Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data

Synthetic data generation in healthcare: A scoping review of reviews on domains, motivations, and future applications

BACKGROUND

The development of Artificial Intelligence in the healthcare sector is generating a great impact. However, one of the primary challenges for the implementation of this technology is the access to high-quality data due to issues in data collection and regulatory constraints, for which synthetic data is an emerging alternative. While previous research has reviewed synthetic data generation techniques, there is limited focus on their applications and the motivations driving their synthesis. A comprehensive review is needed to expand the potential of synthetic data into less explored healthcare areas.

OBJECTIVE

This review aims to identify the healthcare domains where synthetic data are currently generated, the motivations behind their creation, their future uses, limitations, and types of data.

MATERIALS AND METHODS

Following the PRISMA-ScR framework, this review analysed literature from the last 10 years within PubMed, Scopus, and Web of Science. Reviews containing information on synthetic data generation in healthcare were screened and analysed. Key healthcare domains, motivations, future uses, and gaps in the literature were identified through a structured data extraction process.

RESULTS

Of the 346 reviews identified, 42 were included for data extraction. Thirteen main domains were identified, with Oncology, Neurology, and Cardiology being the most frequently mentioned. Five primary motivations for synthetic data generation and three major categories of future applications were highlighted. Additionally, unstructured data, particularly images, were found to be the predominant type of synthetic data generated.

DISCUSSION AND CONCLUSION

Synthetic data are currently being generated across diverse healthcare domains, showcasing their adaptability and potential. Despite their early stage, synthetic data technologies hold significant promise for future applications. Expanding their use into new domains and less common data types (e.g., video and text) could further enhance their impact. Future work should focus on developing evaluation benchmarks and standardized generative models tailored to specific healthcare domains.

High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury

Recovery from acute kidney injury can vary widely in patients and in animal models. Immunofluorescence staining can provide spatial information about heterogeneous injury responses, but often only a fraction of stained tissue is analyzed. Deep learning can expand analysis to larger areas and sample numbers by substituting for time-intensive manual or semi-automated quantification techniques. Here we report one approach to leverage deep learning tools to quantify heterogenous responses to kidney injury that can be deployed without specialized equipment or programming expertise. We first demonstrated that deep learning models generated from small training sets accurately identified a range of stains and structures with performance similar to that of trained human observers. We then showed this approach accurately tracks the evolution of folic acid induced kidney injury in mice and highlights spatially clustered tubules that fail to repair. We then demonstrated that this approach captures the variation in recovery across a robust sample of kidneys after ischemic injury. Finally, we showed markers of failed repair after ischemic injury were correlated both spatially within and between animals and that failed repair was inversely correlated with peritubular capillary density. Combined, we demonstrate the utility and versatility of our approach to capture spatially heterogenous responses to kidney injury.

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.