High Level Of Expertise Research Articles

Blockchain solutions for generative AI challenges in journalism

This study aims to identify and assess AI and blockchain solutions in relation to journalistic authenticity and integrity. Central to our exploration is the role of blockchain technology in verifying content provenance. As a key component of a global Web3 framework, blockchain could offer a foundation for authenticating the origins of content. In this article, we explore how blockchain, with its capacity for creating immutable and cryptographically signed data records, could be applied by journalists to verify photos, videos and documents. Our analysis identified nine blockchain-based solutions for content verification, with three platforms–Attestiv, OriginStamp, and Fact Protocol–showing particular promise for journalistic workflows. We conclude that while AI and blockchain solutions are currently available to journalists today, they require high-level technical expertise. Many media companies are now venturing into this field as well, thus affecting the professional role of journalists in general. In our study, it is evident that integrating AI and blockchain in journalism is not merely about adopting new tools but also about understanding their broader implications for journalism as a profession and the convergence in society. The focus must remain on enhancing journalistic integrity and public trust to ensure that these technological advances benefit the field of journalism and, by extension, the democratic processes it supports.

Medical language model specialized in extracting cardiac knowledge

The advent of the Transformer has significantly altered the course of research in Natural Language Processing (NLP) within the domain of deep learning, making Transformer-based studies the mainstream in subsequent NLP research. There has also been considerable advancement in domain-specific NLP research, including the development of specialized language models for medical. These medical-specific language models were trained on medical data and demonstrated high performance. While these studies have treated the medical field as a single domain, in reality, medical is divided into multiple departments, each requiring a high level of expertise and treated as a unique domain. Recognizing this, our research focuses on constructing a model specialized for cardiology within the medical sector. Our study encompasses the creation of open-source datasets, training, and model evaluation in this nuanced domain.

Nascent and Mature RNA Profiling by Subcellular Fractionation in Human Cells.

Transcription and RNA decay determine steady-state RNA levels in cells available for translation and RNA-mediated regulatory functions. Both processes can be assessed by various techniques, for majority, based on RNA labelling or chromatin immunoprecipitation, but require a high level of expertise. Here, we describe a cost-effective, fast, and simple protocol that enables the profiling of nascent and mature RNA in the cytoplasm, nucleoplasm, and chromatin through subcellular fractionation. The workflow can include α-amanitin inhibition of RNA Polymerase II to assess nascent RNAs as a proxy of transcriptional activity, or it can be used without this treatment to investigate distribution of partially processed or mature transcripts across distinct subcellular compartments. It is applicable for studying any of RNA biotypes, including small and long noncoding RNAs, mRNAs, and their splice variants, on both transcript-specific and transcriptome-wide scales. Nascent or mature RNAs isolated from each fraction can be further analyzed by any technique of choice (northern blot, reverse transcription, RNA sequencing).

Teachers’ noticing in inclusive and special school systems: the same or different?

ABSTRACT The quality of teachers’ noticing in the inclusive classroom is supposed to depend on teachers’ expertise. As part of professional development in video clubs, we compared Austrian and Swiss teachers’ noticing of inclusive teaching. While, in Austria, teachers worked in an inclusive setting, in Switzerland the participating school ran special educational needs (SEN) classes. This led to the interesting question of whether there were differences in teachers’ noticing between the two forms of special needs education. We conducted a video club with mainstream and SEN teachers four times locally and twice cross-nationally. A qualitative content analysis was carried out and the frequencies of categories were compared quantitatively between the groups and over time. Overall, the two groups did not differ in their noticing, which is somewhat surprising but can be explained by their generally high level of expertise in relation to heterogeneous teaching. Lesson content was the only significant category where teachers in inclusive classrooms showed higher frequencies of noticing. It appears that teachers with experience in either setting were able to assess meaningful heterogeneous teaching, but that opportunities for development of inclusive teaching practices are not yet being used fully.

THE POSSIBILITIES OF USING VIRTUAL AND AUGMENTED REALITY TECHNOLOGIES IN MODERN CARDIOLOGY

Justification. Technological innovations have revolutionized medical practice, especially in the field of cardiac surgery, where the complex nature of interventions requires precision and foresight. The use of virtual (VR) and augmented reality (AR) in this field opens up huge prospects for improving preoperative planning, improving the quality of medical education and, ultimately, improving patient treatment outcomes. Aim. to analyze the literature on the role of VR/AR in modern cardiology, to discuss the current role and future directions in this field. Material and methods. As a result of the search, 3,858 publications were extracted from PubMed/MEDLINE, 69 publications from eLibrary and 1,115 publications found using Google Scholar. The search queries included the following keywords and their combinations: virtual reality, augmented reality, cardiology, cardiac surgery, virtual reality, augmented reality, cardiology, cardiac surgery. Search time interval: since the foundation of the corresponding database until May 2024. Results. Modern cardiological care includes increasingly complex procedures that require a high level of expertise. VR is becoming a powerful tool for both pre-procedural planning and educational events. VR opens up new opportunities for education and training of specialists in the field of cardiology. With its help, you can create realistic simulations of various situations that doctors may encounter in their work. This allows students to gain hands-on experience without risking real patients. Conclusion. The integration of VR into cardiology practice has great potential, but for full integration it is necessary to solve a number of problems. It is necessary to develop standards for the safety and effectiveness of using VR for medical purposes. Additional research is also needed to assess the long-term health effects of VR use on patients.

Constructing an Evaluation and Assessment System for Urology Specialists Based on Entrustable Professional Activities.

This study aimed to establish a comprehensive evaluation and assessment system for urology specialists based on entrustable professional activities (EPAs). A research group was formed to develop a preliminary index system for the evaluation and assessment of urology specialists through an extensive literature review and group discussions. The Delphi method was employed to design a questionnaire, and two rounds of expert consultations were conducted to finalize the index system and design a corresponding assessment method. A total of 24 experts participated in the two rounds of consultations, with authority coefficients of 0.885 and 0.894, and agreement levels of 0.270 and 0.256, respectively, indicating a high level of expertise and strong consensus among the participants. Based on the expert feedback, 16 key indicators were identified: Recognizing and admitting urological patients, selecting and interpreting specialized tests, diagnosing and differentiating diseases, making therapeutic decisions, documenting medical information, delivering oral clinical case reports, identifying and managing primary care and speciality medical problems, managing critical illness, handling patient transfers and departmental handovers, communicating informed medical decisions, performing basic and specialized surgical operations, providing health guidance, responding to public health emergencies, and engaging in clinical teaching. All indicators scored above 3.5 in importance, with coefficients of variation below 0.25, signifying their appropriateness. The expected confidence levels for each indicator at various stages were determined, and a comprehensive assessment method encompassing "process evaluation, peer impression review, and endpoint assessment" was developed. This research successfully established an initial EPAs-based evaluation and assessment index system for urology specialists, defined the expected confidence levels for each indicator across different time points, and devised a specific assessment methodology, thereby providing a scientific foundation for the training and evaluation of urology specialists.

Towards real-time calibration-free LIBS supported by machine learning

Calibration-Free Laser-Induced Breakdown Spectroscopy (CF-LIBS) enables multi-elemental quantification without needing standards. This type of approach can be used to analyze complex samples containing traces or gradients of species. This type of diagnosis requires a high level of expertise, and is cumbersome to set up. These constraints limit its application to field diagnostics. Using the MERLIN generalized radiative transfer code, we are able to generate a diversified emission database with no dimensioning limitations. We show that training a convolutional residual network with such a database enables the quantification of 9 species, as well as evaluation of electron density and temperature, without any prior expertise at a rate greater than 10 Hz. The accuracy of this innovative method depends solely on the basic spectroscopic data (emission probabilities and Stark parameters), regardless of the thermodynamic conditions of the laser-induced plasma, as long as it is in Local Thermodynamic Equilibrium (LTE).

Machine learning based finite element analysis (FEA) surrogate for hip fracture risk assessment and visualization

Classical computational biomechanical approaches are costly and require a high level of expertise, often limiting their clinical application. A data-driven Machine Learning (ML) framework can serve as an effective alternative for disease diagnosis and prediction. This study aimed to develop an ML-based modeling approach integrating Quantitative Computed Tomography-based Finite Element Analysis (QCT-based FEA) to assess fall-induced hip fracture probability and probable facture locations in an elderly individual. Specifically, this study focused on predicting patient-specific hip fracture risk using fracture risk index (FRI), calculated on the basis of 1st and 3rd principal strains, and visualizing strain distributions in the proximal femur by generating surrogate FE models via supervised CatBoost model. The training dataset, comprising clinical, anatomical, and mechanical (loading) features, was obtained from QCT image data and QCT-based FEA, which also provided the FRI and strain distributions as targets. The optimized CatBoost model demonstrated 76% accuracy with an AUROC of 0.81 in fracture risk prediction, as well as correlation coefficients of 0.73 for the 1st principal strain and 0.76 for the 3rd principal strain in the surrogate FE models for visualization. Although trained on a limited dataset, this study highlights the efficacy of ML-based surrogate modeling in the QCT-based FEA process for predicting hip fracture risk and visually identifying fracture locations.

Taking Law Seriously: The Challenges of Law as Research Data in Socio-legal Scholarship

‘Law-as-data’ is often understood in terms of computational data. This article reframes it around a research-centred notion of data, broadening the methodological possibilities available to empirical legal research. This approach transcends the qualitative/quantitative divide, identifying common challenges that arise when law is treated as research data. Methodological tools from the social sciences and examples from recently published socio-legal scholarship are used to demonstrate how law presents unique challenges at the stages of conceptualisation, operationalisation and coding. These challenges stem from factors such as the non-commensurability of legal texts, the fluidity and contested meaning of legal notions, the difficulty of systematically gathering legal data and the risk of bias towards data-driven research rather than law- or reality-centred inquiry. However, strategies and best practices can be developed to address or mitigate these risks. Legal academics are particularly well suited to this task, given the high level of expertise required to effectively leverage law as research data. The article argues that a true methodological naturalisation of law as research data, which embraces the unique characteristics and limitations of law as a data source, would greatly benefit legal studies by diversifying its methodological toolbox while preserving its specificities.

The School Budget Management under the Nakhon Ratchasima Primary Educational Service Area Office 4

Background and Aims: Budgeting in its present form began in England around the 16th-17th centuries. During that time The House of Representatives successfully reserved the power to approve government revenue and expenditures. This shows that current budgeting is related to the evolution of the governance system. After the preparation of this budget, it was extended to local government organizations, educational institutions, and other agencies, both public and private. The purpose of this research is to study and compare the level of opinions of personnel regarding budget management of schools under the Nakhon Ratchasima Primary Educational Service Area Office 4. Classified by position, school size, and work experience. Including the study of guidelines for developing budget management for schools under the said organization. Methodology: The sample used in the research consisted of 196 school administrators and heads of budget management groups of educational institutions under the Nakhon Ratchasima Primary Educational Service Area Office 4, which were selected using a stratified random sampling method. The research instrument was a questionnaire with a 5-level rating scale with an IOC between 0.80 - 1.00 and a confidence value of 0.85. In addition, a structured interview was used to collect data for data analysis. Various statistics were used, including frequency, percentage, mean, and standard deviation, T-test and F-test. Results: The results of the research are as follows: (1) School budget management Under the jurisdiction of the Nakhon Ratchasima Primary Educational Service Area Office 4, the overall level is at a high level. And when considering each aspect, it was found that budget management was at a high level in every aspect. (2) Comparative analysis of budget management of schools under the jurisdiction of the Nakhon Ratchasima Primary Educational Service Area Office 4, found to be significantly different at the 0.05 level. When classified by position but when classified by school size and work experience, no differences were found. (3) The budget management guidelines of schools under the Nakhon Ratchasima Primary Educational Service Area Office 4 that should be implemented include: Analysis of strengths and weaknesses Budget allocation is adequate and comprehensive. Having an internal audit system Creating a fundraising plan and using resources from outside agencies or stakeholders. Including having proof of receipt and payment. Preparing accurate accounts according to regulations and reporting information on supplies and equipment (M-OBEC), including creating up-to-date information on buildings, land, structures, and assets (B-OBEC). Conclusion: Budget management of schools under the jurisdiction of the Nakhon Ratchasima Primary Educational Service Office, District 4, that were all assessed were considered to have a high level of expertise. To ensure continued efficiency and transparency in financial management. There should be various guidelines such as detailed data analysis. Establishment of an internal audit system and recording information correctly All of which should be strictly implemented to strengthen confidence in the budget management process.

ASPECTS OF SCIENTIFIC RESEARCH IN UNIVERSAL ENVIRONMENTAL TREATIES: LEGAL AND ECONOMIC ANALYSIS

The article emphasises that aspects of international legal cooperation in the field of scientific research have not yet been the subject of a systematic, comprehensive scientific study either in the context of international law doctrine or in relevant publications. The subject of the study is the legal regulation of scientific research in universal international environmental treaties, with the aim of identifying the specific features of such international legal mechanisms. The paper examines the challenges and paradoxes faced by international environmental law in regulating scientific research as a process, as well as in balancing scientific research with the practical needs for appropriate resources. The research uses hermeneutical, prognostic, comparative and systemic analysis of relevant international treaties as components of the modern system of international law. It applies a complex set of methods, including analysis and synthesis, structural and formal-legal methods, as well as scientific deduction and induction approaches. The combination of these methods provides a thorough examination of how international environmental treaties regulate scientific research. The methodology includes a hermeneutic approach to interpret the language and provisions of various environmental treaties, a predictive approach to forecast potential developments and the outcomes of current regulation, a comparative analysis to identify similarities and differences between different treaties, and a systems analysis to understand how these treaties fit into the broader system of international law. Taken together, these methods provide a reliable basis for assessing the current state and future directions of international legal cooperation in environmental research. The main objective of the article is to reveal the unique characteristics and complexities inherent in the international legal mechanisms governing scientific research under environmental treaties. It aims to highlight the current challenges and paradoxes faced by international environmental law in this context. These include the need to address global environmental problems with often limited and unreliable scientific data, the need to respect and encourage scientific research, and the imperative to balance the search for scientific knowledge with the practical needs of natural resources. The results of the study show that environmental conventions reflect several important challenges. One of the main challenges is to address global environmental issues in the face of a lack of reliable scientific information, while respecting scientific research and its results. Another crucial challenge is to maintain a balance between promoting scientific research and meeting the practical resource needs of humanity. The author posits that international environmental law sources represent an enduring endeavour to achieve equilibrium in the formation of convention bodies. This equilibrium strives to reconcile the sovereign interests of participating states with the necessity of maintaining a high level of expertise within relevant commissions and committees. In conclusion, the paper emphasises the necessity for the continuous development and refinement of legal regulation pertaining to scientific research within the field of international environmental law. This is in order to effectively address the dynamic and complex interplay between scientific advancement and environmental sustainability.

Minimally invasive treatment of an internal pancreaticopleural fistula with massive pleural effusion: a case report

BackgroundA pancreatic duct rupture can lead to various complications such as a fistula, pseudocyst, ascites, or walled-off necrosis. Due to pleural effusion, pancreaticopleural fistula typically causes dyspnea and chest pain. Leaks of enzyme-rich pancreatic fluid forming a pleural effusion can be verified in a thoracocentesis following radiological imaging such as computed tomography or magnetic resonance tomography. While management strategies range from a conservative to endoscopic and surgical approach, we report a case with successful minimally invasive treatment of pancreaticopleural fistula and effusion.Case presentationWe present a case of a patient with pancreaticopleural fistula and successful minimally invasive surgical treatment. A 62-year old Caucasian man presented with acute chest pain and dyspnea. A computed tomography scan identified a left-sided cystoid formation, extending from the abdominal cavity into the left hemithorax with concomitant pleural effusion. Pleural effusion analysis indicated significantly elevated pancreatic enzymes. Magnetic resonance cholangiopancreatography revealed a rupture of the pancreatic duct and nearby fluid accumulation. Endosonography later confirmed proximity to the tail of the pancreas, suggesting a pancreatic pseudocyst with visible tract into the pancreas. We assumed a pancreatic duct rupture with a fistula from the tail of the pancreas transdiaphragmatically into the left hemithorax with a commencing pleural empyema. A visceral and parietal decortication on the left hemithorax and a laparoscopic distal pancreatectomy with splenectomy was performed. The suspected diagnosis of a fistula arising from the pancreatic duct was confirmed histologically.ConclusionPancreaticopleural fistulas often have a long course and may remain undiagnosed for a long time. At this point diagnostic management and therapy demand a high level of expertise. In instances of unclear symptomatic pleural effusion, considering an abdominal focus is crucial. If endoscopic treatment is not feasible, minimally invasive surgery should strongly be considered, especially when located in the distal pancreas.

Using Large Language Models for Microbiome Findings Reports in Laboratory Diagnostics

Background: Advancements in genomic technologies are rapidly evolving, with the potential to transform laboratory diagnostics by enabling high-throughput analysis of complex biological data, such as microbiome data. Large Language Models (LLMs) have shown significant promise in extracting actionable insights from vast datasets, but their application in generating microbiome findings reports with clinical interpretations and lifestyle recommendations has not been explored yet. Methods: This article introduces an innovative framework that utilizes LLMs to automate the generation of findings reports in the context of microbiome diagnostics. The proposed model integrates LLMs within an event-driven, workflow-based architecture, designed to enhance scalability and adaptability in clinical laboratory environments. Special focus is given to aligning the model with clinical standards and regulatory guidelines such as the In-Vitro Diagnostic Regulation (IVDR) and the guidelines published by the High-Level Expert Group on Artificial Intelligence (HLEG AI). The implementation of this model was demonstrated through a prototype called “MicroFlow”. Results: The implementation of MicroFlow indicates the viability of automating findings report generation using LLMs. Initial evaluation by laboratory expert users indicated that the integration of LLMs is promising, with the generated reports being plausible and useful, although further testing on real-world data is necessary to assess the model’s accuracy and reliability. Conclusions: This work presents a potential approach for using LLMs to support the generation of findings reports in microbiome diagnostics. While the initial results seem promising, further evaluation and refinement are needed to ensure the model’s effectiveness and adherence to clinical standards. Future efforts will focus on improvements based on feedback from laboratory experts and comprehensive testing on real patient data.

Why trust scientists?

Wonder implies the desire to learn. Aristotle A number of surveys, conducted in the aftermath of COVID19, have found that levels of trust in scientists are moderately high worldwide, although there are significant differences between countries. As the largest global survey reveals, scientists are perceived to have a high level of expertise, moderate integrity and benevolent intentions. Although with a different research focus, the 2024 edition of the Edelman Trust Barometer also found that trust in scientists is high relative to government leaders or CEOs, making them expected to lead the implementation of innovations.

LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.

The Treatment of Metastatic Renal Cell Carcinoma.

Approximately 15 000 people receive a diagnosis of renal cell carcinoma (RCC) in Germany each year; in 20-30% of cases, metastatic RCC (mRCC) is already present at the time of diagnosis. This disease in the metastatic stage is still mainly treated palliatively, yet the multimodal therapeutic landscape has changed markedly over the past 15 years, with the approval of many new treatments for patients with mRCC. This review is based on prospective studies retrieved by a selective search in PubMed and the ASCO and ESMO databases and on the German and European oncological and urological guidelines for RCC. Drugs are the mainstay of treatment. mRCC can be treated with a combination of two immune checkpoint inhibitors (CPIs), a CPI and a tyrosine-kinase inhibitor (TKI) (evidence level IA), or a TKI as monotherapy (evidence level IIC-IC). With prognosis-based sequential drug treatment, a mean progressionfree survival of 12 to 24 months and an overall survival of approximately 50 months can be achieved from the time of initiation of first-line therapy. Aside from pharmacotherapy, the multidisciplinary tumor board should evaluate the indications for local treatments such as cytoreductive nephrectomy, metastasectomy, and radiotherapy, depending on the individual prognostic constellation and the patient's present condition. Optimal individualized decisions require a high level of expertise and the collabo - ration of a multidisciplinary tumor board. Older prognostic parameters currently play a leading role in decision-making, while predictive parameters and molecular markers are not yet adequately validated.

Self-medication for Cough: a Study of Smokers and Non-smokers in Surabaya Colleges

The number of smokers among students is increasing. Smoking can cause coughing, the body's defense mechanism for clearing the airways. Cough can be treated with self-medication, which can cause drug-related problems. The level of knowledge and perception can influence the choice of cough medicine used. The purpose of this study was to determine the knowledge and perceptions of cough medicine self-medication in smokers and non-smokers. This research method was a case-control study using a questionnaire of knowledge (definition, method of use, and indications) and perceptions (definition, side effects, and experience) regarding using self-medicated cough medicine. The research sample was adult students at a private university in Surabaya from January 2023 to April 2023, and it was collected using a purposive sampling method. The sample size was 124 respondents, 62 smoking and 62 non-smoking students. The level of knowledge in both groups was high, namely 51.62% in the smoker group and 58.84% in the non-smoker group. Meanwhile, the perception level in the smokers group was mostly bad (56.45%), in contrast to the non-smokers who had a good level of perception (64.52%). There was a significant difference in the level of knowledge (0.00) and perception (0.00) between smokers and non-smokers regarding cough medicine self-medication. Therefore, smokers have the same high level of expertise but lower levels of perception than non-smokers. Health promotion programs to increase knowledge are essential considerations in optimizing self-medication.

Impact of resident training on cardiac electrophysiological procedures

BackgroundModern management of cardiac arrhythmias often requires interventions in which young physicians must acquire a high level of expertise. However, concerns have been raised about the increase in side effects during procedures performed with resident involvement. AimThis study aims to identify the effects of resident training on cardiac electrophysiological procedures within a university centre. MethodsIn a single-centre study, cardiac arrhythmia procedures were reviewed retrospectively, and resident involvement was scrutinized. Univariate and multivariable models were built for the following outcomes: fluoroscopy time; operative time; length of hospitalization after procedure; and adverse events. ResultsWe reviewed 991 procedures, 574 without and 417 with resident involvement (650 cardiac pacemakers or defibrillators, 120 generator replacements, 188 electrophysiological studies and 153 radiofrequency ablations). Resident involvement was associated with an increase in fluoroscopy time: +1.7±0.4minutes (P<0.01) for pacemaker implantation; and +2.5±0.9minutes (P=0.01) for electrophysiological studies. Operative time was longer for electrophysiological studies (+10.8±4.9minutes; P=0.03) and pacing implantation (+8.4±2.2minutes; P<0.01). There was no significant association between resident training and adverse events (7.67 vs. 9.83%; P=0.28). ConclusionsCardiac electrophysiological procedures performed with resident involvement have a good safety profile. However, resident training modestly, but significantly, prolongs fluoroscopy time and operative time.

Deep transfer learning-based bird species classification using mel spectrogram images.

The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification. This study aims to establish an audio-based bird species classification system for 264 Eastern African bird species employing modified deep transfer learning. In particular, the pre-trained EfficientNet technique was utilized for the investigation. The study adapts the fine-tune model to learn the pertinent patterns from mel spectrogram images specific to this bird species classification task. The fine-tuned EfficientNet model combined with a type of Recurrent Neural Networks (RNNs) namely Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). RNNs are employed to capture the temporal dependencies in audio signals, thereby enhancing bird species classification accuracy. The dataset utilized in this work contains nearly 17,000 bird sound recordings across a diverse range of species. The experiment was conducted with several combinations of EfficientNet and RNNs, and EfficientNet-B7 with GRU surpasses other experimental models with an accuracy of 84.03% and a macro-average precision score of 0.8342.

Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review

Malaria is a disease that affects millions of people worldwide with a consistent mortality rate. The light microscope examination is the gold standard for detecting infection by malaria parasites. Still, it is limited by long timescales and requires a high level of expertise from pathologists. Early diagnosis of this disease is necessary to achieve timely and effective treatment, which avoids tragic consequences, thus leading to the development of computer-aided diagnosis systems based on artificial intelligence (AI) for the detection and classification of blood cells infected with the malaria parasite in blood smear images. Such systems involve an articulated pipeline, culminating in the use of machine learning and deep learning approaches, the main branches of AI. Here, we present a systematic literature review of recent research on the use of automated algorithms to identify and classify malaria parasites in blood smear images. Based on the PRISMA 2020 criteria, a search was conducted using several electronic databases including PubMed, Scopus, and arXiv by applying inclusion/exclusion filters. From the 606 initial records identified, 135 eligible studies were selected and analyzed. Many promising results were achieved, and some mobile and web applications were developed to address resource and expertise limitations in developing countries.