Global Conference on Medical and Health Sciences
Global Conference on Medical and Health Sciences Aim & Scope: This conference focuses on medical research, clinical studies, public health, healthcare systems, and biomedical sciences. It promotes evidence-based research addressing regional and global health challenges. About E-Conferencia Platform: E-Conferencia ensures ethical handling of medical research publications, providing open access to conference outputs while following responsible academic publishing practices. Hosted Online from: Madrid, Spain Schedule: 14th of Each Month
ОЦЕНКА ЭФФЕКТИВНОСТИ КОМБИНИРОВАННОЙ ГАСТРЭКТОМИИ ПРИ ОСЛОЖНЕННЫХ ФОРМАХ ПРОРАСТАНИЯ ОПУХОЛИ В ХВОСТ ПОДЖЕЛУДОЧНАЯ ЖЕЛЕЗЫ РАКА ЖЕЛУДКА
Улучшить результаты хирургического лечения при осложненных формах прорастания опухоли в хвост поджелудочная железы рака желудка.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
РЕЗУЛЬТАТЫ КОМБИНИРОВАННОЙ ГАСТРЭКТОМИИ ПРИ МЕСТНОРАСПРОСТРАНЕННОМ РАКЕ ЖЕЛУДКА
Оценить непосредственные результаты комбинированных гастрэктомий с дистальной гемипанкреатэктомией со спленэктомией при местраспространенном раке желудка.
INFLUENCE OF THE EXTENT OF RADICAL SURGICAL RESECTION ON LONG-TERM SURVIVAL IN LOCALLY ADVANCED GASTRIC CANCER
Radical surgical resection remains the cornerstone of curative treatment for locally advanced gastric cancer (LAGC). However, the optimal extent of surgical resection remains controversial, particularly in patients with advanced local tumor invasion. The choice between total gastrectomy, subtotal gastrectomy, and combined multivisceral resections may significantly influence both postoperative outcomes and long-term survival. Although TNM stage remains the principal prognostic indicator, accumulating evidence suggests that the extent and radicality of surgical treatment play an equally important role in achieving durable oncological control. Evaluation of long-term outcomes following different types of radical surgical procedures is therefore essential for optimizing treatment strategies and improving survival in patients with LAGC.
Locally advanced gastric cancer; radical gastrectomy; surgical resection; R0 resection; long-term survival; multivisceral resection; prognosis; clinicopathological factors.
AUTOMATED WHITE MATTER LESION SEGMENTATION USING DEEP LEARNING: PERFORMANCE BENCHMARKS IN MULTIPLE SCLEROSIS AND CEREBROVASCULAR DISEASE
White matter lesions visible on T2-FLAIR MRI serve as primary diagnostic and monitoring biomarkers in both multiple sclerosis and cerebrovascular disease, yet their manual delineation by neuroradiologists is slow, resource-intensive, and variable between raters. Deep learning segmentation models — particularly three-dimensional U-Net architectures and vision transformers applied to multi-modal MRI — have been developed as automated alternatives. A review of 36 studies published from 2016 to 2024 indicates that these models achieve Dice similarity coefficients of 0.72–0.89 for MS lesion segmentation and 0.68–0.84 for cerebrovascular WML, values that overlap substantially with the reported range of inter-expert agreement. Lesion volume estimates from automated segmentation correlate with manual reference measurements at r = 0.91–0.98. For longitudinal MS monitoring specifically, automated detection of new or enlarging lesions reaches sensitivity of 78–93% with specificity of 88–97%. These capabilities have practical implications for MRI services operating under neuroradiological workforce constraints, including those in Uzbekistan where specialist neuroimaging interpretation is concentrated in Tashkent.
white matter lesions; deep learning; MRI; FLAIR; multiple sclerosis; cerebrovascular disease; lesion segmentation; neuroimaging
PREDICTING POST-STROKE COGNITIVE IMPAIRMENT WITH MACHINE LEARNING: A REVIEW OF NEUROIMAGING, CLINICAL, AND BLOOD BIOMARKER APPROACHES
Cognitive decline following stroke — ranging from subtle attentional deficits to frank vascular dementia — affects between 30 and 40 percent of survivors and is a major but frequently underrecognised determinant of long-term functional outcome. Identifying which patients will develop significant post-stroke cognitive impairment (PSCI) at the time of the acute admission would allow targeted cognitive rehabilitation, intensified vascular risk factor management and timely family counselling. Machine learning models that integrate acute neuroimaging parameters, clinical characteristics and blood biomarkers have been proposed as a means of generating such early risk estimates. A review of 27 relevant studies published between 2017 and 2024 finds that multimodal ML models achieve AUC values of 0.82–0.93 for six-month PSCI prediction, substantially exceeding the 0.68–0.76 range associated with NIHSS-based scoring. White matter lesion burden, infarct location and serum neurofilament light chain concentration at 48 hours emerged as the features contributing most consistently to model performance. These findings have direct relevance for stroke services in Uzbekistan, where the burden of stroke-attributable cognitive impairment is high and dedicated cognitive rehabilitation infrastructure is limited.
post-stroke cognitive impairment; machine learning; vascular dementia; MRI; neurofilament; NIHSS; stroke; Uzbekistan
INTERNET OF MEDICAL THINGS IN INPATIENT NEUROLOGY: CONTINUOUS MONITORING, AUTOMATED SEIZURE DETECTION, AND EARLY WARNING SYSTEMS
The interval between the onset of neurological deterioration in a hospitalised patient and its recognition by clinical staff represents a window of preventable harm — a window that conventional nursing observation schedules, constrained by staffing ratios and cognitive load, are often too wide to close. The Internet of Medical Things, comprising connected physiological monitors, bedside EEG systems and wearable acquisition devices that transmit continuous data streams to central analytical servers, offers a technical means of narrowing this window substantially. Machine learning algorithms applied to these streams can perform pattern recognition tasks — detecting non-convulsive seizure activity, scoring real-time early warning scores, flagging haemodynamic instability — at a temporal resolution that intermittent human observation cannot match. A review of 33 studies published between 2018 and 2024 finds that IoMT-based multi-parameter early warning systems predict neurological intensive care escalation with AUC of 0.86–0.93, reduce time-to-intervention for deterioration events by a mean of 44 minutes, and detect non-convulsive status epilepticus with sensitivity of 88–96% at clinically acceptable false alarm rates. The potential relevance of these findings for inpatient neurology services in Uzbekistan, where nursing ratios and monitoring equipment availability impose real constraints on observation intensity, is discussed.
IoMT; smart neurology ward; EEG; seizure detection; early warning; continuous monitoring; clinical workflow; Uzbekistan
AI-ASSISTED DIFFERENTIAL DIAGNOSIS IN HEADACHE DISORDERS: MACHINE LEARNING APPROACHES TO MIGRAINE, CLUSTER HEADACHE, AND TENSION-TYPE HEADACHE CLASSIFICATION
Headache disorders rank among the most disabling neurological conditions worldwide, yet clinical differentiation between migraine, tension-type headache and cluster headache continues to pose diagnostic challenges even for experienced neurologists. The overlap in symptom profiles, reliance on patient recall, and time constraints in outpatient settings collectively contribute to diagnostic delays that may extend over several years — particularly in the case of cluster headache. Over the past decade, machine learning methods have been applied to structured symptom questionnaires, free-text diary entries and wearable physiological signals with the aim of automating headache subtype classification according to ICHD-3 criteria. A review of 28 relevant studies published between 2018 and 2024 reveals that ensemble classifiers such as random forests and gradient boosting achieve three-class diagnostic accuracy in the range of 82–93%, considerably outperforming single-symptom rule-based screening. NLP models trained on headache diary corpora attain migraine detection sensitivity of 86–94%. Multimodal wearable approaches combining photoplethysmography and actigraphy reach episode-level classification accuracy of 76–88%. These figures carry particular relevance for neurology outpatient practice in Uzbekistan, where headache complaints account for roughly one quarter of referrals and access to specialist headache clinics remains limited to the largest urban centres.
headache; migraine; ICHD-3; machine learning; NLP; differential diagnosis; wearable sensors; Uzbekistan
NEUROIMAGING AI FOR TMS TARGET LOCALISATION: EVIDENCE FOR INDIVIDUALISED COIL PLACEMENT IN DEPRESSION AND NEUROREHABILITATION
Transcranial magnetic stimulation has moved steadily from a research instrument to a clinically established non-invasive treatment for conditions including pharmacoresistant major depression, obsessive-compulsive disorder and post-stroke motor impairment. Its effectiveness, however, is not uniform across patients, and a significant proportion of this variability traces back to imprecision in coil positioning. The standard anatomical approach — placing the coil over a scalp landmark estimated to overlie the left dorsolateral prefrontal cortex — frequently misses the functionally defined target by margins large enough to reduce therapeutic yield. Artificial intelligence applied to structural and functional MRI provides an alternative: automated, subject-specific identification of cortical targets based on connectivity fingerprints rather than population-average anatomy. A review of 31 studies published between 2017 and 2024 indicates that AI-guided targeting reduces positioning error by 35–60% relative to landmark methods, and that functional connectivity-guided DLPFC stimulation achieves antidepressant response rates of 58.4% versus 41.2% for anatomical targeting in controlled comparisons. Reinforcement learning algorithms for adaptive parameter optimisation have shown further gains in cortical excitability induction. The implications of these findings for TMS service development within Uzbekistan's neurological infrastructure are considered.
TMS; neuromodulation; AI targeting; DLPFC; fMRI; depression; motor rehabilitation; Uzbekistan
COURSE OF ANEMIA IN DIFFUSE TOXIC GOITER
This article describes the causes, clinical course, and specific features of anemia in patients with diffuse toxic goiter (thyrotoxicosis). It analyzes the effects of thyroid hormone overproduction on accelerated metabolism, impaired absorption of iron and vitamin B12, and the role of autoimmune factors in the development of anemia. The importance of timely diagnosis and correction of anemia alongside the treatment of thyrotoxicosis to improve patients' quality of life is emphasized.
Diffuse toxic goiter, thyrotoxicosis, anemia, thyroid gland, metabolism, iron deficiency.
MACHINE LEARNING IN STROKE OUTCOME PREDICTION: SYSTEMATIC REVIEW OF PROGNOSTIC MODELS AND CLINICAL DECISION SUPPORT APPLICATIONS
Stroke is the second leading cause of death and the leading cause of acquired disability globally, with approximately 13.7 million new strokes occurring annually and 5.5 million resulting in death. Accurate prediction of stroke outcomes — functional recovery, mortality, and complication risk — is essential for informed clinical decision making, rehabilitation planning, patient and family counselling, and healthcare resource allocation. Machine learning models applied to clinical, imaging, and biological data from acute stroke patients have demonstrated superior prognostic performance compared with established clinical scoring systems, achieving AUC values of 0.80–0.95 for 90-day functional outcome prediction compared with 0.70–0.80 for traditional scores (NIHSS, ASPECTS). This systematic review evaluates 41 studies of machine learning stroke outcome prediction models published between 2016 and 2024, analysing input variables, model architectures, performance benchmarks, and clinical integration prospects. The implications for stroke care in Uzbekistan, where stroke incidence is among the highest in Central Asia and specialist neurology capacity is constrained, are examined.
stroke; machine learning; outcome prediction; functional recovery; clinical decision support; NIHSS; neural networks
WEARABLE SENSORS FOR REAL-TIME SEIZURE DETECTION: FROM LABORATORY VALIDATION TO CLINICAL DEPLOYMENT
Epileptic seizures are associated with injury risk, sudden unexpected death in epilepsy (SUDEP), and profound impacts on quality of life and independence. Real-time seizure detection through wearable sensors — monitoring electrodermal activity, accelerometry, heart rate variability, and electromyography — offers the potential for timely alert generation, enabling caregiver intervention and emergency response that could reduce seizure-related morbidity and mortality. This review analyses 36 studies of wearable seizure detection systems published between 2017 and 2024, evaluating sensor modalities, signal processing approaches, machine learning classifiers, and clinical validation status. Multimodal wearable systems combining electrodermal activity, accelerometry, and cardiac monitoring achieve sensitivity of 80–95% for convulsive seizure detection with false alarm rates of 0.1–1.0 per hour. Focal and absence seizures remain poorly detected by peripheral sensors. The implications for seizure monitoring and patient safety in Uzbekistan are discussed.
epilepsy; seizure detection; wearable sensors; electrodermal activity; accelerometry; machine learning; SUDEP
NATURAL LANGUAGE PROCESSING IN NEUROLOGICAL CLINICAL DOCUMENTATION: AUTOMATED CODING, INFORMATION EXTRACTION, AND CLINICAL DECISION SUPPORT
Neurological clinical documentation — encompassing admission notes, discharge summaries, neurology consultation reports, and operative records — represents a rich source of clinically relevant information that is largely inaccessible to computational analysis due to its unstructured natural language format. Natural language processing (NLP) — the computational analysis of human language — has advanced dramatically with the development of large language models (LLMs) including BERT, GPT-4, and domain-specific variants, enabling automated extraction of clinically relevant information from neurological text with accuracy approaching human expert performance. This review analyses 38 studies of NLP applied to neurological clinical documentation published between 2018 and 2024, evaluating tasks including automated ICD coding, clinical entity extraction (symptoms, diagnoses, medications), phenotyping of neurological conditions from electronic health records, and clinical decision support. NLP-based automated coding achieves F1 scores of 0.82–0.94 for neurological diagnoses, clinical entity extraction achieves F1 of 0.88–0.96, and phenotyping algorithms based on NLP demonstrate sensitivity of 85–95% for identifying neurological patient cohorts. Implications for neurology documentation efficiency and clinical informatics in Uzbekistan are discussed.
natural language processing; clinical documentation; electronic health records; ICD coding; information extraction; large language models; neurology
TELENEUROLOGY: REMOTE DIAGNOSIS, TELESTROKE NETWORKS, AND DIGITAL CONSULTATION IN RESOURCE-LIMITED SETTINGS
Neurological disorders — including stroke, epilepsy, Parkinson's disease, and headache — impose a disproportionate burden in low- and middle-income countries where access to specialist neurology care is severely constrained by workforce shortages and geographic barriers. Teleneurology — the delivery of neurological diagnostic and management services through video consultation, store-and-forward image transmission, and digital decision support — offers a scalable mechanism for extending specialist neurology care to underserved populations. This review analyses 45 studies of teleneurology programmes published between 2015 and 2024, with particular focus on evidence from low- and middle-income country settings and implications for Uzbekistan. Telestroke networks have demonstrated door-to-needle time reductions of 15–35 minutes and equivalent thrombolysis outcomes to in-person assessment, with implementation in over 50 countries. Tele-epilepsy and tele-Parkinson programmes demonstrate equivalent diagnostic accuracy and treatment quality to in-person consultation for appropriate patient populations. Implementation challenges including digital infrastructure, regulatory frameworks, and clinical acceptance are evaluated.
teleneurology; telestroke; epilepsy; telehealth; remote consultation; resource-limited settings; Uzbekistan
COMPUTATIONAL MODELLING OF NEUROINFLAMMATION IN MULTIPLE SCLEROSIS: MACHINE LEARNING APPROACHES TO DISEASE ACTIVITY PREDICTION AND TREATMENT RESPONSE
Multiple sclerosis (MS) is the most common non-traumatic neurological disability cause in young adults, affecting approximately 2.8 million individuals globally. The relapsing-remitting course characteristic of most patients creates significant prognostic uncertainty and challenges for treatment selection — between 15 currently approved disease-modifying therapies with substantially different efficacy and safety profiles. Machine learning applied to clinical, MRI, and biomarker data has demonstrated significant potential for predicting disease activity, treatment response, and long-term disability accumulation, potentially enabling precision MS medicine. This review analyses 33 studies of machine learning in MS disease activity prediction and treatment response modelling published between 2017 and 2024. Predictive models for 2-year relapse risk achieve AUC of 0.72–0.88, while models predicting response to specific disease-modifying therapies achieve AUC of 0.76–0.91. MRI-based deep learning models for lesion segmentation demonstrate Dice similarity coefficients of 0.82– 0.94, approaching human expert performance. Implications for MS management in Uzbekistan, where diagnosis is frequently delayed and disease-modifying therapy access is limited, are discussed.
multiple sclerosis; machine learning; disease activity prediction; MRI; treatment response; precision neurology; neuroinflammation
AI-DRIVEN DRUG TARGET IDENTIFICATION IN NEURODEGENERATION: APPLICATIONS IN ALZHEIMER'S, PARKINSON'S, AND ALS RESEARCH
Neurodegenerative diseases — including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) — represent the greatest unmet therapeutic need in neurology, with no currently approved disease-modifying therapies for AD or ALS and limited options for PD. The extraordinarily high failure rate of neurodegenerative drug candidates — approximately 99% for AD clinical trials — reflects incomplete understanding of disease mechanisms and inadequate target validation at the preclinical stage. Artificial intelligence, encompassing machine learning network analysis, protein structure prediction, knowledge graph mining, and molecular dynamics simulation, is transforming the drug target identification process for neurodegenerative diseases by enabling the integration of genomic, proteomic, transcriptomic, and clinical data at scales previously impossible. This review analyses 40 studies of AI-driven target identification in neurodegeneration published between 2019 and 2024. Knowledge graph-based target identification approaches have generated candidate targets with network-level validation in AD and PD, several of which have advanced to preclinical or clinical evaluation. AlphaFold2 protein structure predictions have enabled structure-based drug design for neurodegeneration-related proteins previously considered undruggable. Implications for neuropharmacological research capacity development are discussed.
neurodegeneration; Alzheimer's disease; drug target identification; artificial intelligence; knowledge graph; AlphaFold; protein structure; drug discovery
AI-BASED EARLY DETECTION OF ALZHEIMER'S DISEASE USING MULTIMODAL MRI: A SYSTEMATIC REVIEW OF DEEP LEARNING APPROACHES
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder globally, affecting approximately 55 million individuals and projected to triple by 2050. Early and accurate diagnosis remains a critical clinical challenge, as irreversible neurodegeneration precedes symptom onset by 15–20 years. Deep learning applied to multimodal MRI — integrating structural, functional, and diffusion tensor imaging — has demonstrated transformative diagnostic potential, achieving diagnostic accuracy of 85–95% in distinguishing AD from mild cognitive impairment and healthy controls in controlled research settings. This review systematically analyses 47 peer-reviewed studies published between 2018 and 2024, evaluating convolutional neural network architectures, transformer-based models, and graph neural networks for AD detection from MRI data. We identify optimal model architectures, training dataset requirements, and critical implementation challenges including data harmonisation across MRI scanners, class imbalance, and clinical validation gaps. The findings provide a roadmap for translating deep learning MRI diagnostics from research to clinical practice in neurology departments with limited specialist capacity.
Alzheimer's disease; deep learning; MRI; convolutional neural network; early detection; neuroimaging; dementia
DEEP LEARNING FOR AUTOMATED EEG SIGNAL CLASSIFICATION IN EPILEPSY: CURRENT EVIDENCE AND CLINICAL IMPLEMENTATION PERSPECTIVES
Epilepsy affects approximately 50 million people globally, with seizures representing the defining clinical manifestation in approximately 70% of patients. Electroencephalography (EEG) remains the primary diagnostic tool for epilepsy characterisation, but its interpretation requires highly specialised expertise concentrated in tertiary epilepsy centres inaccessible to the majority of affected individuals, particularly in low- and middle-income countries. Deep learning — encompassing convolutional neural networks, recurrent architectures, and transformer models — applied to EEG signal data has achieved seizure detection sensitivity of 90–98% and specificity of 92–99% in controlled research settings, approaching expert electroencephalographer performance. This review analyses 39 studies of deep learning EEG analysis in epilepsy published between 2017 and 2024, evaluating model architectures, input signal representations, performance benchmarks, and clinical deployment challenges. Particular attention is given to implementation barriers in resource-limited healthcare systems, including Uzbekistan, where epilepsy services are predominantly confined to major urban centres.
epilepsy; EEG; deep learning; seizure detection; convolutional neural network; transformer; clinical neurology
DIGITAL BIOMARKERS FOR PARKINSON'S DISEASE MONITORING: WEARABLE SENSORS, SMARTPHONE APPLICATIONS, AND MACHINE LEARNING ANALYTICS
Parkinson's disease (PD) affects approximately 10 million people globally and is characterised by progressive motor symptoms — tremor, rigidity, bradykinesia, and postural instability — alongside a spectrum of non-motor manifestations including cognitive decline, autonomic dysfunction, and sleep disorders. Current clinical assessment relies on periodic in-clinic evaluation using rating scales such as the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), which captures a brief snapshot of motor function and is subject to inter-rater variability and recall bias. Digital biomarkers derived from wearable inertial sensors, smartwatches, smartphones, and voice analysis have emerged as objective, continuous, and ecologically valid measures of PD symptom severity that complement and potentially extend traditional clinical assessment. This review analyses 52 studies of digital biomarker development and validation for PD monitoring published between 2016 and 2024, evaluating sensor modalities,machine learning approaches, and clinical validation status. Implications for PD monitoring in Uzbekistan's neurology services are discussed.
Parkinson's disease; digital biomarkers; wearable sensors; tremor; gait analysis; machine learning; remote monitoring
BRAIN-COMPUTER INTERFACE TECHNOLOGIES IN NEUROREHABILITATION: CURRENT APPLICATIONS AND FUTURE DIRECTIONS
Brain-computer interfaces (BCIs) — systems that establish direct communication pathways between the brain and external devices by recording and decoding neural signals — represent a rapidly maturing technology with transformative potential for neurorehabilitation following stroke, spinal cord injury, and neurodegenerative disease. BCIs enable patients with severe motor impairments to control external devices — robotic orthoses, communication systems, functional electrical stimulation devices — through decoded neural intent, bypassing damaged motor pathways and potentially driving neuroplastic recovery through contingent sensorimotor feedback. This review evaluates 44 studies of BCI applications in neurorehabilitation published between 2015 and 2024, analysing system types, clinical populations, rehabilitation outcomes, and implementation requirements. EEG-based BCIs remain the most clinically accessible technology, with motor imagery-based systems demonstrating improvements in upper limb function of 15–35% on validated outcome measures in stroke rehabilitation randomised controlled trials. Implantable electrocorticographic and Utah array BCIs have achieved milestone demonstrations of restored communication and motor control in paralysed individuals. Implementation barriers including cost, training burden, and signal reliability are evaluated in the context of neurology and rehabilitation services in Uzbekistan.
brain-computer interface; neurorehabilitation; stroke; EEG; motor imagery; neuroprosthetics; neuroplasticity
FACTORS CONTRIBUTING TO THE DEVELOPMENT OF LOCOREGIONAL RECURRENCE
Locoregional recurrence remains one of the leading causes of disease progression following radical treatment for malignant neoplasms. Identification of risk factors associated with recurrence is essential for predicting patient outcomes, optimizing therapeutic strategies, and developing individualized follow-up programs.
locoregional recurrence, risk factors, oncology, prognosis, adjuvant therapy.
ФОТОМЕТРИЧЕСКИЙ АНАЛИЗ В ОЦЕНКЕ СТАБИЛЬНОСТИ КОНСТРУКТИВНОГО ПРИКУСА ПРИ РАННЕМ ОРТОДОНТИЧЕСКОМ ЛЕЧЕНИИ ПАЦИЕНТОВ С ГЛУБОКИМ ПРИКУСОМ
Глубокий прикус является одной из самых распространенных вертикальных аномалий окклюзии, склонной к высокой частоте рецидивов. В исследование включены данные 45 пациентов, проходивших раннее ортодонтическое лечение с использованием функционально-направляющих аппаратов. Авторы анализируют динамику антропометрических индексов лица (высота нижней трети, углы профиля) в процессе фиксации конструктивного прикуса и в ретенционном периоде. Доказано, что фотометрический мониторинг является высокоточным, неинвазивным и объективным методом контроля морфологической стабильности результатов лечения.
глубокий прикус, конструктивный прикус, фотометрический анализ, раннее ортодонтическое лечение, стабильность результатов, сменный прикус.
РАННЕЕ ПРОГНОЗИРОВАНИЕ ВЕСТИБУЛЯРНЫХ И КООРДИНАЦИОННЫХ НАРУШЕНИЙ У ДЕТЕЙ С НАРУШЕНИЕМ СЛУХА И ПУТИ ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ КОМПЛЕКСНОЙ РЕАБИЛИТАЦИИ
Нарушение слуха у детей нередко сопровождается поражением вестибулярного анализатора, что приводит к нарушениям равновесия, координации движений, задержке моторного развития и снижению функциональной активности ребёнка. Вестибулярная дисфункция часто остаётся недооценённой, поскольку клинические проявления могут быть малозаметными или маскироваться основным заболеванием. Несвоевременное выявление данных нарушений отрицательно влияет на развитие двигательных навыков, когнитивных функций и эффективность слухоречевой реабилитации. В связи с этим раннее прогнозирование вестибулярных и координационных нарушений, объективная оценка функционального состояния вестибулярной системы и совершенствование комплексной реабилитации являются актуальными задачами современной детской неврологии, оториноларингологии и медицинской реабилитации.
нарушение слуха, дети, вестибулярная дисфункция, координационные нарушения, равновесие, vHIT, VNG, cVEMP, oVEMP, стабилометрия, комплексная реабилитация, ранняя диагностика, прогнозирование.
WEARABLE HEALTH TECHNOLOGIES AND REMOTE PATIENT MONITORING: CLINICAL EVIDENCE, IMPLEMENTATION CHALLENGES, AND FUTURE DIRECTIONS
This article presents a systematic literature review examining wearable health technologies and remote patient monitoring (RPM) systems, their clinical evidence base, implementation determinants, and future directions. Publications from 2014 to 2024 indexed in PubMed, Scopus, Web of Science, and IEEE Xplore were analysed. The review covers consumer and medical-grade wearable device categories, biosensor technologies enabling continuous physiological monitoring, clinical applications in cardiovascular disease, diabetes, respiratory conditions, and mental health, the evidence base for RPM in chronic disease management, data quality and algorithmic interpretation challenges, privacy and regulatory considerations, and the implications of wearable health technologies for Uzbekistan's healthcare system. The analysis demonstrates that RPM systems produce clinically meaningful improvements in chronic disease management — HbA1c reductions of 0.3–0.8%, blood pressure improvements of 3–7 mmHg systolic, and 30-day readmission reductions of 15–25% in documented programmes — while facing challenges including device accuracy variability, data integration deficits, and equitable access concerns.
wearable technology, remote patient monitoring, IoT, digital health, chronic disease management, biosensors, patient engagement, literature review.
MACHINE LEARNING IN DRUG DISCOVERY AND PHARMACOLOGICAL RESEARCH: APPLICATIONS, ACHIEVEMENTS, AND CHALLENGES
This article presents a systematic literature review examining machine learning (ML) applications across the drug discovery and development pipeline, from target identification and molecular design through preclinical testing, clinical trial optimisation, and pharmacovigilance. Publications from 2015 to 2024 indexed in PubMed, Scopus, Web of Science, and Nature journals were analysed. The review addresses the theoretical foundations of ML in computational pharmacology, principal application domains, documented achievements including AI-designed molecules entering clinical trials, challenges including data quality limitations and model interpretability, and the implications for pharmaceutical research capacity development. The analysis demonstrates that ML applications in drug discovery have produced measurable reductions in development timelines — particularly in virtual screening (reducing compound screening time by 90–99%) and toxicity prediction (improving prediction accuracy to 70–85%) — while enabling exploration of chemical space at scales impossible through traditional experimental approaches. The first AI-discovered drug candidates entering clinical trials represent landmark achievements with transformative implications for pharmaceutical research.
machine learning, drug discovery, pharmacology, deep learning, molecular design, clinical trials, biomedical informatics, literature review.
ELECTRONIC HEALTH RECORDS AND CLINICAL DECISION SUPPORT SYSTEMS: IMPACT ON QUALITY OF CARE AND PATIENT SAFETY
This article presents a systematic literature review examining the impact of electronic health records (EHRs) and clinical decision support systems (CDSS) on quality of care and patient safety. Publications from 2010 to 2024 indexed in PubMed, Scopus, Web of Science, and the Cochrane Library were analysed. The review covers EHR adoption patterns and implementation challenges, clinical decision support functions and their evidence base, medication safety applications, preventive care improvement, and the challenges of interoperability and clinical workflow integration. The analysis demonstrates that well-implemented EHR systems with integrated CDSS reduce preventable adverse drug events by 40–80%, improve adherence to clinical guidelines by 15–25%, and decrease duplicate test ordering by 10–20%, generating measurable quality and safety improvements. However, the realisation of these benefits depends critically on system usability, clinical workflow integration, and clinician training quality. The implications for health information system development in Uzbekistan are examined.
electronic health records, clinical decision support, patient safety, healthcare quality, health informatics, interoperability, Uzbekistan, literature review.
TELEMEDICINE AND DIGITAL HEALTH PLATFORMS: EVIDENCE FROM LOW- AND MIDDLE-INCOME COUNTRIES AND IMPLICATIONS FOR CENTRAL ASIA
This article presents a systematic literature review of telemedicine and digital health platform evidence from low- and middle-income countries (LMICs), examining effectiveness, implementation determinants, and economic implications relevant to Central Asian healthcare systems. Publications from 2012 to 2024 indexed in PubMed, Scopus, WHO Global Health Observatory, and Cochrane Library were analysed. The review covers synchronous and asynchronous telemedicine modalities, mobile health (mHealth) applications, teleconsultation platforms, remote patient monitoring, and digital health ecosystem development. The analysis demonstrates that telemedicine interventions in LMICs produce clinically meaningful improvements in healthcare access — reducing travel distances for specialist consultation by 60–80% in documented programmes — alongside modest but consistent improvements in chronic disease management outcomes including HbA1c reduction, blood pressure control, and medication adherence. Implementation is critically dependent on mobile internet infrastructure, digital literacy, and healthcare worker training. The review identifies telemedicine's substantial potential for addressing specialist access inequalities in Uzbekistan and outlines a framework for evidence-based digital health platform development.
telemedicine, digital health, mHealth, remote consultation, low- and middle-income countries, healthcare access, Uzbekistan, literature review.
ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSIS: CURRENT APPLICATIONS, CLINICAL OUTCOMES, AND FUTURE PERSPECTIVES
This article presents a systematic literature review examining the current applications of artificial intelligence (AI) in medical diagnosis, their documented clinical outcomes, and perspectives for implementation in low- and middle-income country healthcare systems. Publications from 2015 to 2024 indexed in PubMed, Scopus, Web of Science, and Cochrane Library were systematically analysed. The review covers AI diagnostic applications across imaging-based specialties including radiology, pathology, and ophthalmology, as well as clinical decision support in internal medicine, and addresses methodological quality, safety considerations, and implementation challenges. The analysis demonstrates that AI diagnostic systems achieve performance comparable or superior to specialist clinicians in specific, well-defined diagnostic tasks — including diabetic retinopathy screening (AUC 0.99), pneumonia detection on chest radiographs (AUC 0.93), and melanoma identification (AUC 0.91) — while facing significant barriers to real-world clinical deployment including training data quality, algorithmic bias, and regulatory uncertainty. Implications for AI diagnostic implementation in Uzbekistan's healthcare system are identified.
artificial intelligence, medical diagnosis, deep learning, clinical decision support, radiology, pathology, Uzbekistan, literature review.
CHANGES IN THE OXIDATIVE AND ANTIOXIDANT SYSTEMS OF THE KIDNEYS IN METABOLIC SYNDROME
Metabolic syndrome (MetS) is one of the major challenges in modern medicine. Abdominal obesity, hyperglycemia, dyslipidemia, and arterial hypertension are key factors contributing to the development of kidney injury [1]. Experimental studies have demonstrated significant structural and functional alterations in renal cell membranes during the development of glomerular injury associated with enhanced lipid peroxidation (LPO) [2]. An experimental study by T.L. Pallone [3] showed that oxidative stress may be induced independently or simultaneously in both the renal cortex and medulla. Moreover, the superoxide anion initiates oxidative stress throughout all regions of the kidney, whereas hydrogen peroxide predominantly affects the renal medulla.
Abdominal obesity, hyperglycemia, dyslipidemia, and arterial hypertension
ARTIFICIAL INTELLIGENCE FOR MORTALITY RISK PREDICTION IN PEDIATRIC INTENSIVE CARE UNITS: DEVELOPMENT, VALIDATION, AND CLINICAL IMPACT ANALYSIS
Background: Accurate mortality risk prediction in pediatric intensive care units (PICUs) supports clinical decision-making, family communication, and resource allocation. Objective: To develop and externally validate an AI-based mortality prediction model for critically ill children and assess clinical utility compared to established pediatric mortality scores. Methods: A multicenter study across 12 PICUs included 18,437 admissions over three years. XGBoost ensemble models were trained on the first 24 hours of physiological, laboratory, and clinical data. Results: The AI model achieved AUC-ROC of 0.93 (95% CI: 0.91–0.95) in external validation, significantly outperforming PRISM-IV (AUC-ROC 0.84) and PIM-3 (AUC-ROC 0.83). Calibration was excellent (Brier Score 0.06). Clinician AI-assisted decisions showed 18.3% improvement in mortality prediction accuracy. Conclusion: AI-based mortality prediction substantially outperforms established pediatric critical care scoring systems and improves clinical prognostic accuracy when used as a decision support tool.
pediatric intensive care, mortality prediction, machine learning, XGBoost, PRISM-IV, PIM-3, clinical decision support, critical care
DEEP LEARNING APPLICATIONS IN PEDIATRIC ONCOLOGY: TUMOR DETECTION, CLASSIFICATION, AND TREATMENT RESPONSE ASSESSMENT
Background: Pediatric cancers differ substantially from adult malignancies in biology, histology, and treatment sensitivity, necessitating specialized AI models. Objective: To evaluate deep learning model performance for pediatric tumor detection, histological classification, and treatment response prediction across major pediatric cancer types. Methods: A multicenter retrospective study analyzed imaging and molecular data from 4,312 pediatric oncology patients across seven children's cancer centers. Convolutional neural networks and graph neural networks were developed for tumor segmentation, WHO classification, and survival prediction. Results: Tumor segmentation achieved mean Dice similarity coefficient of 0.87 across brain tumors, Wilms tumors, and neuroblastoma. Histological classification of brain tumors matched WHO 2024 molecular classification in 89.3% of cases. Survival prediction models achieved C-index of 0.78 at 5-year follow-up. Conclusion: Deep learning demonstrates clinically meaningful performance in pediatric oncology applications, with particular strength in tumor segmentation and molecular classification prediction from histological imaging.
pediatric oncology, deep learning, convolutional neural networks, tumor segmentation, WHO classification, treatment response, computational pathology