Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling

Accurate evaluation of thyroid nodules is crucial for effective management; however, methods such as ultrasonography and Fine Needle Aspiration Cytology (FNAC) can be subjective and operator-dependent. Indeterminate thyroid nodules (ITNs) complicate diagnosis, coming at the expense of time, money, and potentially additional FNA samplings, causing more discomfort for the patients. Recent advancements in artificial intelligence (AI) assisted ultrasound diagnosis system have demonstrated excellent diagnostic performance and the potential to aid in the differentiation of ITNs. This study aims to develop an AI classifier that integrates the AI-assisted ultrasound diagnosis system, FNAC, and demographic data to enhance the differentiation of benign and malignant thyroid nodules, and to compare the diagnostic performance of the models, with a focus on diagnosing ITNs. In the present research, 620 thyroid nodules were collected from a single medical center and divided into training and testing cohorts (Testing1). We developed five AI models using distinct classification algorithms (Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine) that integrate demographic data, cytological findings, and an AI-assisted ultrasound diagnostic system for thyroid nodule assessment. These models underwent prospective validation (Testing2, n = 243) to identify the optimal model. A subsequent prospective study (Testing3) involving 70 thyroid nodules further evaluated the model's performance, where the selected optimal model was compared against FNAC combined with BRAF V600E mutation analysis. After validation with the Testing1 and Testing2 cohorts, the Random Forest (RF) model demonstrated the best overall performance among the five classifiers. The area under the curve (AUC) for the RF model to diagnose thyroid nodules was 0.994 in the training cohort, 0.993 in the testing cohort, and 0.977 in the prospective data. In addition, for 42 included ITNs in the prospective data, the accuracy, sensitivity, and specificity of the RF model were 90.48%, 89.47%, and 91.30%, respectively. In the Testing 3 cohort, the RF model demonstrated superior diagnostic performance compared to both the standalone AI ultrasound auxiliary diagnostic system and FNAC alone. Its accuracy was comparable to FNAC combined with BRAF V600E mutation analysis. Conclusion: Our developed thyroid nodule AI diagnostic model shows favorable predictive value. It can serve as a decision support tool for non-thyroid specialists and assist thyroid surgeons in the management of ITN. Our developed thyroid nodule AI diagnostic model shows favorable predictive value. It can serve as a decision support tool for non-thyroid specialists and assist thyroid surgeons in the management of ITN.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

To predict the potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into O. hupensis surveillance and control in Yunnan Province. The O. hupensis snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into O. hupensis snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population). Variables were screened with Pearson correlation test and variance inflation factor (VIF) test. The RF and MaxEnt models and the ensemble model were created using the biomod2 package of the software R 4.2.1, and the potential distribution of O. hupensis snails after 2016 was predicted in Yunnan Province. The predictive effects of models were evaluated through cross-validation and independent tests, and the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS) and Kappa statistics were used for model evaluation. In addition, the importance of environmental variables was analyzed, the contribution of environmental variables output by the models with AUC values of > 0.950 and TSS values of > 0.850 were selected for normalization processing, and the importance percentage of environmental variables was obtained to analyze the importance of environmental variables. Data of 148 O. hupensis snail distribution sites and 15 environmental variables were included in training sets of RF and MaxEnt models, and both RF and MaxEnt models had high predictive performance, with both mean AUC values of > 0.900 and all mean TSS values and Kappa values of > 0.800, and significant differences in the AUC (t = 19.862, P < 0.05), TSS (t = 10.140, P < 0.05) and Kappa values (t = 10.237, P < 0.05) between two models. The AUC, TSS and Kappa values of the ensemble model were 0.996, 0.954 and 0.920, respectively. Independent data verification showed that the AUC, TSS and Kappa values of the RF model and the ensemble model were all 1, which still showed high performance in unknown data modeling, and the MaxEnt model showed poor performance, with TSS and Kappa values of 0 for 24%(24/100) of the modeling results. The modeling results of 79 RF models, 38 MaxEnt models and their ensemble models with AUC values of > 0.950 and TSS values of > 0.850 were included in the evaluation of importance of environmental variables. The importance of annual sunshine duration (SSD) was 32.989%, 37.847% and 46.315% in the RF model, the MaxEnt model and their ensemble model, while the importance of annual mean relative humidity (RHU) was 30.947%, 15.921% and 28.121%, respectively. Important environment variables were concentrated in modeling results of the RF model, dispersed in modeling results of the MaxEnt model, and most concentrated in modeling results of the ensemble model. The potential distribution of O. hupensis snails after 2016 was predicted to be relatively concentrated in Yunnan Province by the RF model and relatively large by the MaxEnt model, and the distribution of O. hupensis snails predicted by the ensemble model was mostly the joint distribution of O. hupensis snails predicted by RF and MaxEnt models. Both RF and MaxEnt models are effective to predict the potential distribution of O. hupensis snails in Yunnan Province, which facilitates targeted O. hupensis snail control.

Cancer remains a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for early detection and effective management. Predictive analytics, powered by machine learning (ML) and artificial intelligence (AI), offers a transformative potential in cancer care by integrating multi-source health data for early diagnosis, risk assessment, and personalized treatment. This presents a predictive analytics framework that leverages diverse data sources, including electronic health records (EHRs), genomic and biomarker data, medical imaging, wearable sensors, and lifestyle/environmental factors. By combining these heterogeneous data streams, AI models can identify high-risk individuals, detect cancer at its earliest stages, and optimize treatment strategies based on patient-specific insights. The framework employs advanced ML techniques, such as supervised learning models (Random Forests, Support Vector Machines), deep learning architectures (Convolutional Neural Networks, Recurrent Neural Networks), and Natural Language Processing (NLP) for analyzing clinical notes. Federated learning is also explored as a privacy-preserving approach to facilitate secure data sharing across institutions while maintaining compliance with regulatory standards such as HIPAA and GDPR. The integration of predictive analytics with telemedicine platforms and AI-driven clinical decision support systems further enhances real-time monitoring and personalized patient care. Despite its promise, challenges such as data privacy concerns, heterogeneity in multi-source data integration, model interpretability, and computational scalability must be addressed to ensure the effective implementation of AI-driven cancer diagnostics. Future advancements in Explainable AI (XAI), blockchain for secure data exchange, and multi-modal data fusion will further strengthen predictive analytics in oncology. This study underscores the critical role of big data and AI in revolutionizing cancer detection and management, paving the way for improved patient outcomes and more efficient healthcare delivery.

Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture

Quantum computing (QC) is not a futuristic notion in agriculture, though its full potential has yet to be realized. QC is an emerging field at the intersection of physics and computer science that holds immense potential to revolutionize various sectors, including agriculture production and artificial intelligence (AI) modeling. While QC is still in the early stages of development and practical applications within agriculture are not yet widespread, researchers are actively exploring its potential benefits in various agricultural domains, including crop optimization, livestock breeding, and environmental monitoring. QC harnesses the principles of quantum mechanics to perform computations using quantum bits or qubits, which can exist in multiple states simultaneously. Unlike classical computers, which rely on binary bits representing 0 or 1, quantum computers exploit phenomena such as superposition and entanglement to process information in parallel, potentially offering exponential speedup for certain types of problems. In agriculture production, particularly in animal science, QC offers promising avenues for optimizing processes and enhancing productivity. Quantum algorithms can analyze vast amounts of genomic data to improve breeding programs, leading to the development of more resilient and productive livestock breeds. Furthermore, QC can facilitate precision farming techniques by modeling complex environmental factors and animal behavior to optimize feeding strategies, disease management, and overall farm management practices. Moreover, QC can significantly benefit AI modeling by accelerating computations and enabling more efficient training of AI models. Quantum algorithms can enhance the performance of AI algorithms in various tasks, including pattern recognition, natural language processing, and predictive analytics. By leveraging quantum-enhanced optimization algorithms, AI models can achieve better convergence and accuracy, leading to more effective decision-making and problem-solving capabilities. While hybrid intelligent models also represent a novel frontier in agriculture, QC has the potential to expedite the merging of mechanistic and AI modeling paradigms, facilitating a more holistic understanding of complex systems in agriculture and beyond. By integrating mechanistic models, which describe the underlying physical processes, with AI models, which learn patterns from data, quantum computing can enable comprehensive simulations and predictions of agricultural systems. This fusion of modeling paradigms can lead to more accurate and robust predictions of crop yields, livestock performance, and environmental impacts, facilitating informed decision-making for farmers and policymakers. The application of QC in agriculture, however, requires interdisciplinary collaborations between physicists, computer scientists, agronomists/animal scientists, and AI researchers. These collaborations can drive the development of quantum algorithms tailored to agricultural applications, the integration of quantum-enhanced AI techniques into existing modeling frameworks, and the deployment of QC resources in real-world agricultural systems. Ultimately, harnessing the power of QC holds the potential to revolutionize agriculture production practices, including regenerative agriculture, and advance AI modeling capabilities, paving the way for a more sustainable and efficient agricultural industry.

IntroductionThe utility of non-contact technologies for screening infectious diseases such as COVID-19 can be enhanced by improving the underlying Artificial Intelligence (AI) models and integrating them into data visualization frameworks. AI models that are a fusion of different Machine Learning (ML) models where one has leveraged the different positive attributes of these models have the potential to perform better in detecting infectious diseases such as COVID-19. Furthermore, integrating other patient data such as clinical, socio-demographic, economic and environmental variables with the image data (e.g., chest X-rays) can enhance the detection capacity of these models.MethodsIn this study, we explore the use of chest X-ray data in training an optimized hybrid AI model based on a real-world dataset with limited sample size to screen patients with COVID-19. We develop a hybrid Convolutional Neural Network (CNN) and Random Forest (RF) model based on image features extracted through a CNN and EfficientNet B0 Transfer Learning Model and applied to an RF classifier. Our approach includes an intermediate step of using the RF's wrapper function, the Boruta Algorithm, to select important variable features and further reduce the number of features prior to using the RF model.Results and discussionThe new model obtained an accuracy and recall of 96% for both and outperformed the base CNN model and four other experimental models that combined transfer learning and alternative options for dimensionality reduction. The performance of the model fares closely to relatively similar models previously developed, which were trained on large datasets drawn from different country contexts. The performance of the model is very close to that of the “gold standard” PCR tests, which demonstrates the potential for use of this approach to efficiently scale-up surveillance and screening capacities in resource limited settings.

Introduction: This research examines the potential of cloud-based healthcare systems, enhanced by artificial intelligence (AI) and predictive analytics, to improve patient care. With increasing demand for more efficient healthcare solutions, AI-driven systems offer promising advancements in real-time health monitoring, risk assessments, and personalized treatment recommendations. By integrating machine learning algorithms like random forests, support vector machines (SVMs), and gradient boosting, these systems predict patient health trends and identify high-risk individuals. This research aims to demonstrate how AI-powered frameworks can optimize resource allocation, improve health outcomes, reduce costs, and enhance overall patient satisfaction. Top of Form Bottom of Form Objectives: This research are to develop AI-driven cloud-based healthcare systems for real-time patient monitoring, risk assessment, personalized treatment recommendations, and improved resource allocation to enhance patient care. Methods: This research employs a multi-stage methodology to evaluate the potential of AI-driven cloud-based healthcare systems. First, a comprehensive literature review is conducted to explore current advancements in healthcare technologies, AI algorithms, and cloud infrastructures. Next, various machine learning algorithms, including random forests, support vector machines (SVMs), and gradient boosting, are developed and integrated into predictive analytics frameworks. These algorithms process large-scale healthcare datasets to predict patient health, risk factors, and disease progression. The developed systems are tested in cloud environments to assess their effectiveness in real-time health monitoring, risk assessment, and targeted treatment suggestions. Top of Form Bottom of Form Results: AI-driven cloud-based healthcare systems significantly improve patient care. Predictive analytics frameworks, utilizing random forests, SVMs, and gradient-boosting algorithms, accurately forecast patient outcomes, identify high-risk individuals, and predict disease progression. Real-time health monitoring and risk assessment enabled by these systems allow healthcare providers to deliver more targeted treatment recommendations and optimize resource allocation. The research shows potential for enhanced patient outcomes, reduced healthcare costs, and improved patient satisfaction. These findings confirm that combining AI and cloud-based systems can transform the healthcare industry by offering data-driven, personalized care solutions. Conclusions: The cloud-based healthcare systems powered by AI and predictive analytics can significantly enhance patient care. By enabling real-time monitoring, accurate risk assessment, and personalized treatments, these systems improve health outcomes, reduce costs, and increase patient satisfaction, offering a transformative approach to modern healthcare management. &nbsp;

High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiological features and biopsy method. Using artificial intelligence and machine learning models in breast imaging, evaluations can be made in terms of risk estimation in different research areas. This study aimed to develop a machine learning model to distinguish HRL cases requiring surgical excision from lesions with a low risk of accompanying malignancy. A total of 94 patients who were diagnosed with HRL by image-guided biopsy between January 2008 and March 2020 were included in the study. A structured database was created with clinical and radiological characteristics and histopathological results. A machine learning prediction model was created to make binary classifications of lesions as malignant or benign. Random forest, decision tree, K-nearest neighbors, logistic regression, support vector machine (SVM), and multilayer perceptron machine learning algorithms were used. Among these algorithms, SVM was the most successful. The estimations of malignancy for each case detected by artificial intelligence were combined and statistical analyses were performed. Considering all cases, the malignancy upgrade rate was 24.5%. A significant association was observed between malignancy upgrade rate and lesion size (P = 0.004), presence of mammography findings (P = 0.022), and breast imaging-reporting and data system category (P = 0.001). A statistically significant association was also found between the artificial intelligence prediction model and malignancy upgrade rate (P < 0.001). With the SVM model, an 84% accuracy and 0.786 area-underthe- curve score were obtained in classifying the data as benign or malignant. Our artificial intelligence model (SVM) can predict HRLs that can be followed up with a lower risk of accompanying malignancy. Unnecessary surgeries can be reduced, or second line vacuum excisions can be performed in HRLs, which are mostly benign, by evaluating on a case-by-case basis, in line with radiology-pathology compatibility and by using an artificial intelligence model.

MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer

BackgroundLumbar drainage is widely used in the clinic; however, forecasting lumbar drainage-related meningitis (LDRM) is limited. We aimed to establish prediction models using supervised machine learning (ML) algorithms.MethodsWe utilized a cohort of 273 eligible lumbar drainage cases. Data were preprocessed and split into training and testing sets. Optimal hyper-parameters were archived by 10-fold cross-validation and grid search. The support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were adopted for model training. The area under the operating characteristic curve (AUROC) and precision-recall curve (AUPRC), true positive ratio (TPR), true negative ratio (TNR), specificity, sensitivity, accuracy, and kappa coefficient were used for model evaluation. All trained models were internally validated. The importance of features was also analyzed.ResultsIn the training set, all the models had AUROC exceeding 0.8. SVM and the RF models had an AUPRC of more than 0.6, but the ANN model had an unexpectedly low AUPRC (0.380). The RF and ANN models revealed similar TPR, whereas the ANN model had a higher TNR and demonstrated better specificity, sensitivity, accuracy, and kappa efficiency. In the testing set, most performance indicators of established models decreased. However, the RF and AVM models maintained adequate AUROC (0.828 vs. 0.719) and AUPRC (0.413 vs. 0.520), and the RF model also had better TPR, specificity, sensitivity, accuracy, and kappa efficiency. Site leakage showed the most considerable mean decrease in accuracy.ConclusionsThe RF and SVM models could predict LDRM, in which the RF model owned the best performance, and site leakage was the most meaningful predictor.

Landslide susceptibility evaluation is critical for landslide prevention and risk management. Based on the slope unit, this study uses the information value method- random forest (IV-RF) model to evaluate the landslide susceptibility in the deep valley area. First, based on the historical landslide data, a landslide inventory was developed by using remote sensing technology (InSAR and optical remote sensing) and field investigation methods. Twelve factors were then selected as the input data for a landslide susceptibility model. Second, slope units with different scales were obtained by the r.slopeunits method and the information value method- random forest (IV-RF) model is used to evaluate the landslide susceptibility. Finally, the spatial distribution characteristics of landslide susceptibility grade under the optimal scale are analyzed. The results showed that under the slope unit obtained when c = 0.1 and a = 3 × 105 m2, the internal homogeneity/external heterogeneity of 8425 slope units extracted by the r.slopeunits method is the best, with an AUC of 0.905 and an F1 of 0.908. In this case, the accuracy of landslide susceptibility evaluation is the highest as well; it is shown that the finer slope units would not always lead to the higher accuracy of landslide susceptibility evaluation results; it is necessary to comprehensively consider the internal homogeneity and external heterogeneity of the slope units. Under the optimal slope unit scale, the number of landslides in the highly and extremely highly susceptible areas in the landslide susceptibility map accounted for 82.60% of the total number of landslides, which was consistent with the actual distribution of landslides; this study shows that the method, combining the slope unit and the information value method- random forest (IV-RF) model, for landslide susceptibility evaluation can obtain high accuracy.

Predictive artificial intelligence (AI) models for the assessment of factors and trends related to disease prevalence, prognosis, and the risk prediction of mortality in the early phases, seem to be effective techniques. Machine learning (ML) algorithms as a branch of AI methods have had a considerable role in controlling and management of COVID-19 and previous pandemics. The present study estimates the mortality risk among COVID-19 patients based on ML algorithms, prediction of outcomes in new cases, and also determines the top significant features related to morality in the patients. In this retrospective research, 7115 patients with confirmed COVID-19 disease were included in the study. The information was collected from 35 different hospitals across Khorasan-Razavi province, Northeast of Iran during 20 February 2020 - 21 June 2021. We employed random forests (RF), decision tree (DT), artificial neural networks (ANN), support vector machines (SVM), logistic regression (LR), and boosted trees (BT) ML algorithms. Our findings indicated that the RF model had better performance in different criteria. The RF algorithm had area under curve (AUC) of 88%, accuracy of 96%, precision of 96%, sensitivity of 99%, and specificity of 80%. Intubation, SpO2, age, time from symptoms, loss of consciousness, reception season, and diabetes were the seven ordered and significant predictors of the risk of COVID-19 patients’ death. RF model can aid in clinical decision-making and limited resource allocation. This model needs to be externally validated in larger populations, more features, and multicenter settings. Received: August 4, 2023Accepted: September 13, 2023

The integration of Artificial Intelligence (AI) in agriculture has emerged as a transformative approach to enhance productivity, sustainability, and resource efficiency. This study focuses on the optimum utilization of AI technologies such as machine learning, computer vision, and data analytics in modern farming practices. AI enables precise monitoring of crop health, soil conditions, weather patterns, and pest infestations, thereby supporting data-driven decision-making. Techniques like predictive analytics help in yield forecasting, irrigation management, and disease detection, reducing losses and operational costs. Furthermore, the use of AI-powered automation, including smart irrigation systems and autonomous machinery, minimizes human effort while maximizing output. The paper highlights the challenges in implementation, such as high initial investment, lack of technical awareness, and data accessibility issues, particularly in developing regions. It also explores potential solutions to overcome these barriers for widespread adoption. Overall, the optimum utilization of AI in agriculture can significantly contribute to food security, environmental conservation, and sustainable development.

The research project aims to develop and install environmental monitoring systems in hemi-boreal ecosystems, utilizing low-cost technologies and artificial intelligence (AI). The proposal integrates sensors to measure atmospheric CO2 by the collocation method, calibration and analysis methods are based on machine learning. The initiative seeks to understand atmospheric dynamics in forests (Järvselja Experimental Forest, Estonia), evaluating carbon responses to environmental changes. The systems are field-calibrated using certified reference equipment and enhanced with AI models, such as Random Forest and XGBoost, to ensure data precision and reliability. Sensors will be used to measure gas exchanges and carbon dynamics, contributing to understanding these ecosystems' role in carbon sequestration. Several correction techniques were used to verify the ability of machine learning models to improve measurements, Fig. 1 shows the Taylor Diagram of the comparison between AI models. The calibration was done for the sensors at 30 meters on the main tower of SMEAR Estonia, which were installed next to the pipe that sucks air for analysis in the reference equipment. The CO2 concentration in ppm, temperature and relative humidity (3 variables measured by the SCD41 sensor) were used as input data and the objective was to train the algorithms to estimate the CO2 concentration in ppm measured by the Los Gatos equipment at 30 meters high. The analysis indicates that for this case the Random Forest (RF) and XGBoost non-linear regression models performed better than the multiple linear regression (MLR) model. Fig. 2 shows the time series of CO2 reference concentration and SCD41 sensor corrections using Multiple Linear Regression (MLR) and Random Forest (RF). With an interdisciplinary focus, the proposal collaborates with national and international institutions, such as SMEAR Estonia, and promotes human resource training, the generation of long-term data, and the publication of scientific results. The project also aims to influence public policies and democratize environmental monitoring, strengthening awareness of air quality and climate change.