Highly reliable personalized noninvasive hemoglobin estimation by using Vision Transformers and dual fine-tuning.

Owing to the complex and often asymptomatic presentations, the diagnosis of biliopancreatic diseases, including pancreatic and biliary malignancies, remains challenging. Recent technological advancements have remarkably improved the diagnostic accuracy and patient outcomes in these diseases. This review explores key advancements in diagnostic modalities, including biomarkers, imaging techniques, and artificial intelligence (AI)-based technologies. Biomarkers, such as cancer antigen 19-9, KRAS mutations, and inflammatory markers, provide crucial insights into disease progression and treatment responses. Advanced imaging modalities include enhanced computed tomography (CT), positron emission tomography-CT, magnetic resonance cholangiopancreatography, and endoscopic ultrasound. AI integration in imaging and pathology has enhanced diagnostic precision through deep learning algorithms that analyze medical images, automate routine diagnostic tasks, and provide predictive analytics for personalized treatment strategies. The applications of these technologies are diverse, ranging from early cancer detection to therapeutic guidance and real-time imaging. Biomarker-based liquid biopsies and AI-assisted imaging tools are essential for non-invasive diagnostics and individualized patient management. Furthermore, AI-driven models are transforming disease stratification, thus enhancing risk assessment and decision-making. Future studies should explore standardizing biomarker validation, improving AI-driven diagnostics, and expanding the accessibility of advanced imaging technologies in resource-limited settings. The continued development of non-invasive diagnostic techniques and precision medicine approaches is crucial for optimizing the detection and management of biliopancreatic diseases. Collaborative efforts between clinicians, researchers, and industry stakeholders will be pivotal in applying these advancements in clinical practice.

This article devises the Artificial Intelligence (AI) methods of designing models of short-term forecasting (in 12 h and 24 h horizons) of electricity production in a selected Small Hydropower Plant (SHP). Renewable Energy Sources (RESs) are difficult to predict due to weather variability. Electricity production by a run-of-river SHP is marked by the variability related to the access to instantaneous flow in the river and weather conditions. In order to develop predictive models of an SHP facility (installed capacity 760 kW), which is located in Southern Poland on the Skawa River, hourly data from nearby meteorological stations and a water gauge station were collected as explanatory variables. Data on the water management of the retention reservoir above the SHP were also included. The variable to be explained was the hourly electricity production, which was obtained from the tested SHP over a period of 3 years and 10 months. Obtaining these data to build models required contact with state institutions and private entrepreneurs of the SHP. Four AI methods were chosen to create predictive models: two types of Artificial Neural Networks (ANNs), Multilayer Perceptron (MLP) and Radial Base Functions (RBFs), and two types of decision trees methods, Random Forest (RF) and Gradient-Boosted Decision Trees (GBDTs). Finally, after applying forecast quality measures of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2), the most effective model was indicated. The decision trees method proved to be more accurate than ANN models. The best GBDT models’ errors were MAPE 3.17% and MAE 9.97 kWh (for 12 h horizon), and MAPE 3.41% and MAE 10.96 kWh (for 24 h horizon). MLPs had worse results: MAPE from 5.41% to 5.55% and MAE from 18.02 kWh to 18.40 kWh (for 12 h horizon), and MAPE from 7.30% to 7.50% and MAE from 24.12 kWh to 24.83 kWh (for 24 h horizon). Forecasts using RBF were not made due to the very low quality of training and testing (the correlation coefficient was approximately 0.3).

Is precision medicine the future of healthcare?

This article aims to present how the advanced solutions of artificial intelligence and precision medicine work together to refine medical management. Multi-omics seems the most suitable approach for biological analysis of data on precision medicine and artificial intelligence. We searched PubMed and Google Scholar databases to collect pertinent articles appearing up to 5 March 2021. Genetics, oncology, radiology, and the recent coronavirus disease (COVID-19) pandemic were chosen as representative fields addressing the cross-compliance of artificial intelligence (AI) and precision medicine based on the highest number of articles, topicality, and interconnectedness of the issue. Overall, we identified and perused 1572 articles. AI is a breakthrough that takes part in shaping the Fourth Industrial Revolution in medicine and health care, changing the long-time accepted diagnostic and treatment regimens and approaches. AI-based link prediction models may be outstandingly helpful in the literature search for drug repurposing or finding new therapeutical modalities in rapidly erupting wide-scale diseases such as the recent COVID-19.

Artificial intelligence: its applications in reproductive medicine and the assisted reproductive technologies

Marvels of Artificial and Computational Intelligence in Life Sciences is a primer for scholars and students who are interested in the applications of artificial intelligence (AI) and computational intelligence (CI) in life sciences and other industries. The book consists of 16 chapters (9 of which focus on AI and 7 of which showcase the benefits of CI approaches to solve specific problems). Chapters are edited by subject experts who describe the roles and applications of AI and CI in different parts of our lives in a concise and lucid manner. The book covers the following key themes: AI Revolution in Healthcare and Drug Discovery: AI's Impact on Biology and Energy Management AI and CI in Physical Sciences and Predictive Modeling Computational Biology The editors have compiled a good blend of topics in applied science and engineering to give readers a clear understanding of the multidisciplinary nature of the two facets of computing. Each chapter includes references for advanced readers.

Getting AI Right: Introductory Notes on AI & Society

Long-term load forecasting based on adaptive neural fuzzy inference system using real energy data

The healthcare industry is in a state of profound transformation due to rapid integration of digital technologies. The digital revolution is causing a paradigm shift in the healthcare industry, encouraging innovation, and eventually improving patient outcomes. One of the main drivers of this development has been the introduction of electronic health records, or EHRs, which allow for easy data interchange and interoperability across healthcare providers. Improved decision-making, customised treatment plans, and enhanced collaboration across multidisciplinary healthcare teams are all made possible by real-time access to patient data. Furthermore, new directions in predictive analytics, early disease detection, and precision medicine have been made possible by the incorporation of artificial intelligence (AI) and machine learning (ML) algorithms into healthcare processes. Expanding healthcare accessible through telemedicine has shown to be a potent tool, especially in underserved or remote places. The digital revolution has made it easier to obtain healthcare and improve patient participation by enabling telehealth platforms, virtual consultations, and remote monitoring. Reactive healthcare is being replaced with proactive healthcare through the use of Wearable technology, sensors, and mobile applications that enable people to take an active role in their own health management. Personalised medicine and genomics have advanced as a result of the digital revolution. Using complex algorithms in conjunction with genetic data analysis, genetic predispositions can be identified, allowing for customised treatment regimens depending on each patient's distinct genetic composition. Precision medicine is a paradigm shift that has the potential to maximise positive outcomes while reducing negative ones. The healthcare industry's digital transformation raises ethical, legal, and societal issues that need to be carefully considered as it develops. To fully use digital technology and maintain patient safety, data security, and fair access to healthcare services, it is imperative to strike a balance between innovation and ethical issues. In summary, the digital revolution in healthcare is a disruptive force that has enormous potential to enhance accessibility, improve patient outcomes, and optimise the delivery of healthcare. To fully realise the promise of this paradigm change in healthcare, stakeholders must work together to Overcome obstacles as the integration of digital technology spreads.

Objective: Timely monitoring right ventricular systolic blood pressure (RVSBP) is helpful in the early detection of pulmonary hypertension (PH). However, it is not easy to monitor RVSBP directly. The objective of this paper is to develop a deep learning technique for RVSBP noninvasive estimation using heart sound (HS) signals supported by (electrocardiography) ECG signals without complex features extraction. Methods: Five beagle dog subjects were used. The medicine U-44069 was injected into the subjects to induce a wide range of RVSBP variation. The blood pressure in right ventricle, ECG of lead I and HS signals were recorded simultaneously. Thirty-two records were collected. The relations between RVSBP and cyclic HS signals were modeled by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. Results: The mean absolute error (MAE) ± standard deviation (SD) inside record was 1.85 ± 1.82 mmHg. It was 4.37 ± 2.49 mmHg across record but within subject. The corrective factors were added after training the Bi-LSTM network across subjects. Finally, the MAE ± SD from 12.46 ± 6.56 mmHg dropped to 6.37 ± 4.90 mmHg across subjects. Significance: Our work was the first to apply the Bi-LSTM network to build relations between the HS signal and RVSBP. This work suggested a noninvasive and continuous RVSBP estimation using the HS signal supported by the ECG signal by deep learning architecture without the need of healthcare professionals.

Mapping of groundwater salinization and modelling using meta-heuristic algorithms for the coastal aquifer of eastern Saudi Arabia

Stabilized oily-wastewater separation based on superhydrophilic and underwater superoleophobic ceramic membranes: Integrated experimental design and standalone machine learning algorithms

Precision Medicine is one of the most impactful global opportunities in healthcare. We can debate the name, whether it is precision, stratified or personal but at the highest strategic level, we all share a common goal to enable a world where healthcare is informed by each person unique clinical, molecular and lifestyle information. Precision Medicine will spur a revolution in healthcare, bringing the prospects of earlier, pre-symptomatic diagnosis, more effective treatment, significant cost savings for the NHS and all the above combined with better patient outcomes. Moreover, Precision Medicine will reach the global market value of £134 billion by 2025 thus contributing to unprecedented developments in Life Sciences industry, including new jobs creation, and a significant economic growth. The UK is in a prime position to lead on Precision Medicine globally due to the NHS facilitating advanced health data management, excellent genomics and other omics, new imaging modalities and major advances in the artificial intelligence applied to diagnostics. All the above is supported by the triple helix approach, which involves close collaborations among academic researchers, the NHS and industry. The Covid-19 pandemic has made a major contribution to the UK molecular diagnostic capability. This legacy includes very high throughput laboratories, which should become key building blocks for the UK cutting edge Precision Medicine strategy for the next decade and beyond.

Развит подход к синтезу специальной рекуррентной нейронной сети для задачи прогнозирования полосы когерентности трансионосферных радиоканалов. Создан алгоритм подготовки данных и обучения нейронной сети. Валидация разработанной нейросети выполнена на данных натурных экспериментов, проведённых с помощью пассивных радиосенсоров глобальных навигационных спутниковых систем при использовании фазовых и кодовых измерений. Экспериментально показана достигаемая высокая точность прогноза. Introduction. Artificial intelligence (AI) stands as a pivotal technology in satellite communications and radio navigation, contributing significantly to enhancing user experiences and optimizing communication stability. The urgent need for reliable coherence band prediction methods to ensure stable radiocommunication over transionospheric radio channels underscores the importance of developing more accurate and faster approaches using artificial intelligence. The aim of the work is to develop methods for analyzing and predicting the dynamics of coherence bands of wideband transionospheric radio channels using artificial neural networks. Data preparation and neural network training algorithm. The processing of data and the training of neural network models were conducted in the Python programming language, utilizing the Keras framework and the Tensorflow library. Auxiliary operations were performed using the numpy and pandas packages. Findings. The study scrutinizes the potential of employing existing neural networks in forecasting tasks for radio communication systems, drawing conclusions about the applicability of recurrent neural networks using fully connected and LSTM layers. The development of the method involved intelligent analysis of data from passive radio sensors in the Global Navigation Satellite System (GNSS) network. An algorithm for data preparation and training a neural network model for predicting the coherence band has been devised with the use of Keras Tuner library. This algorithm optimizes hyperparameters of the neural network in a given space, enhancing accuracy during training and validation. Conclusion. The resulting architecture of the neural network, based on hyperparameter selection, includes: an input layer (9,1); 1 LSTM layer with 260 neurons; 2 LSTM layer with 184 neurons; 1 Dense layer with 184 neurons (SeLU); 2 Dense layer with 152 neurons (SeLU); 3 Dense layer with 152 neurons (SeLU); 4 Dense layer with 80 neurons (SeLU); 5 Dense layer with 32 neurons (SeLU); Output Dense layer with 1 neuron; Optimizer: Adam, learning rate = 0.001. With an average Mean Absolute Percentage Error (MAPE) metric of 3.3%, the accuracy of model training and validation was notable. Experimental tests of the predicted coherence band, based on the synthesized neural network structure, resulted in a final accuracy of 18.2 MHz for Mean Absolute Error (MAE), an R2 coefficient of determination of 0.89, and a Mean Absolute Percentage Error (MAPE) of 6%.

Overdiagnosis in cancer care remains a significant concern, often resulting in unnecessary physical, emotional, and financial burdens on patients. Artificial intelligence (AI) has the potential to address this challenge by enabling more accurate, personalized cancer diagnoses and facilitating tailored treatment plans. Integrating AI with precision medicine can minimize unnecessary treatments and associated adverse effects by optimizing care strategies based on individual patient data. However, the integration of AI in oncology requires rigorous research and validation to ensure its effectiveness across diverse populations and clinical settings. Challenges such as algorithmic bias, data representation, and limited access to technology in resource-constrained settings highlight the need for equitable AI applications in health care. Addressing health equity disparities is critical, as diverse and representative training data sets significantly affects the fairness and efficacy of AI systems. AI also holds promise for advancing cancer care in resource-limited settings by providing cost-effective diagnostic tools, democratizing access to advanced health care technologies, and improving outcomes in low- and middle-income nations. Interdisciplinary and international collaborations between researchers, clinicians, and technologists are crucial to maximizing AI's potential in cancer care. By fostering these partnerships and focusing on the development of accessible, ethical, and patient-centered AI applications, the health care community can revolutionize cancer diagnosis and treatment. The growing role of AI in precision medicine brings hope for equitable, cost-effective, and improved patient outcomes worldwide.