Chronic obstructive pulmonary disease (COPD) stands as a global health crisis, responsible for substantial morbidity and mortality on a worldwide scale. Its insidious nature underscores the importance of early detection and accurate diagnosis. While spirometry has been the cornerstone for COPD diagnosis, the role of computed tomography (CT) imaging has evolved, offering a valuable avenue for early detection and subtype classification. Recently, the advent of artificial intelligence (AI) has brought forth the potential to revolutionize the accuracy and efficiency of COPD diagnosis, with a specific focus on CT images. This intersection of healthcare and technology signifies a paradigm shift in the way we approach COPD management. The transformative capacity of AI positions it as a vital instrument for early detection and precise subtype classification of COPD. Moreover, the synergistic relationship between medical imaging and AI paves the way for more precise and efficient disease management. Therefore, in this perspective, we tend to offer a comprehensive exploration of the latest breakthroughs in the field of CT-based AI in COPD diagnosis, aiming to demonstrate the promise and potential of AI in refining the accuracy of COPD classification and to illuminate the evolving landscape of AI’s impact on COPD management.
The eye serves as a unique window into systemic health, offering clinicians a valuable opportunity for early detection and targeted treatment. Against this backdrop, advancements in artificial intelligence (AI) and ophthalmic imaging are converging to pave the way for more precise and predictive diagnostics. This review aims to elucidate the transformative role of AI in utilizing ophthalmic imaging for the detection and prediction of systemic diseases. We begin by introducing the advantages of the eye as a valuable tool for detecting systemic diseases. We also provide an overview of various ophthalmic imaging techniques that have proven useful in predicting systemic ailments. Then, we summarize two research patterns for analyzing ocular data, followed by the introduction of current AI applications using ophthalmic images that significantly increase diagnostic precision. Despite the promise, challenges such as data heterogeneity and model interpretability persist, which are also covered in this review. We conclude by discussing future directions and the immense potential these AI-enabled approaches hold for revolutionizing healthcare. As AI technologies advance, their potential integration with ophthalmic imaging offers promising avenues for improving the diagnosis, prediction, and management of various systemic diseases, thereby contributing to the evolving landscape of integrated healthcare.
Diabetic nephropathy (DN) represents a prevalent chronic microvascular complication of diabetes mellitus (DM) and is a major cause of end-stage renal disease. The anfractuous surrounding of DN pathogenesis and the intricate nature of this metabolic disorder often pose challenges in both the diagnosis and treatment of DN compared to other kidney diseases. Hyperglycaemia in DM predispose vulnerable renal cells into microenvironmental disequilibrium and thereby results in innate immunocytes infiltration including neutrophils, macrophages, myeloid-derived suppressor cells, dendritic cells, and so forth. These immune cells play dual roles in kidney injury and closely correlated with the degree of proteinuria in DN patients. Additionally, innate immune signaling cascades, initiated by altered metabolic and hemodynamic in diabetic context, are crucial in instigating and perpetuating renal inflammation, which detrimentally contribute to DN pathogenesis. As such, antiinflammatory therapies, particularly those targeting innate immunity, hold renoprotective promise in DN. In this article, we reviewed the origin and feature of the above four prominent kidney innate immune cells, analyze their pathogenic role in DN, and discuss potential targeted-therapeutic strategies, aiming to enhance the current understanding of renal innate immunity and hence help to discover promising therapeutic approaches for DN.
Postoperative acute kidney injury requiring dialysis (PO-AKID) is a serious adverse event that not only affects acute morbidity and mortality, but also long-term prognosis. Here, we developed a practical and explainable web-based calculator (PO-AKID-teller) to detect patients who might experience PO-AKID after acute type A aortic dissection (ATAAD) surgery. This retrospective study reviewed 549 patients undergoing ATAAD surgery from October 2016 to June 2021. PO-AKID frequency was 19.7% (108 of 549 patients). The initial dataset was split into an 80% training cohort (n = 439) and a 20% test cohort (n = 110). There were seven predictors that could indicate PO-AKID, including prior cardiovascular surgery, platelet, serum creatinine, the terminal site of dissection involvement, right coronary artery involvement, estimated blood loss, and urine output. Among six machine learning classifiers, the random forest model exhibited the best predictive performance, with an area under the curve of 0.863 in the training cohort and 0.763 in the test cohort. This model was translated into a web-based risk calculator PO-AKID-teller to estimate an individual’s probability of PO-AKID. The PO-AKID-teller can accurately estimate an individual’s risk for PO-AKID in an interpretable manner, which may aid in informed decision-making, patient counseling, perioperative optimization, and longer-term care provision.