Advancements in Radiomics and Artificial Intelligence for Thyroid Cancer Diagnosis

Thyroid cancer is an increasing global health concern that requires advanced diagnostic methods. The application of AI and radiomics to thyroid cancer diagnosis is examined in this review. A review of multiple databases was conducted in compliance with PRISMA guidelines until October 2023. A combination of keywords led to the discovery of an English academic publication on thyroid cancer and related subjects. 267 papers were returned from the original search after 109 duplicates were removed. Relevant studies were selected according to predetermined criteria after 124 articles were eliminated based on an examination of their abstract and title. After the comprehensive analysis, an additional six studies were excluded. Among the 28 included studies, radiomics analysis, which incorporates ultrasound (US) images, demonstrated its effectiveness in diagnosing thyroid cancer. Various results were noted, some of the studies presenting new strategies that outperformed the status quo. The literature has emphasized various challenges faced by AI models, including interpretability issues, dataset constraints, and operator dependence. The synthesized findings of the 28 included studies mentioned the need for standardization efforts and prospective multicenter studies to address these concerns. Furthermore, approaches to overcome these obstacles were identified, such as advances in explainable AI technology and personalized medicine techniques. The review focuses on how AI and radiomics could transform the diagnosis and treatment of thyroid cancer. Despite challenges, future research on multidisciplinary cooperation, clinical applicability validation, and algorithm improvement holds the potential to improve patient outcomes and diagnostic precision in the treatment of thyroid cancer.

Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina

Diabetes mellitus (DM) predisposes patients to vascular complications. Retinal images and vasculature reflect the body's micro- and macrovascular health. They can be used to diagnose DM complications, including diabetic retinopathy (DR), neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as forecast the risk of cardiovascular events. Artificial intelligence (AI)-enabled systems developed for high-throughput detection of DR using digitized retinal images have become clinically adopted. Beyond DR screening, AI integration also holds immense potential to address challenges associated with the holistic care of the patient with DM. In this work, we aim to comprehensively review the literature for studies on AI applications based on retinal images related to DM diagnosis, prognostication, and management. We will describe the findings of holistic AI-assisted diabetes care, including but not limited to DR screening, and discuss barriers to implementing such systems, including issues concerning ethics, data privacy, equitable access, and explainability. With the ability to evaluate the patient's health status vis a vis DM complication as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM.

Current and future roles of artificial intelligence in retinopathy of prematurity

Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. While semi-automated systems have been used in the past to diagnose ROP-related plus disease by quantifying retinal vessel features, traditional machine learning (ML) models face challenges like accuracy and overfitting. Recent advancements in deep learning (DL), especially convolutional neural networks (CNNs), have significantly improved ROP detection and classification. The i-ROP deep learning (i-ROP-DL) system also shows promise in detecting plus disease, offering reliable ROP diagnosis potential. This research comprehensively examines the contemporary progress and challenges associated with using retinal imaging and artificial intelligence (AI) to detect ROP, offering valuable insights that can guide further investigation in this domain. Based on 89 original studies in this field (out of 1487 studies that were comprehensively reviewed), we concluded that traditional methods for ROP diagnosis suffer from subjectivity and manual analysis, leading to inconsistent clinical decisions. AI holds great promise for improving ROP management. This review explores AI's potential in ROP detection, classification, diagnosis, and prognosis.

Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis

Mosquito-borne diseases present considerable risks to the health of both animals and humans. Aedes aegypti mosquitoes are the primary vectors for numerous medically important viruses such as dengue, Zika, yellow fever, and chikungunya. To characterize this mosquito neural activity, it is essential to classify the generated electrical spikes. However, no open-source neural spike classification method is currently available for mosquitoes. Our work presented in this paper provides an innovative artificial intelligence-based method to classify the neural spikes in uninfected, dengue-infected, and Zika-infected mosquitoes. Aiming for outstanding performance, the method employs a fusion of normalization, feature importance, and dimension reduction for the preprocessing and combines convolutional neural network and extra gradient boosting (XGBoost) for classification. The method uses the electrical spiking activity data of mosquito neurons recorded by microelectrode array technology. We used data from 0, 1, 2, 3, and 7 days post-infection, containing over 15 million samples, to analyze the method's performance. The performance of the proposed method was evaluated using accuracy, precision, recall, and the F1 scores. The results obtained from the method highlight its remarkable performance in differentiating infected vs uninfected mosquito samples, achieving an average of 98.1%. The performance was also compared with 6 other machine learning algorithms to further assess the method's capability. The method outperformed all other machine learning algorithms' performance. Overall, this research serves as an efficient method to classify the neural spikes of Aedes aegypti mosquitoes and can assist in unraveling the complex interactions between pathogens and mosquitoes.

Designing Interpretable ML System to Enhance Trustworthy AI in Healthcare: A Systematic Review of the Last Decade to A Proposed Robust Framework

AI-based medical technologies, including wearables, telemedicine, LLMs, and digital care twins, significantly impact healthcare. Ensuring AI results are accurate and interpretable is crucial, especially for clinicians. This paper reviews processes and challenges of interpretable ML (IML) and explainable AI (XAI) in healthcare. Objectives include reviewing XAI processes, methods, applications, and challenges, with a focus on quality control. The IML process is classified into data pre-processing interpretability, interpretable modeling, and post-processing interpretability. The paper aims to establish the importance of robust interpretability in healthcare through experimental results, providing insights for creating communicable clinician-AI tools. Research questions, eligibility criteria, and goals were identified following PRISMA and PICO methods. PubMed, Scopus, and Web of Science were systematically searched using specific strings. The survey introduces a step-by-step roadmap for implementing XAI in clinical applications, addressing existing gaps and acknowledging XAI model limitations.

An Investigation of Hepatitis B Virus Genome using Markov Models

The human genome encodes a family of editing enzymes known as APOBEC3 (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3). Several family members, such as APO-BEC3G, APOBEC3F, and APOBEC3H haplotype II, exhibit activity against viruses such as HIV. These enzymes induce C-to-U mutations in the negative strand of viral genomes, resulting in multiple G-to-A changes, commonly referred to as 'hypermutation.' Mutations catalyzed by these enzymes are sequence context-dependent in the HIV genome; for instance, APOBEC3G preferen-tially mutates G within GG, TGG, and TGGG contexts, while other members mutate G within GA, TGA, and TGAA contexts. However, the same sequence context has not been explored in relation to these enzymes and HBV. In this study, our objective is to identify the mutational footprint of APOBEC3 enzymes in the HBV genome. To achieve this, we employ a multivariable data analytics technique to investigate motif preferences and potential sequence hierarchies of mutation by APOBEC3 enzymes using full genome HBV sequences from a diverse range of naturally infected patients. This approach allows us to distinguish between normal and hypermutated sequences based on the representation of mono- to tetra-nucleotide motifs. Additionally, we aim to identify motifs associated with hypermutation induced by different APOBEC3 enzymes in HBV genomes. Our analyses reveal that either APOBEC3 enzymes are not active against HBV, or the induction of G-to-A mutations by these enzymes is not sequence context-dependent in the HBV genome.

Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade

Background: Cardiovascular diseases (CVDs) continue to be the leading cause of mortality on a global scale. In recent years, the application of artificial intelligence (AI) techniques, particularly deep learning (DL), has gained considerable popularity for evaluating the various aspects of CVDs. Moreover, using fundus images and optical coherence tomography angiography (OCTA) to diagnose retinal diseases has been extensively studied. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with non-invasive retinal scanning. Leveraging AI-assisted early detection and prediction of cardiovascular diseases on a large scale holds excellent potential to mitigate cardiovascular events and alleviate the economic burden on healthcare systems. Method: A comprehensive search was conducted across various databases, including PubMed, Medline, Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related to cardiovascular diseases and artificial intelligence. Results: A total of 87 English-language publications, selected for relevance were included in the study, and additional references were considered. This study presents an overview of the current advancements and challenges in employing retinal imaging and artificial intelligence to identify cardiovascular disorders and provides insights for further exploration in this field. Conclusion: Researchers aim to develop precise disease prognosis patterns as the aging population and global CVD burden increase. AI and deep learning are transforming healthcare, offering the potential for single retinal image-based diagnosis of various CVDs, albeit with the need for accelerated adoption in healthcare systems.

Machine Learning Meets Advanced Robotic Manipulation

Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources. Robotic manipulator arms have major role in the automation process. However, for complex manipulation tasks, hard coding efficient and safe trajectories is challenging and time consuming. Machine learning methods have the potential to learn such controllers based on expert demonstrations. Despite promising advances, better approaches must be developed to improve safety, reliability, and efficiency of ML methods in both training and deployment phases. This survey aims to review cutting edge technologies and recent trends on ML methods applied to real-world manipulation tasks. After reviewing the related background on ML, the rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue. The paper is closed with important research directions for future works.

Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey

The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. The aim of this review article is to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.

AI Framework for Early Diagnosis of Coronary Artery Disease: An Integration of Borderline SMOTE, Autoencoders and Convolutional Neural Networks Approach

The accuracy of coronary artery disease (CAD) diagnosis is dependent on a variety of factors, including demographic, symptom, and medical examination, ECG, and echocardiography data, among others. In this context, artificial intelligence (AI) can help clinicians identify high-risk patients early in the diagnostic process, by synthesizing information from multiple factors. To this aim, Machine Learning algorithms are used to classify patients based on their CAD disease risk. In this study, we contribute to this research filed by developing a methodology for balancing and augmenting data for more accurate prediction when the data is imbalanced and the sample size is small. The methodology can be used in a variety of other situations, particularly when data collection is expensive and the sample size is small. The experimental results revealed that the average accuracy of our proposed method for CAD prediction was 95.36, and was higher than random forest (RF), decision tree (DT), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN).