Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.

Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Prediction of Dyspnea after 12 Months of an Acute Episode of COVID-19.

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice.

First evidence of tick-borne encephalitis (TBE) outside of Hokkaido Island in Japan.

Tick-borne encephalitis (TBE) is an infection of the central nervous system caused by the tick-borne encephalitis virus (TBEV). TBE is endemic in parts of Europe and Asia. TBEV is transmitted to humans primarily by Ixodes ticks. There have been 5 TBE cases identified in Japan, all on the northern island of Hokkaido. Rodents with TBEV antibodies and Ixodes ticks have been identified throughout Japan, indicating that TBEV infection might be undiagnosed in Japan. Residual serum and cerebrospinal fluid (CSF) collected in 2010-2021 from 520 patients ≥1 year-of-age previously hospitalized with encephalitis or meningitis of unknown etiology at 15 hospitals (including 13 hospitals outside of Hokkaido) were screened by ELISA for TBEV IgG and IgM antibodies; TBEV infection was confirmed by the gold standard neutralization test. Residual serum was available from 331 (63.6%) patients and CSF from 430 (82.6%) patients; both serum and CSF were available from 189 (36.3%). Two patients were TBE cases: a female aged 61 years hospitalized for 104 days in Oita (2000 km south of Hokkaido) and a male aged 24 years hospitalized for 11 days in Tokyo (1200 km south of Hokkaido). Retrospective testing also identified a previous TBEV infection in a female aged 45 years hospitalized for 12 days in Okayama (1700 km south of Hokkaido). TBEV infection should be considered as a potential cause of encephalitis or meningitis in Japan. TBE cases are likely undiagnosed in Japan, including outside of Hokkaido, due to limited clinical awareness and lack of availability of TBE diagnostic tests.

The 3Rs in Experimental Liver Disease.

Patients with cirrhosis present multiple physiological and immunological alterations that play a very important role in the development of clinically relevant secondary complications to the disease. Experimentation in animal models is essential to understand the pathogenesis of human diseases and, considering the high prevalence of liver disease worldwide, to understand the pathophysiology of disease progression and the molecular pathways involved, due to the complexity of the liver as an organ and its relationship with the rest of the organism. However, today there is a growing awareness about the sensitivity and suffering of animals, causing opposition to animal research among a minority in society and some scientists, but also about the attention to the welfare of laboratory animals since this has been built into regulations in most nations that conduct animal research. In 1959, Russell and Burch published the book "The Principles of Humane Experimental Technique", proposing that in those experiments where animals were necessary, everything possible should be done to try to replace them with non-sentient alternatives, to reduce to a minimum their number, and to refine experiments that are essential so that they caused the least amount of pain and distress. In this review, a comprehensive summary of the most widely used techniques to replace, reduce, and refine in experimental liver research is offered, to assess the advantages and weaknesses of available experimental liver disease models for researchers who are planning to perform animal studies in the near future.

First record of Triatoma longipennis, Usinger, 1939 (Hemiptera: Reduviidae: Triatominae) in Tecozautla, Hidalgo.

BACKGROUND: We report the presence of Triatoma longipennis for the first time in two localities in Hidalgo, Mexico. METHODS: This study was conducted at Tecozautla municipality, Hidalgo. Collection was performed in April 2022. RESULTS: We collected eight triatomines from Guadalupe: two fourth-instar nymphs, three fifth-instar nymphs, one female, and two males. In San Miguel Caltepantla, a female was collected inside a dwelling. One sample tested positive for Trypanosoma cruzi. CONCLUSIONS: These findings suggest the need to investigate the dynamics of this species with respect to the inhabitants of the study area.

Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient's health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients' condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA.

Triatoma pallidipennis (Stål, 1872) (Hemiptera: Reduviidae) and its potential for infestation in Tecozautla, Hidalgo state, Mexico.

Triatoma pallidipennis is an exclusive and widely distributed species in Mexico and one of the three main vectors that transmit Chagas disease in the country. The state of Hidalgo is an endemic area for Chagas disease where the presence of several species of triatomines has been reported. The objective of our work was to describe the morphology, colonization process, and reproductive behavior of T. pallidipennis in Guadalupe, Tecozautla, two years after the first collection of a specimen in this region. A total of 28 specimens was collected at both domicile and peridomicile, showing a 17.8% infection rate. The main collection site was a woodshed, and despite the collection of adults in the dwelling, we did not find eggs, exuviae, or nymphs. One female monitored from collection day until death laid 566 eggs, with a hatching rate of 95%, showing an increase of oviposition when cohabited with a male. The results showed the capacity that T. pallidipennis has to infest areas (mainly human dwellings) when it settles down, which would imply a risk for the population that lives in the locality.

Fungal infections in pediatric patients with acute myeloid leukemia in a tertiary hospital.

Introduction: Acute leukemia accounts for more than 30% of all pediatric cancer cases, and of these, 15-20% are acute myeloid leukemia (AML). Children who super from AML are more likely to develop infections due to the humoral and cellular immune deficits generated by the disease and its treatment. The incidence of fungal infections is underestimated; reports show that up to 75% of fungal infections go undiagnosed until autopsy. In only 30 years, the incidence of invasive candidiasis has increased by 40-fold. Thus, the high morbidity and mortality associated with fungal infections in hematological patients make it necessary to adopt preventive measures. Methods: This work aimed to retrospectively identify pediatric patients with acute myeloid leukemia and invasive fungal diseases (IFDs) in a Latin American tertiary care hospital. A retrospective analysis of 36 clinical records of pediatric patients diagnosed with AML from 2007 to 2017 was carried out. Results: One hundred and twenty-nine hospitalizations were associated with infectious events. Thirteen patients in our study presented 15 infectious events associated with IFDs (11.6%). Two patients died because of complications related to IFDs (15.3%). The most frequent IFD type was aspergillosis, which was observed in 7 cases, followed by Candidemia, which was observed in 4 cases. The most frequent clinical manifestations were fever and respiratory distress. Discussion: Mortality due to IFD can be prevented with effective pharmacotherapy. An appropriate antifungal prophylaxis strategy still needs to be developed through larger prospective studies in Latin America.

Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Screening and Early Diagnosis of Breast Cancer.

Breast cancer is the most frequently diagnosed tumor pathology on a global scale, being the leading cause of mortality in women. In light of this problem, screening programs have been implemented on the population at risk in the form of mammograms, starting in the 20th century. This has considerably reduced the associated deaths, as well as improved the prognosis of the patients who suffer from this disease. In spite of this, the evaluation of mammograms is not without certain variability and depends, to a large extent, on the experience and training of the medical team carrying out the assessment. With the aim of supporting the evaluation process of mammogram images and improving the diagnosis process, this work presents the design, development and proof of concept of a novel intelligent clinical decision support system, grounded on two predictive approaches that work concurrently. The first of them applies a series of expert systems based on fuzzy inferential engines, geared towards the treatment of the characteristics associated with the main findings present in mammograms. This allows the determination of a series of risk indicators, the Symbolic Risks, related to the risk of developing breast cancer according to the different findings. The second one implements a classification machine learning algorithm, which using data related to mammography findings as well as general patient information determines another metric, the Statistical Risk, also linked to the risk of developing breast cancer. These risk indicators are then combined, resulting in a new indicator, the Global Risk. This could then be corrected using a weighting factor according to the BI-RADS category, allocated to each patient by the medical team in charge. Thus, the Corrected Global Risk is obtained, which after interpretation can be used to establish the patient's status as well as generate personalized recommendations. The proof of concept and software implementation of the system were carried out using a data set with 130 patients from a database from the School of Medicine and Public Health of the University of Wisconsin-Madison. The results obtained were encouraging, highlighting the potential use of the application, albeit pending intensive clinical validation in real environments. Moreover, its possible integration in hospital computer systems is expected to improve diagnostic processes as well as patient prognosis.

Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile.

Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient's health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8-0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time.

Prevalence, comorbidities, and treatment patterns of Japanese patients with alopecia areata: A descriptive study using Japan medical data center claims database.

Real-world data on alopecia areata (AA) demographics, comorbidities, and treatment patterns are sparse, not only in Japan but worldwide. This cross-sectional study assessed the current prevalence of AA in Japan, including analysis of severe subsets, frequency of comorbidities, and unmet medical needs surrounding treatment. Patients registered in the Japan Medical Data Center claims database (January 2012 to December 2019) and diagnosed with AA were included. Prevalence was calculated yearly, with the most common comorbidities evaluated, and treatments described in the Japanese Dermatological Association AA management guidelines and approved in Japan were included in the analysis. In total, 61 899 patients were diagnosed with AA. Among them, 1497 were diagnosed with severe subtypes. AA prevalence in Japan has been gradually increasing (from 0.16% in 2012 to 0.27% in 2019). The most common comorbidities are allergic rhinitis, atopic dermatitis, and asthma. Depression and anxiety are frequent in these patients, as are autoimmune diseases, e.g., vitiligo, thyroid diseases, and rheumatoid arthritis. Intriguingly, the analysis found Down syndrome to be a comorbidity associated with severe AA in children. The principal treatments were topical corticosteroids, followed by carpronium chloride and cepharanthine. The use of systemic corticosteroids and antihistamines is increased in severe disease. The Japanese Dermatological Association guidelines do not support the use of oral corticosteroids in children; however, in the database, this has been prescribed in up to 2.5% and 9.8% of all pediatric and severe pediatric AA cases, respectively. Despite the limitations of using a claims database, the current study demonstrates that AA prevalence in Japan has gradually increased in recent years, with allergic diseases being the most common comorbidities. The data also imply that there is a need for effective and safe therapies, especially for severe and pediatric cases.

First record of Triatoma longipennis, Usinger, 1939 (Hemiptera: Reduviidae: Triatominae) in Tecozautla, Hidalgo

ABSTRACT Background: We report the presence of Triatoma longipennis for the first time in two localities in Hidalgo, Mexico. Methods: This study was conducted at Tecozautla municipality, Hidalgo. Collection was performed in April 2022. Results: We collected eight triatomines from Guadalupe: two fourth-instar nymphs, three fifth-instar nymphs, one female, and two males. In San Miguel Caltepantla, a female was collected inside a dwelling. One sample tested positive for Trypanosoma cruzi. Conclusions: These findings suggest the need to investigate the dynamics of this species with respect to the inhabitants of the study area.

Costs associated with adverse events from remission induction for children with Acute Lymphoblastic Leukemia (ALL).

BACKGROUND: ALL is the most frequent hematological tumor in children, so during remission induction chemotherapy protocol (RICP) adverse events (AEs) may appear. The public program in Mexico in charge of financial support to oncologic children without social security delivered a fix amount for ALL chemotherapy, but additional money needed to treat any other unexpected condition should be taken from the budget of the oncologic healthcare providers. So the purpose of our study was to estimate and evaluate the direct medical costs associated to EAs during RICP in children with ALL. METHODS: This study was retrospective, longitudinal, and observational based on medical records review of patients in RICP. The CTCAE was used to identify and classify AEs according to a SOC category. We focused on extracting resources data that were consumed both for inpatients and outpatients AEs. A micro-costing approach was adopted which involve quantification of each healthcare resource consumed by the hospital multiplying them by unit cost. The probability distributions of data were evaluated to identify the appropriated statistical tests to be used for comparisons between groups that were performed with Wilcoxon rank sum test. Generalized linear models (GLM) were adjusted to evaluate the effects of patient characteristics on total cost. RESULTS: Forty patients accumulated 204 inpatient and 81 outpatient AEs during RICP. Comparison of total costs between groups showed an incremental cost of $7,460.23 likewise attributable to AEs. The total cost of a pediatric patient undergoing RICP without adverse events was $3,078.36 and the total cost of a patient with AEs exceeds it threefold. CONCLUSIONS: The costs associated with AEs during RICP in Mexican children with ALL representing a high burden for the healthcare provider. Generalized linear models showed that variables such as sex, risk category and alive status are associated with the total costs of AEs. This is the first study aiming to analyze the effect of ALL-related AEs on health care costs in pediatric population, so our results may help not only to local decision making but also it may contribute to the research agenda in this field.

Occupational Therapy Interventions for Dementia Caregivers: Scoping Review.

BACKGROUND AND PURPOSE: caregivers of people with dementia (PwD) often experience a significant caregiver burden. Occupational Therapy (OT) is a helpful discipline for improving quality of life and other health factors for these caregivers. We conducted a Scoping Review to describe OT interventions for caregivers of PwD. METHODS: two authors searched PubMed, Scopus, EMBASE and Web of Science databases and OT journals indexed in the Journal Citation Reports. Terms included in the search strategy were: dementia, Alzheimer, Parkinson, caregivers and OT. We included articles with experimental design in which an OT intervention in caregivers of PwD was carried out, written in Spanish or English and with the full text available. RESULTS: a total of 2121 articles were obtained, 31 of which were included; 22 of them described home-based OT interventions: Tailored Activity Program (TAP) (n = 5), Environmental Skill-Building Program (ESP) (n = 4) and Advancing Caregiver Training (ACT) (n = 3) and other household interventions (n = 10); the remaining studies described OT interventions in other settings (n = 9). CONCLUSIONS: OT interventions for caregivers of PwD were mainly carried out at home. The most commonly used interventions were TAP focused on caregivers of people with Alzheimer's disease, aimed at lessening the burden, depression and stress experienced by caregivers.

Genetic diversity in peridomiciliary populations of Triatoma mexicana (Hemiptera: Reduviidae: Triatominae) in central Mexico.

Triatoma mexicana is an important vector of Trypanosoma cruzi-the etiological agent of Chagas disease. This triatomine species occurs in central Mexico, but little is known about its genetic variability. Using Cyt-b gene as a genetic marker, in this study, we determined the population genetic structure of T. mexicana collected from the States of Hidalgo, Guanajuato, and Queretaro where populations are largely peridomiciliary. A Bayesian approach was performed for the design of phylogenies, median-joining networks, and clustering among populations of T. mexicana. Our results show that the Hidalgo population was the most distinct, with the highest genetic and haplotypic variation (Hd = 0.963, π = 0.06129, and ɵ = 0.05469). Moderate gene flow (Nm) was determined among populations of Hidalgo and Queretaro. Populations from the three states showed differentiation (FST) values ranging from 0.22 to 0.3, suggesting an important genetic differentiation. The phylogenetic analysis showed the presence of five well-defined groups, as well as the haplotype network, where 24 haplotypes were observed forming five haplogroups with high mutational steps among them: 68 (Hgo-W2), 26 (Qto), 59 (Hgo-M), 44 (Hgo-W1), and 46 (Gto). Genetic isolation was apparently inferred in the Guanajuato population; however, the Mantel test did not show correlation between genetic (FST) and geographic (km) distances (p = 0.05). The STRUCTURE analyses showed seven genetic clusters and it was observed that a single cluster predominates in each sampled location. However, genetic admixture was detected in four localities. Our results show evidence that there are multiple species within the collected sampling area.

Design and Definition of a New Decision Support System Aimed to the Hierarchization of Patients Candidate to Be Admitted to Intensive Care Units.

The triage processes prior to the assignation of healthcare resources in hospitals are some of the decision-making processes that more severely affect patients. This effect gets even worse in health emergency situations and intensive care units (ICUs). Aiming to facilitate the decision-making process, in this work the use of vague fuzzy numbers is proposed, aiming to define a multi-attribute patient hierarchization method to be used in emergency situations at hospital ICUs. The incorporation of fuzzy models allows for modelling the vagueness and uncertainty associated with decision criteria evaluation, with which more efficient support is provided to the decision-making process. After defining the methodology, the effectiveness of this new system for patient hierarchization is shown in a case study. As a consequence of that, it is proved that the integration of decision-support systems into healthcare environments results to be efficient and productive, suggesting that if a part of the decision process is supported by these systems, then the errors associated with wrong interpretations and/or diagnoses might be reduced.

Design and Development of an Intelligent Clinical Decision Support System Applied to the Evaluation of Breast Cancer Risk.

Breast cancer is currently one of the main causes of death and tumoral diseases in women. Even if early diagnosis processes have evolved in the last years thanks to the popularization of mammogram tests, nowadays, it is still a challenge to have available reliable diagnosis systems that are exempt of variability in their interpretation. To this end, in this work, the design and development of an intelligent clinical decision support system to be used in the preventive diagnosis of breast cancer is presented, aiming both to improve the accuracy in the evaluation and to reduce its uncertainty. Through the integration of expert systems (based on Mamdani-type fuzzy-logic inference engines) deployed in cascade, exploratory factorial analysis, data augmentation approaches, and classification algorithms such as k-neighbors and bagged trees, the system is able to learn and to interpret the patient's medical-healthcare data, generating an alert level associated to the danger she has of suffering from cancer. For the system's initial performance tests, a software implementation of it has been built that was used in the diagnosis of a series of patients contained into a 130-cases database provided by the School of Medicine and Public Health of the University of Wisconsin-Madison, which has been also used to create the knowledge base. The obtained results, characterized as areas under the ROC curves of 0.95-0.97 and high success rates, highlight the huge diagnosis and preventive potential of the developed system, and they allow forecasting, even when a detailed and contrasted validation is still pending, its relevance and applicability within the clinical field.