treatment of COVID-19 and Long COVID.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to significant global health burden, including both acute infections and persistent post-acute sequelae, also known as Long COVID (LC) in survivors. While clinical management has reduced case-fatality rates, a substantial proportion of patients develop LC, a heterogeneous syndrome with long-term symptoms. This complex continuum requires therapeutic strategies for both the acute and chronic phases. Plasma proteomics has emerged as a powerful tool in precision medicin

Opportunistic CT for Prediction of Adverse Postoperative Events in Patients with Spinal Metastases.

The purpose of this study was to assess the value of body composition measures obtained from opportunistic abdominal computed tomography (CT) in order to predict hospital length of stay (LOS), 30-day postoperative complications, and reoperations in patients undergoing surgery for spinal metastases. 196 patients underwent CT of the abdomen within three months of surgery for spinal metastases. Automated body composition segmentation and quantifications of the cross-sectional areas (CSA) of abdominal visceral and subcutaneous adipose tissue and abdominal skeletal muscle was performed. From this,

Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.

BackgroundPatients with cancer often have unmet psychosocial needs. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This work used natural language processing to predict which patients will see a counsellor or psychiatrist from a patient's initial oncology consultation document. We believe this is the first use of artificial intelligence to predict psychiatric outcomes from non-psychiatric medical documents.MethodsThis retrospective prognostic study used data from 47,625 patients at BC Cancer. We analyzed initial oncology consultation documents

A Scalable Radiomics- and Natural Language Processing-Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study.

BackgroundThe identification of objective pain biomarkers can contribute to an improved understanding of pain, as well as its prognosis and better management. Hence, it has the potential to improve the quality of life of patients with cancer. Artificial intelligence can aid in the extraction of objective pain biomarkers for patients with cancer with bone metastases (BMs).ObjectiveThis study aimed to develop and evaluate a scalable natural language processing (NLP)- and radiomics-based machine learning pipeline to differentiate between painless and painful BM lesions in simulation computed tomo

Design for a digital twin in clinical patient care.

Digital Twins hold great potential to personalize clinical patient care, provided the concept is translated to meet specific requirements emerging from established clinical workflows. We present a general and unspecialized Digital Twin design combining knowledge graphs and ensemble learning to reflect the entire patient's clinical journey and assist clinicians in their decision-making. Such a design is predictive, modular, evolving, informed, interpretable and explainable, thus opening broad clinical applications.

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… A general potential risk for the introduction of biases comes from supervised machine learning, focusing on the identification of structures frequently represented within the data. …

Искусственный интеллект основывается на достижениях в области наномедицины для лечения мозговых расстройств: обновленный обзор повествования.

Наномедицина - это наномасштабные, биосопоставимые материалы, которые предлагают перспективные альтернативы традиционным методам лечения расстройств мозга. Технологические достижения в области искусственного интеллекта (АИ), в частности машинного обучения (МЛ) и глубокого обучения (ДЛ), трансформируют область наномедицины путем совершенствования диагностики заболеваний, биомаркировки, прогнозирования и мониторинга заболеваний, целевых поставок наркотиков и терапевтического вмешательства, а также содействия расчетным и методологическим разработкам. Эти успехи могут быть достигнуты за счет анали

From virtual to reality: innovative practices of digital twins in tumor therapy.

BackgroundAs global cancer incidence and mortality rise, digital twin technology in precision medicine offers new opportunities for cancer treatment.ObjectiveThis study aims to systematically analyze the current applications, research trends, and challenges of digital twin technology in tumor therapy, while exploring future directions.MethodsRelevant literature up to 2024 was retrieved from PubMed, Web of Science, and other databases. Data visualization was performed using R and VOSviewer software. The analysis includes the research initiation and trends, funding models, global research distri

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.

Цифровые биомаркеры и АИ для дистанционного мониторинга утомительного прогресса в области неврологических расстройств: подключение механизмов к клиническому применению.

Цифровые биомаркеры для мониторинга усталости в неврологических расстройствах представляют собой новаторский подход к преодолению разрыва между механистическим пониманием и клиническим применением. В настоящем перспективном документе рассматривается вопрос о том, каким образом меры, разработанные с помощью смартфонов и проанализированные с помощью искусственных методов интеллекта, могут трансформировать оценку усталости из субъективной, эпизодической отчетности в непрерывный, объективный мониторинг. Предлагаемые рамки для цифровой фенотификации на основе смартфонов содержат пассивные данные (о

Risk factors associated with skeletal-related events following discontinuation of denosumab treatment among patients with bone metastases from solid tumors: A real-world machine learning approach.

BACKGROUND: Clinical practice guidelines recommend the use of bone-targeting agents for preventing skeletal-related events (SREs) among patients with bone metastases from solid tumors. The anti-RANKL monoclonal antibody denosumab is approved for the prevention of SREs in patients with bone metastases from solid tumors. However, real-world data are lacking on the impact of individual risk factors for SREs, specifically in the context of denosumab discontinuation. PURPOSE: We aim to identify risk factors associated with SRE incidence following denosumab discontinuation using a machine learning

Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT.

Bone metastasis from breast cancer significantly elevates patient morbidity and mortality, making early detection crucial for improving outcomes. This study utilizes radiomics to analyze changes in the thoracic vertebral bone marrow microenvironment from chest computerized tomography (CT) images prior to bone metastasis in breast cancer, and constructs a model to predict metastasis.MethodsThis study retrospectively gathered data from breast cancer patients who were diagnosed and continuously monitored for five years from January 2013 to September 2023. Radiomic features were extracted from the

Машинное обучение для интерпретации серума протеина и иммунофиксации электрофореза в мультипликационной миеломе: обзор скопирования.

Фон: Для диагностики множественной миэломы (ММ) необходимы электрофорез сухого белка (СФЭ) и электрофорез иммунофиксации (ИФЕ). Методы машинного обучения (ML) стали потенциальным подходом к автоматизации процесса интерпретации электрофореза в целях повышения эффективности диагностики и увеличения времени перехода. В ходе этого аналитического обзора были выявлены нынешние виды применения ML в диагностике и классификации MM с уделением особого внимания автоматизированному толкованию ОИН и IFE. Методы: базы данных PubMed, Web of Science и Scopus были подвергнуты обыску в соответствии с руководящи

The role of AI in oncology: present applications and future horizons.

Artificial intelligence (AI) offers a powerful means to accelerate precision oncology by individualizing care in an era of rapidly evolving treatment paradigms. However, there is limited regulatory oversights for safe clinical implementation of AI, and concerns surrounding data bias, ownership, and privacy have hindered broad integration into healthcare practice. In this perspective, we offer a forward-looking roadmap for the dissemination of AI in oncology. We discuss the role for AI in guiding biomarker-driven patient selection for clinical trials and in facilitating both personalized drug s

Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives.

Medical biosensors have set the basis of medical diagnostics, and Artificial Intelligence (AI) has boosted diagnostics to a great extent. However, false results are evident in every method, so it is crucial to identify the reasons behind a possible false result in order to control its occurrence. This is the first critical state-of-the-art review article to discuss all the commonly used biosensor types and the reasons that can give rise to potential false results. Furthermore, AI is discussed in parallel with biosensors and their misdiagnoses, and again some reasons for possible false results

Искусственный интеллект в педиатрической нейрорадиологии: текущий ландшафт, проблемы и будущие направления.

В настоящем обзоре содержится информация о нынешнем ландшафте искусственного интеллекта (АИ) в области педиатрической и эмбриональной нейрорадиологии, критическая оценка текущей практики, барьеров на пути к клиническому усыновлению и будущего потенциала. Мы искали рецензированные исследования за последнее десятилетие, сфокусировавшись на сегментации изображений, обнаружении повреждений, классификации, прогнозировании и поддержке клинического решения при педиатрической съемке мозга. Особое внимание было уделено уникальным педиатрическим факторам, таким, как развитие мозга и нехватка данных. Мет

Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models.

PurposeOsteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. Deep-learning and specific models achieve highly accurate results using different imaging modalities. The primary purpose of this research was to develop unimodal and multimodal deep-learning-based diagnostic models to predict bone mineral loss of the lumbar vertebrae using magnetic resonance (MR) and computed tomography (CT) imaging.MethodsPatients wh

Prediction of myelosuppression in cervical cancer after concurrent chemoradiotherapy by CT radiomics-based model.

BackgroundConcurrent chemoradiotherapy (CCRT) was highly effective in treating cervical cancer (CC) but raised the risk of bone marrow suppression. However, models to predict the risk of myelosuppression in CC patients after CCRT based on computed tomography (CT) imaging histology are immature.MethodsThe Region of interest (ROI) of the CT images was segmented, and radiomic features were extracted. Next, important features were further selected. The training and test sets were split 7:3, and 8 machine learning algorithms were applied to classify the features in the training set. The model's per

Текстурный анализ и искусственный интеллект как инструменты поддержки решений в диагностике множественного склероза - систематический обзор.

ВВЕДЕНИЕ: Магнитная резонансная визуализация (MRI) обычно используется для обнаружения и диагностики множественных склерозов (MS), часто дополняемых поясничными проколами - очень инвазивным методом для подтверждения диагноза. Кроме того, МРТ периодически повторяется для контроля за развитием и эффективностью лечения заболеваний. В ходе недавних исследований основное внимание уделялось применению искусственного интеллекта (АИ) и радиомики при обработке медицинских изображений, диагностике и планировании лечения. МЕТОДЫ: Был проведен обзор существующей литературы с анализом использования моделей

Stress cardiomyopathy in hospitalized patients with cancer: machine learning analysis by primary malignancy type.

AimsPrevious studies have shown that patients with stress (Takotsubo) cardiomyopathy (SC) and cancer have higher in-hospital mortality than patients with SC alone. No studies have examined outcomes in patients with active cancer and SC compared to patients with active cancer without SC. We aimed to assess the potential association between primary malignancy type and SC and their shared interaction with inpatient mortality.Methods and resultsWe analysed SC by primary malignancy type with propensity score adjusted multivariable regression and machine learning analysis using the 2016 United State