Health Informatics: Engaging Modern Healthcare Units: A Brief Overview

Clinical Applications and Future Directions of Smartphone Fundus Imaging

The advent of smartphone fundus imaging technology has marked a significant evolution in the field of ophthalmology, offering a novel approach to the diagnosis and management of retinopathy. This review provides an overview of smartphone fundus imaging, including clinical applications, advantages, limitations, clinical applications, and future directions. The traditional fundus imaging techniques are limited by their cost, portability, and accessibility, particularly in resource-limited settings. Smartphone fundus imaging emerges as a cost-effective, portable, and accessible alternative. This technology facilitates the early detection and monitoring of various retinal pathologies, including diabetic retinopathy, age-related macular degeneration, and retinal vascular disorders, thereby democratizing access to essential diagnostic services. Despite its advantages, smartphone fundus imaging faces challenges in image quality, standardization, regulatory considerations, and medicolegal issues. By addressing these limitations, this review highlights the areas for future research and development to fully harness the potential of smartphone fundus imaging in enhancing patient care and visual outcomes. The integration of this technology into telemedicine is also discussed, underscoring its role in facilitating remote patient care and collaborative care among physicians. Through this review, we aim to contribute to the understanding and advancement of smartphone fundus imaging as a valuable tool in ophthalmic practice, paving the way for its broader adoption and integration into medical diagnostics.

Leveraging Data and Technology to Enhance Interdisciplinary Collaboration and Health Outcomes

OBJECTIVE

To give an overview of recent research and propose a selection of best papers published in 2022 in Informatics for One Health.

METHODS

An extensive search using PubMed and Web of Science was conducted to identify peer-reviewed articles published between December 2021 and December 2022, in order to find relevant publications in the 'Informatics for One Health' field. The selection process comprised three steps: (i) eight candidate best papers were first selected by the two section editors; (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper; and (iii) the editorial committee of the Yearbook conducted the final best paper selection.

RESULTS

The candidate best papers represent studies that characterized significant challenges facing Informatics for One Health. Other trends of interest related to the deployment of medical artificial intelligence tools and the implementation of the FAIR principles within the One Health broad scenario. In general, papers identified in the search fell into one of the following categories: 1) Health improvement via digital technology; 2) Climate change/Environment/Biodiversity; and 3) Maturity of healthcare services.

CONCLUSION

The topic turns extremely important in the next future for what concerns the need to understand complex interactions in order to safeguard the health of populations and ecosystems.

Evaluation of nutritional status and clinical depression classification using an explainable machine learning method

Introduction

Depression is a prevalent disorder worldwide, with potentially severe implications. It contributes significantly to an increased risk of diseases associated with multiple risk factors. Early accurate diagnosis of depressive symptoms is a critical first step toward management, intervention, and prevention. Various nutritional and dietary compounds have been suggested to be involved in the onset, maintenance, and severity of depressive disorders. Despite the challenges to better understanding the association between nutritional risk factors and the occurrence of depression, assessing the interplay of these markers through supervised machine learning remains to be fully explored.

Methods

This study aimed to determine the ability of machine learning-based decision support methods to identify the presence of depression using publicly available health data from the Korean National Health and Nutrition Examination Survey. Two exploration techniques, namely, uniform manifold approximation and projection and Pearson correlation, were performed for explanatory analysis among datasets. A grid search optimization with cross-validation was performed to fine-tune the models for classifying depression with the highest accuracy. Several performance measures, including accuracy, precision, recall, F1 score, confusion matrix, areas under the precision-recall and receiver operating characteristic curves, and calibration plot, were used to compare classifier performances. We further investigated the importance of the features provided: visualized interpretation using ELI5, partial dependence plots, and local interpretable using model-agnostic explanations and Shapley additive explanation for the prediction at both the population and individual levels.

Results

The best model achieved an accuracy of 86.18% for XGBoost and an area under the curve of 84.96% for the random forest model in original dataset and the XGBoost algorithm with an accuracy of 86.02% and an area under the curve of 85.34% in the quantile-based dataset. The explainable results revealed a complementary observation of the relative changes in feature values, and, thus, the importance of emergent depression risks could be identified.

Discussion

The strength of our approach is the large sample size used for training with a fine-tuned model. The machine learning-based analysis showed that the hyper-tuned model has empirically higher accuracy in classifying patients with depressive disorder, as evidenced by the set of interpretable experiments, and can be an effective solution for disease control.