Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Liver fibrosis analysis using digital pathology.

Digital pathology has enabled the noninvasive quantification of pathological parameters. In addition, the combination of digital pathology and artificial intelligence has enabled the analysis of a vast amount of information, leading to the sharing of much information and the elimination of knowledge gaps. Fibrosis, which reflects chronic inflammation, is the most important pathological parameter in chronic liver diseases, such as viral hepatitis and metabolic dysfunction-associated steatotic liver disease. It has been reported that the quantitative evaluation of various fibrotic parameters by digital pathology can predict the prognosis of liver disease and hepatocarcinogenesis. Liver fibrosis evaluation methods include 1 fiber quantification, 2 elastin and collagen quantification, 3 s harmonic generation/two photon excitation fluorescence (SHG/TPE) microscopy, and 4 Fibronest™..　In this review, we provide an overview of role of digital pathology on the evaluation of fibrosis in liver disease and the characteristics of recent methods to assess liver fibrosis.

Evolutionary Strategies Enable Systematic and Reliable Uncertainty Quantification: A Proof-of-Concept Pilot Study on Resting-State Functional MRI Language Lateralization.

Reliable and trustworthy artificial intelligence (AI), particularly in high-stake medical diagnoses, necessitates effective uncertainty quantification (UQ). Existing UQ methods using model ensembles often introduce invalid variability or computational complexity, rendering them impractical and ineffective in clinical workflow. We propose a UQ approach based on deep neuroevolution (DNE), a data-efficient optimization strategy. Our goal is to replicate trends observed in expert-based UQ. We focused on language lateralization maps from resting-state functional MRI (rs-fMRI). Fifty rs-fMRI maps were divided into training/testing (30:20) sets, representing two labels: "left-dominant" and "co-dominant." DNE facilitated acquiring an ensemble of 100 models with high training and testing set accuracy. Model uncertainty was derived from distribution entropies over the 100 model predictions. Expert reviewers provided user-based uncertainties for comparison. Model (epistemic) and user-based (aleatoric) uncertainties were consistent in the independently and identically distributed (IID) testing set, mainly indicating low uncertainty. In a mostly out-of-distribution (OOD) holdout set, both model and user-based entropies correlated but displayed a bimodal distribution, with one peak representing low and another high uncertainty. We also found a statistically significant positive correlation between epistemic and aleatoric uncertainties. DNE-based UQ effectively mirrored user-based uncertainties, particularly highlighting increased uncertainty in OOD images. We conclude that DNE-based UQ correlates with expert assessments, making it reliable for our use case and potentially for other radiology applications.

Deep Learning-Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study.

Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs were retrospectively retrieved from two hospitals between 2015 and 2020. All CXRs were annotated with pixel-level labels for NGT localization and image-level labels for NGT presence and malposition. In the CAD system, DeepLabv3 + with backbone ResNeSt50 and DenseNet121 served as the model architecture for segmentation and classification models, respectively. The CAD system was tested on images from chronologically different datasets (National Taiwan University Hospital (National Taiwan University Hospital)-20), geographically different datasets (National Taiwan University Hospital-Yunlin Branch (YB)), and the public CLiP dataset. For the segmentation model, the Dice coefficients indicated accurate delineation of the NGT course (National Taiwan University Hospital-20: 0.665, 95% confidence interval (CI) 0.630-0.696; National Taiwan University Hospital-Yunlin Branch: 0.646, 95% CI 0.614-0.678). The distance between the predicted and ground-truth NGT tips suggested accurate tip localization (National Taiwan University Hospital-20: 1.64 cm, 95% CI 0.99-2.41; National Taiwan University Hospital-Yunlin Branch: 2.83 cm, 95% CI 1.94-3.76). For the classification model, NGT presence was detected with high accuracy (area under the receiver operating characteristic curve (AUC): National Taiwan University Hospital-20: 0.998, 95% CI 0.995-1.000; National Taiwan University Hospital-Yunlin Branch: 0.998, 95% CI 0.995-1.000; CLiP dataset: 0.991, 95% CI 0.990-0.992). The CAD system also detected NGT malposition with high accuracy (AUC: National Taiwan University Hospital-20: 0.964, 95% CI 0.917-1.000; National Taiwan University Hospital-Yunlin Branch: 0.991, 95% CI 0.970-1.000) and detected abnormal nasoenteric tube positions with favorable performance (AUC: 0.839, 95% CI 0.807-0.869). The CAD system accurately localized NGTs and detected NGT malposition, demonstrating excellent potential for external generalizability.

Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline.

De-identification of medical images intended for research is a core requirement for data sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the United States National Cancer Institute (NCI) convened a two half-day virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the second day of the workshop, the recordings and presentations of which are publicly available for review. The topics covered included pathology whole slide image de-identification, de-facing, the role of AI in image de-identification, and the NCI Medical Image De-Identification Initiative (MIDI) datasets and pipeline.

Application of Artificial Intelligence in the Headache Field.

PURPOSE OF REVIEW: Headache disorders are highly prevalent worldwide. Rapidly advancing capabilities in artificial intelligence (AI) have expanded headache-related research with the potential to solve unmet needs in the headache field. We provide an overview of AI in headache research in this article. RECENT FINDINGS: We briefly introduce machine learning models and commonly used evaluation metrics. We then review studies that have utilized AI in the field to advance diagnostic accuracy and classification, predict treatment responses, gather insights from various data sources, and forecast migraine attacks. Furthermore, given the emergence of ChatGPT, a type of large language model (LLM), and the popularity it has gained, we also discuss how LLMs could be used to advance the field. Finally, we discuss the potential pitfalls, bias, and future directions of employing AI in headache medicine. Many recent studies on headache medicine incorporated machine learning, generative AI and LLMs. A comprehensive understanding of potential pitfalls and biases is crucial to using these novel techniques with minimum harm. When used appropriately, AI has the potential to revolutionize headache medicine.

Mapping of specialized metabolite terms onto a plant phylogeny using text mining and large language models.

Plants produce a staggering array of chemicals that are the basis for organismal function and important human nutrients and medicines. However, it is poorly defined how these compounds evolved and are distributed across the plant kingdom, hindering a systematic view and understanding of plant chemical diversity. Recent advances in plant genome/transcriptome sequencing have provided a well-defined molecular phylogeny of plants, on which the presence of diverse natural products can be mapped to systematically determine their phylogenetic distribution. Here, we built a proof-of-concept workflow where previously reported diverse tyrosine-derived plant natural products were mapped onto the plant tree of life. Plant chemical-species associations were mined from literature, filtered, evaluated through manual inspection of over 2500 scientific articles, and mapped onto the plant phylogeny. The resulting "phylochemical" map confirmed several highly lineage-specific compound class distributions, such as betalain pigments and Amaryllidaceae alkaloids. The map also highlighted several lineages enriched in dopamine-derived compounds, including the orders Caryophyllales, Liliales, and Fabales. Additionally, the application of large language models, using our manually curated data as a ground truth set, showed that post-mining processing can largely be automated with a low false-positive rate, critical for generating a reliable phylochemical map. Although a high false-negative rate remains a challenge, our study demonstrates that combining text mining with language model-based processing can generate broader phylochemical maps, which will serve as a valuable community resource to uncover key evolutionary events that underlie plant chemical diversity and enable system-level views of nature's millions of years of chemical experimentation.

Current advances in the use of artificial intelligence in predicting and managing urological complications.

BACKGROUND: Artificial intelligence (AI) has emerged as a promising avenue for improving patient care and surgical outcomes in urological surgery. However, the extent of AI's impact in predicting and managing complications is not fully elucidated. OBJECTIVES: We review the application of AI to foresee and manage complications in urological surgery, assess its efficacy, and discuss challenges to its use. METHODS AND MATERIALS: A targeted non-systematic literature search was conducted using the PubMed and Google Scholar databases to identify studies on AI in urological surgery and its complications. Evidence from the studies was synthesised. RESULTS: Incorporating AI into various facets of urological surgery has shown promising advancements. From preoperative planning to intraoperative guidance, AI is revolutionising the field, demonstrating remarkable proficiency in tasks such as image analysis, decision-making support, and complication prediction. Studies show that AI programmes are highly accurate, increase surgical precision and efficiency, and reduce complications. However, implementation challenges exist in AI errors, human errors, and ethical issues. CONCLUSION: AI has great potential in predicting and managing surgical complications of urological surgery. Advancements have been made, but challenges and ethical considerations must be addressed before widespread AI implementation.

Predicting blood-brain barrier permeability of molecules with a large language model and machine learning.

Predicting the blood-brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood-brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores < 1.29423E-05, which designate compounds that pass through the human BBB) can pass through the spheroid cells of the BBB. Our validation of in vitro experiments indicated that the in silico prediction of small-molecule permeation in the BBB model is accurate. Transformer-based models like MegaMolBART, leveraging the SMILES representations of molecules, show great promise for applications in new drug discovery. These models have the potential to accelerate the development of novel targeted treatments for disorders of the central nervous system.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students.

BACKGROUND: To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students. METHODS: The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the 'AI intelligent assisted diagnosis system' teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5-10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared. RESULTS: There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1-4 and 5-7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively. CONCLUSION: The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

Vaccine design and development: Exploring the interface with computational biology and AI.

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

The application of vaccines is one of the most promising treatments for numerous infectious diseases. However, the design and development of effective vaccines involve huge investments and resources, and only a handful of candidates successfully reach the market. Only relying on traditional methods is both time-consuming and expensive. Various computational tools and software have been developed to accelerate the vaccine design and development. Further, AI-enabled computational tools have revolutionized the field of vaccine design and development by creating predictive models and data-driven decision-making processes. Therefore, information and awareness of these AI-enabled computational resources will immensely facilitate the development of vaccines against emerging pathogens. In this review, we have meticulously summarized the available computational tools for each step of in-silico vaccine design and development, delving into the transformative applications of AI and ML in this domain, which would help to choose appropriate tools for each step during vaccine development, and also highlighting the limitations of these tools to facilitate the selection of appropriate tools for each step of vaccine design.

Application of multimodal ultrasonography to predicting the acute kidney injury risk of patients with sepsis: artificial intelligence approach.

The occurrence of acute kidney injury in sepsis represents a common complication in hospitalized and critically injured patients, which is usually associated with an inauspicious prognosis. Thus, additional consequences, for instance, the risk of developing chronic kidney disease, can be coupled with significantly higher mortality. To intervene in advance in high-risk patients, improve poor prognosis, and further enhance the success rate of resuscitation, a diagnostic grading standard of acute kidney injury is employed to quantify. In the article, an artificial intelligence-based multimodal ultrasound imaging technique is conceived by incorporating conventional ultrasound, ultrasonography, and shear wave elastography examination approaches. The acquired focal lesion images in the kidney lumen are mapped into a knowledge map and then injected into feature mining of a multicenter clinical dataset to accomplish risk prediction for the occurrence of acute kidney injury. The clinical decision curve demonstrated that applying the constructed model can help patients whose threshold values range between 0.017 and 0.89 probabilities. Additionally, the metrics of model sensitivity, specificity, accuracy, and area under the curve (AUC) are computed as 67.9%, 82.48%, 76.86%, and 0.692%, respectively, which confirms that multimodal ultrasonography not only improves the diagnostic sensitivity of the constructed model but also dramatically raises the risk prediction capability, thus illustrating that the predictive model possesses promising validity and accuracy metrics.

CyberEduPlatform: an educational tool to improve cybersecurity through anomaly detection with Artificial Intelligence.

Cybersecurity has become a central concern in the contemporary digital era due to the exponential increase in cyber threats. These threats, ranging from simple malware to advanced persistent attacks, put individuals and organizations at risk. This study explores the potential of artificial intelligence to detect anomalies in network traffic in a university environment. The effectiveness of automatic detection of unconventional activities was evaluated through extensive simulations and advanced artificial intelligence models. In addition, the importance of cybersecurity awareness and education is highlighted, introducing CyberEduPlatform, a tool designed to improve users' cyber awareness. The results indicate that, while AI models show high precision in detecting anomalies, complementary education and awareness play a crucial role in fortifying the first lines of defense against cyber threats. This research highlights the need for an integrated approach to cybersecurity, combining advanced technological solutions with robust educational strategies.

FLMatchQA: a recursive neural network-based question answering with customized federated learning model.

More sophisticated data access is possible with artificial intelligence (AI) techniques such as question answering (QA), but regulations and privacy concerns have limited their use. Federated learning (FL) deals with these problems, and QA is a viable substitute for AI. The utilization of hierarchical FL systems is examined in this research, along with an ideal method for developing client-specific adapters. The User Modified Hierarchical Federated Learning Model (UMHFLM) selects local models for users' tasks. The article suggests employing recurrent neural network (RNN) as a neural network (NN) technique for learning automatically and categorizing questions based on natural language into the appropriate templates. Together, local and global models are developed, with the worldwide model influencing local models, which are, in turn, combined for personalization. The method is applied in natural language processing pipelines for phrase matching employing template exact match, segmentation, and answer type detection. The (SQuAD-2.0), a DL-based QA method for acquiring knowledge of complicated SPARQL test questions and their accompanying SPARQL queries across the DBpedia dataset, was used to train and assess the model. The SQuAD2.0 datasets evaluate the model, which identifies 38 distinct templates. Considering the top two most likely templates, the RNN model achieves template classification accuracy of 92.8% and 61.8% on the SQuAD2.0 and QALD-7 datasets. A study on data scarcity among participants found that FL Match outperformed BERT significantly. A MAP margin of 2.60% exists between BERT and FL Match at a 100% data ratio and an MRR margin of 7.23% at a 20% data ratio.

A wave delay neural network for solving label-constrained shortest route query on time-varying communication networks.

The focus of the research is on the label-constrained time-varying shortest route query problem on time-varying communication networks. To the best of our knowledge, research on this issue is still relatively limited, and similar studies have the drawbacks of low solution accuracy and slow computational speed. In this study, a wave delay neural network (WDNN) framework and corresponding algorithms is proposed to effectively solve the label-constrained time-varying shortest routing query problem. This framework accurately simulates the time-varying characteristics of the network without any training requirements. WDNN adopts a new type of wave neuron, which is independently designed and all neurons are parallelly computed on WDNN. This algorithm determines the shortest route based on the waves received by the destination neuron (node). Furthermore, the time complexity and correctness of the proposed algorithm were analyzed in detail in this study, and the performance of the algorithm was analyzed in depth by comparing it with existing algorithms on randomly generated and real networks. The research results indicate that the proposed algorithm outperforms current existing algorithms in terms of response speed and computational accuracy.

Automated sentiment analysis of visually impaired students' audio feedback in virtual learning environments.

This research introduces an innovative intelligent model developed for predicting and analyzing sentiment responses regarding audio feedback from students with visual impairments in a virtual learning environment. Sentiment is divided into five types: high positive, positive, neutral, negative, and high negative. The model sources data from post-COVID-19 outbreak educational platforms (Microsoft Teams) and offers automated evaluation and visualization of audio feedback, which enhances students' performances. It also offers better insight into the sentiment scenarios of e-learning visually impaired students to educators. The sentiment responses from the assessment to point out deficiencies in computer literacy and forecast performance were pretty successful with the support vector machine (SVM) and artificial neural network (ANN) algorithms. The model performed well in predicting student performance using ANN algorithms on structured and unstructured data, especially by the 9th week against unstructured data only. In general, the research findings provide an inclusive policy implication that ought to be followed to provide education to students with a visual impairment and the role of technology in enhancing the learning experience for these students.

Analyzing the European institutional response to ethical and regulatory challenges of artificial intelligence in addressing discriminatory bias.

The European Union and some of its institutions have taken significant steps to address the challenges posed by the development and use of Artificial Intelligence (AI) in various contexts. The ubiquity of AI applications in everyday life, affecting both citizens and professionals, has made AI a common topic of discussion. However, as is evident from the documents analyzed here, concerns have been raised about the possible negative social consequences of AI, in particular discriminatory bias, making it a particularly relevant issue if people-centred, rights-based AI is to be implemented. This article aims to examine the challenges of defining, identifying and mitigating discriminatory bias in AI systems from two perspectives: (1) to conduct an ethical and normative review of European Commission documents from the last 8 years (from GDPR to AI Act regulation); and (2) to expose recommendations for key stakeholders, including designers, end-users and public authorities, to minimize/mitigate this risk. The document review was carried out on 21 EU regulatory and ethical guidelines in the field of AI, from which 152 measures were extracted, differentiated between design, governance and organizational measures. It has also been observed that there is no clear conceptual framework on the issue at the European level, showing a clear problem in providing definitions of algorithmic bias and discrimination, but not in assessing their potential negative impact on individuals. Secondly, these gaps may affect the concreteness and detail of the possible mitigation/minimization measures proposed and, subsequently, their application in different contexts. Finally, the last section of this paper presents a brief discussion and conclusions on possible issues related to the implementation of the measures extracted and certain limitations of the study.

Unveiling significant risk factors for intensive care unit-acquired weakness: Advancing preventive care.

In this editorial, we discuss an article titled, "Significant risk factors for intensive care unit-acquired weakness: A processing strategy based on repeated machine learning," published in a recent issue of the World Journal of Clinical Cases. Intensive care unit-acquired weakness (ICU-AW) is a debilitating condition that affects critically ill patients, with significant implications for patient outcomes and their quality of life. This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors. Data from a cohort of 1063 adult intensive care unit (ICU) patients were analyzed, with a particular emphasis on variables such as duration of ICU stay, duration of mechanical ventilation, doses of sedatives and vasopressors, and underlying comorbidities. A multilayer perceptron neural network model was developed, which exhibited a remarkable impressive prediction accuracy of 86.2% on the training set and 85.5% on the test set. The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.