Efficiency Of Disease Management Research Articles

LT-DeepLab: an improved DeepLabV3+ cross-scale segmentation algorithm for Zanthoxylum bungeanum Maxim leaf-trunk diseases in real-world environments.

Zanthoxylum bungeanum Maxim is an economically significant crop in Asia, but large-scale cultivation is often threatened by frequent diseases, leading to significant yield declines. Deep learning-based methods for crop disease recognition have emerged as a vital research area in agriculture. This paper presents a novel model, LT-DeepLab, for the semantic segmentation of leaf spot (folium macula), rust, frost damage (gelu damnum), and diseased leaves and trunks in complex field environments. The proposed model enhances DeepLabV3+ with an innovative Fission Depth Separable with CRCC Atrous Spatial Pyramid Pooling module, which reduces the structural parameters of Atrous Spatial Pyramid Pooling module and improves cross-scale extraction capability. Incorporating Criss-Cross Attention with the Convolutional Block Attention Module provides a complementary boost to channel feature extraction. Additionally, deformable convolution enhances low-dimensional features, and a Fully Convolutional Network auxiliary header is integrated to optimize the network and enhance model accuracy without increasing parameter count. LT-DeepLab improves the mean Intersection over Union (mIoU) by 3.59%, the mean Pixel Accuracy (mPA) by 2.16%, and the Overall Accuracy (OA) by 0.94% compared to the baseline DeepLabV3+. It also reduces computational demands by 11.11% and decreases the parameter count by 16.82%. These results indicate that LT-DeepLab demonstrates excellent disease segmentation capabilities in complex field environments while maintaining high computational efficiency, offering a promising solution for improving crop disease management efficiency.

E-Goat Doctor: An Expert System for the Diagnosis of Common Goat Diseases

The E-Goat Doctor is an expert system designed to diagnose common goat diseases, particularly in rural areas where access to veterinary services is limited. Utilizing a rule-based algorithm, the system analyzes symptoms provided by users and matches them with known diseases, offering accurate and timely diagnoses. The development of the E-Goat Doctor involved series of consultations with domain experts, ensuring a comprehensive knowledge base that addresses prevalent goat health issues in Northern Mindanao, Philippines, such as mastitis, foot rot, pneumonia, worm infestations, Enterotoxemia, and Calcium Deficiency. During testing, the system demonstrated high accuracy in diagnosing diseases, making it a valuable tool for goat farmers. The E-Goat Doctor not only improves the efficiency of disease management but also contributes to the overall health and productivity of goat herds. This expert system is positioned to be a significant asset in supporting sustainable goat farming practices, especially in regions with limited veterinary support.

Molecular Detection and Genotyping of Dengue Viruses: Current Techniques and Future Prospect

The Dengue virus is a prevalent mosquito-borne disease, and its global incidence has been steadily increasing due to the favorable environmental conditions that promote mosquito breeding, primarily influenced by rising temperatures. Bangladesh has been particularly hard-hit by an intense and ongoing outbreak, resulting in a surge of cases and fatalities. Effective management of this disease necessitates the implementation of robust public health measures, including rigorous surveillance and early diagnosis. While serological tests are commonly employed in clinical diagnosis, molecular methods hold a critical role in identifying the specific Dengue virus strain, thereby contributing to a more comprehensive understanding of disease severity. This article serves as an extensive review, delving into various molecular testing techniques employed for both surveillance and clinical diagnosis. It offers valuable insights for research and clinical laboratories engaged in the detection of Dengue virus RNA in mosquitoes, environmental samples, and clinical specimens. The methods covered encompass a spectrum of approaches, including conventional PCR, isothermal amplification, real-time RT-PCR, Sanger sequencing, and whole-genome sequencing, providing a holistic overview of the available techniques. These methods play pivotal roles in clinical diagnosis, outbreak analysis, vector surveillance, and vaccine development. Furthermore, the article underscores the importance of integrating these techniques into existing healthcare systems, emphasizing their significance in ensuring precise dengue diagnostics to enhance the efficiency of disease management. These molecular methods are indispensable tools that contribute to accurate diagnosis, enable effective outbreak investigation, facilitate vector surveillance, and support vaccine preparation, thereby enhancing the overall management and control of dengue, ultimately working toward mitigating its impact on public health.
 Bangladesh J Microbiol, Volume 40, Number 1, June 2023, pp 41-49

Quantitative assessment of essential tremor based on machine learning methods using wearable device

BackgroundEssential tremor (ET) is a progressive neurological disorder with characteristic motor symptoms. Current clinical assessments are primarily based on expert consultation combined with reviewing patient complaints and physician expertise and diagnostic experience. Research on objective quantification through wearable sensor technology combined with machine learning methods has excellent potential for application. This study automatically rates the severity of symptoms in ET patients using wearable sensors in a standardized scenario. MethodsThis study relied on a rigorous clinical trial paradigm and a wearable device based on a nine-axis Inertial Measurement Unit (IMU) to collect a large amount of kinematic data from ET patients and obtain professional physician-supported scale scores. In this paper, hand tremor signals were comprehensively analyzed, and multiple kinematic features in the time and frequency domains were extracted. These features were used to explore different machine learning approaches to automatically and quantitatively assess the disease severity in ET patients. ResultsThe optimized algorithm AdaBoost has a multi-classification F1 score of up to 97.33%, with 99.64% accuracy and 99.39% specificity, respectively. The model still has a better AUC for predicting a few classes and has the current optimal automatic ET symptom recognition performance. DiscussionThese results show that the proposed method is suitable for applying standardized laboratory tests to help clinicians automate the scoring of complex or early ET cases to aid decision-making and improve disease management efficiency.

Glycemic control as an important tool in the prevention of cardiovascular aging in patients with diabetes mellitus

In recent decades, there has been a significant increase in the incidence and prevalence of diabetes mellitus. Diabetes mellitus is characterized by the development of vascular complications leading to early disability and a decrease in the life expectancy of patients. Aging of the body inevitably leads to the occurrence of age-related diseases, including the cardiovascular system, and increases the risk of death. Metabolic and structural and functional disorders of the cardiovascular system arising in diabetes mellitus have common pathophysiological mechanisms with aging of the body. Chronic hyperglycemia can accelerate the aging process and play a decisive role in the occurrence and prognosis of cardiovascular events in patients with diabetes mellitus. Achieving target glycemic values is an important step towards preventing vascular complications in patients with diabetes mellitus. Improved models of glucometers, equipped with a number of additional functions, allow for structured self-control of glycemia, analyze the data obtained and carry out timely correction of therapy, actively involve patients in the process of diabetes management, which will significantly increase the efficiency of disease management, reduce the risk of complications in patients and improve the quality of life.

Penerapan Algoritma Random Forest dengan Kombinasi Ekstraksi Fitur Untuk Klasifikasi Penyakit Daun Tomat

The tomato plant is widely consumed by the community and is widely cultivated by farmers. Tomato plants are susceptible to disease attacks. Plant diseases cause a decrease in the quality and quantity of crops or agricultural produce. The idea of the 4.0 agricultural revolution emerged as a result of the 4.0 industrial revolution. Farmers are not ready to face increasingly rapid technological advances. It is important to identify the disease in tomato leaves correctly in the efficiency of disease management for efforts to control so that disease in tomato leaves does not develop. The main objective of the proposed method is to develop a technique for identifying foliar diseases in tomato plants by increasing the classification accuracy. The novelty of this research is a combination of several feature extractions to improve classification accuracy. The features used are the color feature, the Hu-Moment feature, and the firur haralick. In the classification process, the Random Forest algorithm and other classification algorithms are applied for comparison. In this study, the Random Forest method and the combination of extraction features have shown an increase in accuracy, the accuracy obtained is 96%.

Artificial Intelligence in Healthcare: A Review of Diagnostic Applications and Impact on Clinical Practice

Background: Early detection of health issues is essential for effective treatment, necessitating advanced hospital service quality subsystems, ideally through an integrated Hospital Management Information System (HMIS). Artificial Intelligence (AI) has become a crucial element in clinical medicine, particularly in diagnostics. AI technologies, especially in medical imaging, are poised to revolutionize diagnostic and predictive analysis in healthcare. Methods: This study examines the application of AI in enhancing diagnostic accuracy and predictive capabilities in healthcare. It draws on evidence from pathology and dermatology studies, as well as research on AI's utility in mental health risk assessment and medical imaging. Results: AI has demonstrated superior performance compared to human experts in accurately detecting and classifying various cancers, such as those identified through pathology and dermatology studies. AI's capabilities extend beyond cancer detection to include the differentiation between benign and malignant conditions, thus aiding in more precise disease diagnosis. Additionally, AI shows promise in predicting mental health risks, including mental illnesses and suicide risks among vulnerable populations, such as psychiatric patients, prisoners, and soldiers. These applications highlight AI's potential to provide timely and precise disease information, leading to quicker and more accurate diagnoses. Conclusion: AI integration into healthcare significantly enhances diagnostic accuracy and optimizes disease management efficiency, improving patient outcomes and streamlining healthcare delivery. The technology's potential in medical imaging and mental health assessment underscores its value in providing early and accurate diagnoses, thereby supporting effective treatment and patient care. Continued research and development in AI applications are crucial to further realize its benefits in clinical settings.

Interstate disparities in the performances in combatting COVID-19 in India: efficiency estimates across states

BackgroundCurrently, the novel coronavirus or COVID-19 pandemic poses the greatest global health threat worldwide, and India is no exception. As an overpopulated developing country, it is very difficult to maintain social distancing to restrict the spread of the disease in India. Under these circumstances, it is necessary to examine India’s interstate performances to combat COVID-19.This study aims to explore twin objectives: to investigate the comparative efficiency of Indian states to combat COVID-19 and to unfold the factors responsible for interstate disparities in the efficiency in combatting COVID-19.MethodsThe stochastic production frontier model was utilized for data analysis. The empirical analysis was facilitated by the inefficiency effects model, revealing the factors that influence interstate variability in disease management efficiency. Three types of variables, namely, output, inputs, and exogenous, were used to measure health system efficiency. The relevant variables were compiled from secondary sources. The recovery rate from COVID-19 was the output variable and health infrastructures were considered as the input variable. On the contrary, the non-health determinants considered to have a strong influence on the efficiency of states’ disease management, but could not be considered as input variables, were recognised as exogenous variables. These exogenous variables were specifically used for the inefficiency analysis.ResultsThe empirical results demonstrated the existence of disparities across Indian states in the level of efficiency in combatting COVID-19. A non-trivial outcome of this study was that Tamil Nadu was the best performer and Manipur was the worst performer of the investigated states. Variables such as elderly people, sex ratio, literacy rate, population density, influenced the efficiency of states, and thus, affected the recovery rate.ConclusionThis study argues for the efficient utilisation of the existing health infrastructures in India. Simultaneously, the study suggests improving the health infrastructure to achieve a long-run benefit.

Fabrication of pH-Sensitive Tetramycin Releasing Gel and Its Antibacterial Bioactivity against Ralstonia solanacearum.

Ralstonia solanacearum (R. solanacearum)-induced bacterial wilt of the nightshade family causes a great loss in agricultural production annually. Although there has been some efficient pesticides against R. solanacearum, inaccurate pesticide releasing according to the onset time of bacterial wilt during the use of pesticides still hinders the disease management efficiency. Herein, on the basis of the soil pH change during R. solanacearum growth, and pH sensitivity of the Schiff base structure, a pH-sensitive oxidized alginate-based double-crosslinked gel was fabricated as a pesticide carrier. The gel was prepared by crosslinking oxidized sodium alginate (OSA) via adipic dihydrazide (ADH) and Ca2+. After loading tetramycin into the gel, it showed a pH-dependent pesticide releasing behavior and anti-bacterial activity against R. solanacearum. Further study also showed that the inhibition rate of the tetramycin-loaded gel was higher than that of industrial pesticide difenoconazole. This work aimed to reduce the difficulty of pesticide administration in the high incidence period of bacterial wilt and we believe it has a great application potential in nightshade production.

The application of unmanned aircraft systems to plant protection in China

Agricultural use of unmanned aircraft systems (UAS) in China has risen recently with the increased demand for precision agriculture. The focus of UAS use in China is on improving the efficiency of crop production and reducing environmental harm from runoff related to current crop management activities. This use will keep increasing with the support of new policies on land transfer and appropriately scaled operations for farming in the near future. A major agricultural application of UAS in China is plant protection, toward increasing pest and disease management efficiency. The advantages of UAS are apparent in the country, which has the largest number of small-sized farms in the world. UAS in China have advantages for farms in mountainous regions and orchard production, where it is difficult for ground-based machines to manoeuvre. Advances of UAS use in the country have created new challenges to the agricultural aviation industry in crop monitoring, pest sampling, pest detection, pest diagnosis and spraying technology. This article reviews the agricultural use of UAS in China, with a focus on plant protection. The challenges and prospects of UAS use in the country are also summarised.

Eradication of Transboundary Animal Diseases: Can the Rinderpest Success Story be Repeated?

A matrix system was developed to aid in the evaluation of the technical amenability to eradication, through mass vaccination, of transboundary animal diseases (TADs). The system involved evaluation of three basic criteria - disease management efficiency, surveillance and epidemiological factors - each in turn comprised of a number of elements (17 in all). On that basis, 25 TADs that have occurred or do occur in southern Africa and for which vaccines are available, in addition to rinderpest (incorporated as a yardstick because it has been eradicated worldwide), were ranked. Cluster analysis was also applied using the same criteria to the 26 diseases, creating division into three groups. One cluster contained only diseases transmitted by arthropods (e.g. African horse sickness and Rift Valley fever) and considered difficult to eradicate because technologies for managing parasitic arthropods on a large scale are unavailable, while a second cluster contained diseases that have been widely considered to be eradicable [rinderpest, canine rabies, the Eurasian serotypes of foot and mouth disease virus (O, A, C & Asia 1) and peste des petits ruminants] as well classical swine fever, Newcastle disease and lumpy skin disease. The third cluster contained all the other TADs evaluated with the implication that these constitute TADs that would be more difficult to eradicate. However, it is acknowledged that the scores assigned in the course of this study may be biased. The point is that the system proposed offers an objective method for assessment of the technical eradicability of TADs; the rankings and groupings derived during this study are less important than the provision of a systematic approach for further development and evaluation.

A Protocol Suggested for Management of Cantaloupe Downy Mildew

The inhibitory effect of the fungicides Acrobat copper, Previcure-N and Unilax, the inducer resistance chemicals (IRCs) bion, chitosan, salicylic acid and zinc sulphate and the bioagents Bacillus polymyxa, B. subtilis and Pseudomonas fluorescens on sporangial germination of fungus like Pseudoperonospora cubensis, the causal of cantaloupe downy mildew was tested in vitro. The role of the tested fungicides, IRCs and bioagents on management of cantaloupe downy mildew was evaluated under greenhouse conditions. In addition, the alternation between the sprayed tested fungicide Unilax, IRC chitosan and bioagent P.fluorescens on management of the disease under field conditions was investigated. The inhibitory effect of the tested fungicides, IRCs and bioagents on sporangial germination of P. cubensis revealed that they caused significant reduction to the germinated sporangia. The tested fungicides were the most efficient ones followed by the bioagents then IRCs. Disease management showed the same trend of in vitro experiment when they sprayed on artificially inoculated cantaloupe plants with the sporangia of the causal fungus like under greenhouse conditions. Under field conditions, spraying cantaloupe plants with the tested fungicide Unilax still the most efficient trial for management the disease during 2012 and 2013 growing seasons compared with spraying any of IRC (chitosan) and the bioagent (P. fluorescens) alone . However, spraying the tested fungicide in alternation with the tested IRC and the bioagent ranked the second efficiency of disease management. Meanwhile, spraying the tested fungicide in alternation with any of the tested IRC and bioagent was of moderate efficiency.

Are we measuring what matters in health technology assessment of disease management? Systematic literature review

An overview was produced of indicators currently used to assess disease management programs and, based on these findings, provide a framework regarding sets of indicators that should be used when taking the aims and types of disease management programs into account. A systematic literature review was performed. Thirty-six studies met the inclusion criteria. It appeared that a link between aims of disease management and evaluated structure, process, as well as outcome indicators does not exist in a substantial part of published studies on disease management of diabetes and asthma/chronic obstructive pulmonary disease, especially when efficiency of care is concerned. Furthermore, structure indicators are largely missing from the evaluations, although these are of major importance for the interpretation of outcomes for purposes of decision-making. Efficiency of disease management is mainly evaluated by means of process indicators; the use of outcome indicators is less common. Within a framework, structure, process, and outcome indicators for effectiveness and efficiency are recommended for each type of disease management program. The link between aims of disease management and evaluated structure, process, and outcome indicators does not exist in a substantial part of published studies on disease management. The added value of this study mainly lies in the development of a framework to guide the choice of indicators for health technology assessment of disease management.

The effectiveness of disease and case management for people with diabetes: A systematic review

Overview: This report presents the results of a systematic review of the effectiveness and economic efficiency of disease management and case management for people with diabetes and forms the basis for recommendations by the Task Force on Community Preventive Services on the use of these two interventions. Evidence supports the effectiveness of disease management on glycemic control; on screening for diabetic retinopathy, foot lesions and peripheral neuropathy, and proteinuria; and on the monitoring of lipid concentrations. This evidence is applicable to adults with diabetes in managed care organizations and community clinics in the United States and Europe. Case management is effective in improving both glycemic control and provider monitoring of glycemic control. This evidence is applicable primarily in the U.S. managed care setting for adults with type 2 diabetes. Case management is effective both when delivered in conjunction with disease management and when delivered with one or more additional educational, reminder, or support interventions.