The recent discovery of infraslow oscillations (ISOs), rhythmic fluctuations at roughly 0.02 Hz, has been increasingly linked to critical sleep functions, including coordination of neural activity, autonomic functions, and facilitation of glymphatic clearance. Given their relevance to aging and brain health, understanding how ISO properties evolve across the adult lifespan remains an important step toward establishing normative baselines. Here, we aim to develop an atlas of ISO features that are publicly available to the research community that enables downstream research. Overnight polysomnography recordings from 878 participants were selected from Human Sleep Project, which is a clinical sleep dataset. The patient selection was balanced across four age groups and biological sex. We first extracted a timeseries of 1/f corrected sigma-band (11–15 Hz), and then computed its spectrum using multi-taper spectral estimation to extract infraslow fluctuations. Relative ISO band power from 0.005-0.03 Hz, peak frequency, and relative peak power were included in the atlas. The ISO band power increased until early adulthood, remained stable, and then decreased from 60 years old onward. Females exhibited higher ISO band power than males, particularly around 50-60 years old. The ISO peak power had similar patterns with the ISO band power. The ISO peak frequency remained largely stable with a median of 0.011 Hz, with minor shifts at age extremes. The resulting atlas enables multiple future downstream research opportunities, such as medication effect and outcome prediction.

The electroencephalograph (EEG) during sleep carries important information about brain health, which may be altered in children with autism spectrum disorder (ASD) compared to those with typical development (TD). However, it remains unclear at what point during sleep these differences are most pronounced. Using EEG data collected from 62 participants (31 ASD, 31 TD), functional connectivity-measured by imaginary coherence (ImCoh)-was computed between all unique pairs of 25 EEG channels across six frequency bands. ImCoh values were averaged within five-minute windows before and after sleep onset. Logistic regression was then applied to classify ASD versus TD status using these features. We compared five time periods and found that connectivity within the five-minute window following sleep onset achieved the highest classification performance, with an AUROC of 0.73 [0.59-0.84] and an AUPRC of 0.77 [0.64-0.89]. At Youden's optimal threshold, the F1 score was$0.72[0.58-0.84]$, sensitivity was$0.63[0.45-0.79]$, and specificity was$0.80[0.65-0.94]$. These findings suggest that functional connectivity dynamics immediately following sleep onset may more effectively capture neurophysiological signatures of ASD.

Mental health challenges such as depression and anxiety continue to affect a significant portion of the global population. As the understanding of these challenges evolves, there is an increasing need for data-driven methods to identify the factors that contribute to mental health and to strengthen resources for at-risk individuals. This study utilizes a publicly available mental health dataset from Kaggle to investigate predictors of depression in older adults. An XGBoost machine learning model was employed to estimate depression scores based on multiple psychological and behavioral variables. The analysis revealed that productivity score was the most significant predictor of depression in older adults, exhibiting a feature importance score of 0.762, substantially higher than other contributing factors. Visual analyses confirmed a strong negative correlation between productivity levels and depression severity. These findings underscore the pivotal role of productivity in late-life mental health and highlight opportunities for healthcare practitioners to develop targeted interventions aimed at improving well-being and reducing depressive symptoms among older adults.

Multiple sclerosis (MS) is a disease of the central nervous system that causes deterioration of nerves. The purpose of this study is to explore the use of different biomarkers and machine learning methods to predict MS progression, as well as analyzing the data over time instead of simply predicting the current severity of MS in a patient.Two datasets were analyzed in this study: one from Kaggle and one from G-Node Open Data. The Kaggle dataset contained a variety of medical features, whereas the G-Node dataset contained only medical features from motor evoked potential tests. Two machine learning models (scikit-learn Random Forest Regressor and XGBoost Regressor) were developed on the datasets to predict the final Expanded Disability Status Scale (EDSS) score for each patient given various medical and demographic features, as well as their initial EDSS score.Both datasets showed potential for use in prediction of MS disease progression, although the models trained on the Kaggle dataset performed better than the G-Node dataset, suggesting that a variety of biomarkers may be more effective in MS prediction rather than only one biomarker.

Periodontal disease is common among older adults, with around 50% of all adults aged 30 years and older having some form of gum disease. Although the disease might seem minor, without early or proper diagnosis, the disease can affect all supporting structures of the teeth, including gums, periodontal ligament, and alveolar bone. This research can help find patterns common among people with periodontal disease and possibly prevent the effects of it in the elderly. Here, we explored the deep learning capabilities of convolutional neural networks (CNN) and VGG16 models for detecting disease-associated patterns derived from 16S rRNA gene sequences of plaque samples. The CNN model achieved a 90.2% accuracy for differentiating periodontal disease from health samples, with 76% precision and 86% recall. This model could lead to earlier detection and more effective treatment of periodontal disease, ultimately improving patients’ dental health outcomes and quality of life.

Head injuries represent a significant challenge in modern medicine due to their potential for severe long-term consequences such as brain damage, memory loss, and other complications. Prompt and accurate diagnosis is essential for effective treatment; however, many healthcare systems face inefficiencies, resulting in delayed care. This research attempts to develop a robust machine learning (ML) model capable of accurately predicting the presence and type of brain hemorrhage from a CT scan dataset. In addition to detecting the presence of intracranial hemorrhages, the model proposed in this study identifies specific types of hemorrhages: intraventricular, intraparenchymal, subarachnoid, epidural, and subdural. The study’s findings indicate that ML can effectively facilitate a two-step diagnostic process: an initial binary classification to detect the presence of an injury, followed by a multilabel classification to identify the hemorrhage type. Future improvements in model quality are anticipated as more detailed and expansive datasets become publicly accessible.

Machine learning can be utilized to enhance proper police response to Property Crime. However, many current predictive policing strategies unfairly target underprivileged racial demographics by training models with biased data. This is due in part to the racial bias patterns prevalent in policing, which are ultimately replicated by machine learning models. In our study, we focus on the city of Boston and investigate their publicly available Census and Crime Incident datasets to identify the possibility to create less racially biased machine learning models for Property Crime prediction. By utilizing a Multi-Variable Regression model, we propose an alternative to more traditional crime mapping methods. The performance of the model shows a 74.2% correlation to accurately mapping the hotspots of crime. Furthermore, we utilize GIS software to map and visualize geographic property crime rates based on Census Tracts. After running multiple Machine Learning Regression Analyses on our data, we discovered that underprivileged racial demographic variables have an overall low correlation to the rate of Property Crime in the Boston area. Thus, this work is a small step for the possibility of creating machine learning models that both effectively map predicted property crime and are less racially biased against select demographics.

Injuries in professional soccer are a major problem for both players and clubs. Serious injuries have the potential to cause detrimental effects on a player's career, or even end it prematurely. Clubs also suffer when key players are injured; their game tactics may need to be changed to better fit the limited team members available, which can severely affect performance, especially in competitive leagues. The goal of this research is to demonstrate the feasibility of using a large dataset to train an accurate Machine Learning model to predict injuries in professional-level soccer. Machine Learning algorithms used to solve this problem have historically had small datasets, which are prone to variance and unreliable results. Therefore, a larger dataset will be constructed from publicly available data to prove that a reliable injury prediction tool can be created. The data in this study will span multiple years and include data about player minutes, age, appearances, and whether they were recovering from an injury. The results of the study show that an injury prediction tool using Machine Learning is practical for use in professional-level soccer to help teams predict and prevent non-contact injuries sustained by athletes. Further, the quality of these Machine Learning tools will increase as more accurate data collection technology is acquired by teams.

Adolescent idiopathic scoliosis is the most common spinal disorder among adolescents, affecting 1-3% of children aged 10 to 16 years worldwide. The application of deep learning algorithms can directly diagnose scoliosis from spine X-rays scans, reducing unnecessary referrals and labor costs in scoliosis screening. We developed and validated a neural net approach for automated scoliosis screening analyzing back X-ray images. The algorithm has 95% accuracy in the diagnosis of scoliosis and can be used in the initial rapid examination without a professional orthopedic surgeon.

Acute lymphoblastic leukemia (ALL) is one of the most common types of cancer among children. It can rapidly become fatal within weeks, and as such, early diagnosis is critical. Problematically, ALL diagnosis mainly involves manual blood smear analysis relying on the expertise of medical professionals, which is error-prone and time-consuming. Thus, it is important to develop artificial intelligence tools that will identify leukemic cells from a microscopic image faster, more accurately, and cheaper. Here, we investigate the capabilities of a traditional convolutional neural network(CNN) and You Only Look Once (YOLO) models for real-time detection of leukemic cells. The YOLOv5s model shows 97.2% accuracy for the task of object detection of ALL cells, with the inference speed allowing 80 image frames to be processed per second. These new findings can provide valuable insight in applying real-time object detection algorithms for improving the efficiency of blood cancer diagnosis.

As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians.

Diabetic Retinopathy (DR) diagnosis is a time consuming and complex task, in addition to requiring experienced doctors. In this study, an advanced Convolution Neural Network (CNN) and data augmentation were successfully used to predict the stages of diabetic retinopathy. Specifically, we modified and trained this network with the publicly available Kaggle dataset and obtained promising results. On the validation of 1452 images using this classifier, an accuracy of 82%, average sensitivity of 80% over the three categories and average specificity of 91% over the three categories could be achieved, indicating the feasibility of our CCN model for the identification of different stages of the diabetic retinopathy. It implies the great potential of this artificial intelligent method for the diagnosis of diabetic retinopathy, especially for the early diagnosis for patients in the future.

According to the World Health organization, over 300 million people worldwide are affected by major depressive disorder (MDD). Individuals battling this serious mental condition may exhibit symptoms including anxiety, fatigue, and self-harm, all of which severely affect well-being and quality of life. Current trends in social media and population behavior bring up an urgent need for health professionals to strengthen mental health resources, improve access, and accurately diagnose depression. To mitigate the disparate impact of depression on different social and racial groups, this study identifies factors that strongly correlate with the prevalence of depression in the U.S., which focuses on adults using survey data from the 2019 pre-pandemic National Health Institute Survey (NHIS). In this study we trained four machine learning models—including Catboost, KNN, XGBoost—and concluded that a random forest model performed the best classification task on survey data of American adults, capable of an accuracy of 98.7%. Our results conclude that age, education, income, and household demographics are the primary factors impacting mental health. Awareness of these mental health stressors may help medical professionals, institutions, and governments to identify and diagnose more effectively the individuals at risk for MDD using clinical data. By receiving adequate mental health services, Americans can improve their quality of life and form a more fulfilling society.

Alzheimer’s disease (AD) is a chronic, neurodegenerative illness characterized by forgetfulness, cognitive impairment, and the progressive loss of a variety of other brain functions as well as daily life independence. By 2050, the existing 47 million AD sufferers are predicted to grow to 152 million, with major economic, medical, and societal implications. The pathophysiology of Alzheimer’s disease is still unknown, and there is no cure or treatment that can stop the illness from progressing altogether. Alzheimer’s disease must be detected early in order to be effectively managed. An emerging detection methodology includes deep learning approaches that have demonstrated promising results; nonetheless, effective implementations in real-world settings must combine high accuracy, quick processing time, and scalability for a variety of demographics or populations. An AlexNet convolutional neural network (CNN) based classification model using magnetic resonance imaging (MRI) images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). This AlexNet model was then applied to the ADNI dataset to classify four different stages of AD. The model was then tested on test datasets to evaluate the accuracy of the model per stage: 95.5% for early dementia, 95.1% for moderate dementia, 89.3% for no dementia, and 92.7% for late dementia. In the visualization results, the ventricles and outer regions were identified as key regions for classification. These CNN recognition results were further accurately interpreted with the newly developed class activation mapping (CAM), allowing for the interpretability of given diagnoses.

There is literature on Machine Learning Sentiment Analysis (MLSA) during the COVID-19 pandemic, however, to the best of our knowledge, there has been little to no research investigating the effectiveness of different internet media sources for the prediction of population-level sentiment; Twitter is currently the most often used in MLSA research. This study conducts COVID-19 related MLSA on various internet media sources to determine the relative effectiveness in each for mining public sentiment. The natural language processing is achieved through a long short-term memory (LSTM) neural network. By comparing trends of sentiment between social medias Twitter and Reddit, and news source USA Today with that of a control survey by data intelligence company Morning Consult, it is found Twitter has the lowest deviation in trends to that of the control. Assuming the objectivity of the control, Twitter is a better indicator of public sentiment as compared to Reddit and USA Today, capable for future applications of MLSA, especially when used in tandem with pre-existing surveys. This work helps advance research in MLSA with implications in informed decisions on fighting/recovering from COVID-19, flattening future potential pandemic curves, and indicating trends in public psychological and mental health.

Digestive Diseases are a commonality among Americans (almost one out of every five Americans are affected by digestive diseases). The most effective way of identifying such diseases is through Endoscopic Examinations. While patients normally have to wait for a long time to take high quality images from Endoscopic Examinations for diagnosis. This waiting period is problematic because some of these diseases can turn cancerous. This brings in the need for quick and accurate diagnosis of digestive diseases because the ability to identify diseases and the severity of the diseases vary from one doctor to another. This study analyzes the ability for our deep learning models to distinguish between the Z-Line, the Pylorus, the Cecum, Esophagitis, Ulcerative Colitis, Polyps, Dyed-Lifted Polyps and dyed resurrection margins. We developed a Convolutional Neural Network algorithm to distinguish between the anatomical landmarks to reach pathological findings. We also experimented with three transfer learning models to compare the final results, and ended up with 85 percent accuracy primarily with research optimization of the AlexNet type model. This indicates a significant impact of our artificial intelligence technology on making tremendous strides to be a tool that doctors may use in the near future, so as to improve the overall health of humanity.